National Drought Monitoring System for Drought Early ...

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Final Report to NASA Decisions/05-2-0000-0167 and 0119 March 2011

National Drought Monitoring System for Drought Early Warning Using Hydrologic and Ecologic Observations from NASA Satellite Data Research Team Jet Propulsion Laboratory: S. V. Nghiem (PI), E. G. Njoku, G. Neumann, and S. K. Chan U.S. Geological Survey: J. Verdin (co-PI), J. Brown, and Y. Gu National Drought Mitigation Center: D. A. Wilhite (Co-I, Institution Lead), M. J. Hayes, M. D. Svoboda, B. D. Wardlow, and T. Tadesse NOAA Physical Science Division: R. Dole (Co-I, Institution Lead), B. Liebmann, D. Allured Dartmouth College: G. R. Brakenridge (Co-I, now at CSDMS, INSTAAR, University of Colorado) NOAA Climate Prediction Center: D. LeComte (Collaborator), M. Rosencrans

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Section 1 (Led by Jet Propulsion Laboratory)

Microwave Remote Sensing of Soil Moisture

S. V. Nghiem1, D. Allured2, M. D. Svoboda3, B. D. Wardlow3, D. LeComte4, M. Rosencrans4, S. K. Chan1, and G. Neumann1 1

Jet Propulsion Laboratory, MS 300-227 California Institute of Technology

4800 Oak Grove Drive, Pasadena, CA 91109, USA 2

Earth System Research Laboratory, Physical Science Division National Oceanic and Atmospheric Administration 325 Broadway, Boulder, CO 80305, USA 3

National Drought Mitigation Center University of Nebraska-Lincoln

3310 Holdrege Street, Lincoln, NE 68583, USA 4

Climate Prediction Center, Room 604

National Oceanic and Atmospheric Administration 5200 Auth Road, Camp Springs, MD 20746, USA

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Abstract

This chapter focuses on soil moisture measurements using wide-swath microwave sensors including satellite passive radiometer and active scatterometer. First, a review is presented on the background research and state-of-the-art methods for remote sensing of soil moisture. Emission and scattering physics is then presented to explain the underlying science of the different approaches of passive and active estimates of soil moisture. Results are obtained at local, regional, and continental scales to show patterns of soil moisture variations in daily, weekly, and seasonal time scales. Practical applications to assessments of drought conditions are illustrated in specific examples in the United States and other countries. For these applications, soil moisture change is an important parameter to address problems of anomalous propagation in radar meteorology and to identify issues related to dry rain or virga, which is relevant to hydrological drought monitoring and forecasting.

1. Introduction

Soil moisture is a fundamental link between global water and carbon cycles, and has major applications in predicting natural hazards such as floods and droughts [National Research Council, 2007]. Precipitation data can be used to estimate soil wetness. In the United States (US), preliminary precipitation data are based on measurements gathered from many active stations nationwide each month, and it takes three to four months to assemble final, qualitycontrolled data. In western US, some climate divisions may have no stations reporting in a particular month or may lack a first- or second-order station altogether. A first-order station

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reports all climate variables three times per day, and a second-order station reports maximum and minimum temperatures and rainfall once per day and other variables twice per day. For direct soil moisture measurements, a limited number of observations are provided from the Oklahoma Mesonet System [Illston et al., 2004] and the Soil Climate Analysis Network [USDA, 2009a], but the data are generally too sparse spatially with different data quality and accuracy for their assimilation to be of much use. Also, there are soil-moisture observations from enhanced additions in the SNOTEL network [USDA, 2009b], but fully calibrated data are not yet available routinely. Given the very limited number of stations collecting point-based, in-situ data, the use of such information may not be representative of regional water or soil moisture amounts and variations. Soil moisture measurements over large spatial extent (areal data rather than point data) with little or no missing gaps are crucial to represent land surface water distribution from regional to continental scales. Recognizing the importance of soil moisture, especially as a key variable for drought monitoring, the void in adequate soil moisture information in the U.S. and elsewhere demands immediate and extensive measurements from satellite microwave remote sensing of soil moisture with both passive and active sensors. A radiometer is used to measure the natural emission from land surface in passive remote sensing, while a radar, including synthetic aperture radar (SAR) and scatterometer, is used to transmit signals to a targeted surface area and measure the scattering return in active remote sensing. From passive or active microwave data, soil moisture can be estimated with various algorithms. Passive methods use data from microwave radiometers such as the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the Advanced Microwave

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Scanning Radiometer on the Earth Observing System (AMSR-E), and the Soil Moisture and Ocean Salinity sensor (SMOS) [Wang, 1985; Owe et al.,1988; Kerr and Njoku, 1990; Teng et al., 1993; van de Griend and Owe, 1994; Engman, 1995; Jackson, 1997; Kerr et al., 2001; Njoku et al., 2003]. For active remote sensing, many approaches have been used for various datasets from SARs including Seasat, Spaceborne Imaging Radar-C (SIR-C), European Remote Sensing (ERS), RADARSAT, Environmental Satellite (Envisat), and Advanced Land Observing Satellite (ALOS) [Blanchard and Chang, 1983; Dubois et al., 1995; Cognard et al., 1995; Shrivastava et al., 2009; Loew et al., 2006; Takada et al., 2009], and from scatterometers such as ERS and QuikSCAT [Wagner et al., 1999; Wagner and Scipal, 2000; Nghiem et al., 2000]. In this chapter, we review the science principle of active and passive remote sensing of large-scale soil moisture and illustrate the results from AMSR-E and QuikSCAT for drought applications.

2. Remote Sensing Science

The principle of microwave remote sensing of soil moisture is based on the sensitivity of soil permittivity to the amount of liquid water. The permittivity of a medium, like moist soil, characterizes electromagnetic wave propagation and attenuation in the medium. Both brightness temperature (measured by a radiometer) and backscatter (measured by a radar) are dependent on the soil permittivity. Empirical models of dielectric constant, which is the permittivity relative to that of the free space, for different soil types as a function of volumetric moisture content (mv) at microwave frequencies between 1.4 and 18 GHz was developed [Hallikainen et al., 1985; Dobson et al., 1985].

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While soil dielectric constant can be measured in-situ with a probe [Jackson, 1990], satellite remote sensors do not directly provide soil dielectric measurements. Instead, these sensors acquire brightness temperature or backscatter signatures, which are dependent on soil dielectric and thus on soil moisture. Such a relationship enables the inversion of soil moisture from brightness temperature or backscatter data, which can be complicated by natural effects such as vegetation cover, surface roughness, rainfall, and anthropogenic effects such as radio-frequency interference (RFI), which have different impacts on the accuracy of soil moisture retrieval at different microwave frequencies.

2.1. Passive Remote Sensing The retrieval of soil moisture from brightness temperature, measured by a satellite passive radiometer, was long formulated by many researchers and has been summarized by Njoku et al. [2003].

Here is a brief review for an isothermal vegetated soil surface, whose brightness

temperature Tbp at the physical temperature Ts is expressed as Tbp = Ts { esp exp(–τc) + (1 – ωp) [ 1 - exp(–τc) ] [ 1 + rsp exp(–τc) ] }

(1)

where the soil emissivity is esp = 1 – rsp for soil reflectivity rsp, which is influenced by soil moisture through the effect of moisture on the soil dielectric constant. The emitting depth is controlled by the near-surface moisture profile, and is smaller for higher microwave frequencies and for wetter soils. Although microwaves can only sense soil moisture in the top surface layer, there is a correlation to soil moisture in deeper soil at night when the soil moisture and temperature profiles are more uniform than in the early afternoon. In (1), τc and ωp are the vegetation opacity and the vegetation single scattering albedo, respectively. Multiple scattering

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in the vegetation layer is neglected, and a quasi-specular soil surface and no reflection at the airvegetation boundary are assumed in (1). Vegetation opacity and multiple scattering have lesser effects at lower microwave frequencies. For surface roughness, based on a fixed viewing angle, an empirical formulation has been found practically useful for relating the reflectivity of a rough soil surface, rsp, to that of the equivalent smooth surface, rop [Wang and Choudhury, 1981; Wang, 1983], which is expressed as: rsp = [ (1–Q) rop + Q roq ] exp(–h)

(2)

where p and q represent either of the orthogonal polarization states (V or H), and Q and h are roughness parameters. Q may be approximated as zero at low frequencies (e.g., L and C bands). The simple separation of soil moisture and roughness effects through (2) is not precise, and the parameter h has a residual moisture dependence [Li et al., 2000; Wigneron et al., 2001]. To normalize the surface temperature (Ts) dependence in (1), the polarization ratio (PR) is obtained by: PR = ( Tbv - Tbh ) / ( Tbv + Tbh )

(3)

which is suitable for multi-channel data taken at the same incidence angle [Kerr and Njoku, 1990]. At large incidence angles (e.g., >50o), the difference between the vertically and horizontally polarized brightness temperatures for bare soils is large, giving rise to a large PR signal. Nevertheless, the observation path length through the vegetation becomes longer at large incidence angles, increasing the vegetation attenuation and thus decreasing the sensitivity to the soil moisture.

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While (1-3) form a general theoretical basis for soil moisture retrieval from passive microwave data, different approaches have been used for different satellite datasets with different correction methods for the effects of soil types, roughness, vegetation, and surface temperature [Njoku et al, 2003] although further advances should be considered for various nonisothermal conditions and multiple interactions between soil surface and vegetation cover at different growth stages.

In practice for data from the Advanced Microwave Scanning

Radiometer on the EOS Aqua satellite (AMSR-E), the soil moisture retrieval utilizes primarily the frequency channels of 10.7 and 18.7 GHz to consider the effects of atmospheric and vegetative attenuation and to minimize the requirement for ancillary data inputs. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) has 10.7 and 19.3-GHz channels, which can be used to obtain PR for soil moisture applications with a better consistency at the lower frequency [Njoku et al, 2003]. Further details of the retrieval were published [Njoku and Li, 1999; Njoku et al, 2003; Njoku, 2004].

2.2. Active Remote Sensing In active remote sensing, soil moisture can be derived from backscatter measured by a radar such as a SAR at a high spatial resolution with a small and infrequent coverage, and by a scatterometer at a low spatial resolution with a large and frequent coverage. Many theoretical models (references given in the introduction) have been developed to characterize backscatter signatures of vegetated soil. Here, a scattering model, based on the analytic vector wave theory [Nghiem et al., 1993a], and a practical formulation are reviewed. Backscatter σ0 from moist soil with a vegetation cover is determined from an ensemble average of the correlation of scattered field components E as follows:

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(4) where subscript 0 represents the air space above the vegetation, and subscript 1 indicates the vegetation cover occupying volume V1 over the soil surface. The dyadic Green's function G and the mean field F are obtained as described by Nghiem et al. [1990]. The correlation function C characterizes the vegetation scatterers having different size, shape, and orientation angle ψf in elevation and ϕf in azimuth. For the vegetation canopy, the effective permittivity is calculated

under the strong permittivity fluctuation theory, which accounts for wave attenuation including scattering and absorption loss [Nghiem et al., 1993a]. The analytic vector wave theory accounts for fully polarimetric scattering, preserves the phase information, and includes multiple reflection and transmission interactions of up-going and down-going electromagnetic waves with the soil surface, thereby conveying the soil moisture information. This is because the soil transmissivity and the soil reflectivity are controlled by the soil dielectric constant as a function of volumetric soil moisture. Rough surface scattering can be included in the contribution to the total backscatter. The small-scale roughness of the soil surface is described with a standard deviation height and a slope. When a large-scale roughness also exists, the overall roughness is accounted for by a joint probability density function for both roughness scales [Nghiem et al., 1995]. The vegetation volume scattering and the soil surface scattering are assumed to be uncorrelated due to independent statistic representations of vegetation scatterers (e.g., leaves, twigs, branches) and soil surface roughness. Then, the total backscatter is a sum of the vegetation volume backscatter

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and soil surface backscatter. In the layer scattering configuration, such as a vegetation layer over a rough soil surface, contributions from the rough surface scattering are considered with wave interactions, differential propagation delay, and wave attenuation in the vegetation layer [Nghiem et al., 1995], which can be effectively anisotropic when vegetation scatters have a preferential directional structure (e.g., planophile, plagiothile, erectophile, or extremophile orientation distribution) [Nghiem et al., 1993b]. The backscatter from rough soil surface depends strongly on the soil dielectric constant and also on transmissivity and reflectivity due to wave interaction with the soil boundary, and thus the surface scattering also contains soil moisture signature in addition to the soil moisture information in the volume scattering component. Nevertheless, a dense vegetation canopy can have a large imaginary part in its effective permittivity, which attenuates both the soil surface scattering and soil interactions in the volume scattering, and consequently masks the soil moisture signature. Specific mathematical details of the volume and surface scattering in layered media can be found in earlier publications [Nghiem et al., 1990, 1993a, 1993b, 1995]. While the above formulation provides physical insights and serves as a theoretical basis for active remote sensing of soil moisture, in practice, due to the complexities of natural environments in different climate regimes, it is not possible to set up an inversion method of soil moisture strictly based on theoretical modeling of electromagnetic scattering. A simple empirical linear equation relates backscatter σ0 to volumetric soil moisture mv as

σ0 = a⋅mv + b

(5)

where coefficients a and b are dependent on incidence angle, polarization, vegetation conditions, soil type, surface variation, and climate regime [Mo et al., 1984, Prevot et al., 1993, Shrivastava

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et al., 1997; Shoshany et al., 2000; Hutchingson, 2003]. Particularly for Ku-band backscatter data from the SeaWinds scatterometer aboard the QuikSCAT satellite (QSCAT), the bias term b in (5) contains a signature of seasonal vegetation change while changes in volumetric soil moisture mv from rainwater are detectable in backscatter variations in a time scale consistent with the initial impulse increase in wetness from the precipitation input throughout the subsequent drying process [Nghiem et al., 2005]. Thus, soil moisture change (SMC) can be directly inferred from (5) using the temporal backscatter-change method, which removes most of the background bias.

2.3. Passive and Active Blending As presented in sections 2.1 and 2.2, passive and active sensors measure different parameters: passive brightness temperature and active radar backscatter, which represent different responses to vegetated soil with different sensitivities to soil moisture and vegetation cover. In a theoretical ideal case of smooth bare soil (τc = 0), (1) dictates that the brightness temperature is directly proportional to the emissivity esp, which is determined by soil dielectric constant and is thereby most sensitive to soil moisture. As opposed to the passive signature in this idealized case, there is no backscatter since there is no vegetation (V1 = 0 in (4) without vegetation) and no rough surface; hence, the soil moisture is not measurable by a radar for bare soil without any roughness. In reality, there is some surface roughness or some vegetation cover on moist soil, which affects the sensitivity to soil moisture differently in passive data [Njoku et al., 2003] and in active data [Nghiem et al., 1993a] until the vegetation cover becomes sufficiently dense to start masking the soil effects. Therefore, information conveyed in passive

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and active signatures together may yield more comprehensive results compared to the capability of each one separately. As an illustration of the passive and active blending, correlation analysis results of observed, satellite-based remote sensing signatures versus in-situ soil moisture and vegetation measurements at a U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) site in Lonoke, Arkansas (91.867oW and 34.833oN) is presented. The vegetation cover in the Lonoke region is primarily agricultural crops including soybeans (41%), rice (21%), and wheat (11%) [Njoku et al., 2003]. TMI passive microwave data at 10.7 and 19.3 GHz were collected for all points with their centers located within 25 km of the Lonoke SCAN site location. More than a year of time series data in 19992000 were analyzed. Results show a wide range of sensitivity in the response of instantaneous PR (obtained at each local overpass time) and in that of transient SMC after rain events. The variance between measurements and linear fit values of daily PR (10.7 GHz) versus the contemporaneous daily mv becomes large at larger values of soil moisture so much that PR can be different by a factor of 3 at mv = 34%, or transient soil moisture can change between 6% to 34% for the same PR around 0.017. This is consistent with the conclusion that a number of transient soil moisture events recorded in the SCAN data are not evident in the TMI data [Njoku et al., 2003], from which the retrieved soil moisture can be inconsistent among various transient precipitation events. For seasonal trends, seasonal TMI PR (90-day running average) is well correlated with seasonal soil moisture (90-day running average) measured at a depth of 5 cm at the Lonoke SCAN site, as indicated by high values of correlation coefficient (Table 1). Plots of seasonal data (90-day running average) for contemporaneous SCAN mv and TMI PR at both frequencies reveal

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a hysteresis behavior (Figure 1). Theoretically, (1) suggests that the hysteresis is caused by attenuation effects on the passive microwave signatures under different vegetation conditions in different seasons. In Figure 1, the linear fit for all data in the whole year is used as a reference for each frequency. The polarization ratio PR is mostly above the annual linear fit in fall and summer seasons from fall equinox in 1999 to spring equinox in 2000, when vegetation starts to decrease in early fall down to a minimum in winter and then increases toward the spring equinox. In contrast, PR is mostly below the annual linear fit during spring and summer from spring equinox in 2000 to fall equinox in 2000, when vegetation increases in spring to a peak in summer then slightly decrease toward the fall equinox. In different seasons, PR can be larger for less vegetation in winter and smaller for more vegetation in summer, thereby causing the hysteresis in seasonal PR versus mv at both frequencies. Also, the vegetation attenuation effects are less severe at the lower frequency. This is evident by two distinctive characteristics in the plots at 10.7 and 19.3 GHz (Figure 1): steeper slope at 10.7 GHz, and larger hysteresis at 19.3 GHz. Note that the annual correlation coefficient ρ declines significantly from 0.936 at 10.7 GHz to 0.792 at 19.3 GHz because the hysteresis is much larger, corresponding to strong vegetation attenuation at the higher frequency. Thus far, this discussion has been theoretical since the passive microwave analysis has not included any independent data characterizing the vegetation cover, which needs to be addressed. For the active microwave analysis, time-series QSCAT data are extracted within 25 km around the same SCAN site in the same manner as done in the case for TMI data. In contrast to the passive microwave case, daily QSCAT backscatter change correlates well with contemporaneous SMC from rainwater. In fact, daily QSCAT data capture 91% of the rain events recorded at the Lonoke SCAN site in 1999-2000. To illustrate the high correlation of

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QSCAT backscatter to transient soil moisture, a regression analysis is carried out for the period of 4 October to 19 November 1999 when two major rain events occurred as observed in daily insitu soil moisture measured at the SCAN site. With the linear formulation in the inverted form of (5) such that mv = a'⋅σ0 + b' for backscatter σ0 in dB and mv in percent, a' = 8.921%/dB and b' = 111.11% with a high correlation coefficient of 0.907 and a small standard deviation of 3.7% for the case of backscatter at the horizontal polarization (σ0HH), which well represents both the initial impulse of soil moisture increase from rain and the subsequent soil moisture decrease in the entailed drying process. For backscatter at the vertical polarization (σ0VV), the result is similar with SMC of 8.369% for a dB change in σ0VV, and thus the backscatter at the vertical polarization is slightly less sensitive than that at the horizontal polarization to transient soil moisture. This is consistent with (4) in which the dyadic Green’s function includes soil reflection which is stronger at the horizontal polarization compared to that at the vertical polarization due to the Brewster effects. Also, the incidence angle at 54o for σ0VV is larger than 46o for σ0HH, which means that σ0VV suffers from higher attenuation effects due to the longer path length in the vegetation cover. Nevertheless, QSCAT data can identify rain events even in peak vegetation conditions when the rain is sufficiently heavy for the backscatter to increase above the seasonal level of the background backscatter, explaining the high percentage of success in the QSCAT capability for identification of transient SMC. This capability to capture transient rain events illustrates the complimentary information that active microwave remote sensing provides in combination with the seasonal SMC that can be estimated from passive microwave remote sensing to provide a better representation of soil moisture conditions when both types of satellitebased microwave data are combined or blended.

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Regarding seasonal trends, active backscatter data primarily contain vegetation information. To verify this, normalized difference vegetation index (NDVI) data representing seasonal vegetation change [Justice et al. 1985; Verdin et al., 1999; Zhang et al., 2010] are used to compare with seasonal QSCAT backscatter data (90 running average). NDVI is defined by NDVI = (NIR−VIS) / (NIR+VIS), where NIR is the near infrared band and VIS is the visible band from a spectral sensor such as the Advanced Very High Resolution Radiometer (AVHRR) aboard the National Oceanic and Atmospheric Administration (NOAA) operational polarorbiting satellites. AVHRR NDVI data are collected and averaged within 25 km around the SCAN site so that the spatial scale of AVHRR NDVI data become compatible with that of QSCAT data.

Results from the linear regression analysis of contemporaneous QSCAT

backscatter and AVHRR NDVI around Lonoke show a very high correlation coefficient of 0.946 for linear σ0VV and a lower correlation coefficient of 0.864 for linear σ0HH. Therefore, seasonal QSCAT backscatter can be used to characterize seasonal vegetation change regardless of cloud cover, which is transparent to QSCAT at the Ku-band frequency of 13.4 GHz. This is consistent with earlier results on the relation of Ku-band backscatter with NDVI [Moran et al., 1997], with green leaf area index [Moran et al., 1998], and with above-ground biomass [Nghiem, 2001]. Here, QSCAT data, conveying vegetation change information independent of the passive data, are used to substantiate the theoretical discussion of vegetation cover effects on passive microwave presented earlier. For this purpose, seasonal running averaged QSCAT σ0VV (more sensitive to seasonal vegetation change compared to σ0HH) and TMI PR at 10.7 GHz (more sensitive to seasonal SMC compared to 19.3 GHz data) are collocated in time and partitioned in fall-winter seasons and spring-summer seasons. The hysteresis behavior is clearly observed in the curve of σ0VV versus PR (Figure 2). In fall and winter, PR is below the annual linear fit,

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corresponding to less vegetation cover as compared to spring-summer PR above the linear fit with more vegetation cover. The less vegetation cover indicated by lower backscatter in fall and winter supports the fact that PR is above the annual linear fit in the PR-mv hysteresis (Figure 1) for less vegetation attenuation effects on PR. Similarly, the more vegetation cover characterized by higher backscatter in spring and summer confirms the stronger vegetation attenuation forcing PR to the lower section of the PR-mv hysteresis below the annual linear fit (Figure 1). In terms of timing, the vegetation peak observed in σ0VV occurs in summer after the seasonal soil moisture reaches the maximum seen in PR in spring.

Thus, independent information on seasonal

vegetation change in active backscatter data is complementary to the information on seasonal SMC in passive polarization ratio data. The active and passive combination can thereby provide different information that will be missed if only one type of data is used separately.

3. Drought Applications 3.1. Drought Monitoring Issues In U.S., two major factors have been identified as important impediments to early detection and monitoring of drought and its impacts at the county level where key drought-related decisions are made: the lack of accurate and objective data source and the coarse level of spatial detail in drought analyses and results [Nghiem et al., 2010]. These issues are even more severe in many other countries across the world where there is a significant lack of both data and drought monitoring systems. The Lincoln Declaration on Drought Indices in 2009 declared ‘a consensus that the Standardized Precipitation Index (SPI) be used to characterize the meteorological droughts

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around the world’ [World Meteorological Organization, 2009]. The U.S. Drought Monitor (USDM) [Svoboda et al., 2001, 2002] uses SPI, which is based on preliminary precipitation data obtained from surface observations from the NOAA Climate Prediction Center (CPC) and National Climatic Data Center (NCDC). The use of rain gauge data from stations (point measurements) is a ‘hit-or-miss’ approach and may not be representative of regional rainfall amounts, and rain gauge data can also be in considerable error [Story, 2009]. Rain radars may provide better coverage for rain rate estimations; however, surface radars suffer from anomalous propagation (AP) problems [Smith et al., 1996] resulting in inaccurate representations of rainwater. Satellite precipitation estimates can provide nearly global coverage [Kummerow et al., 1996; Sorooshian et al., 2000] without AP problems in surface radar meteorology; however, the temporal and spatial resolutions are coarse. A more fundamental issue is that, while precipitation data are useful for assessing meteorological drought, such data may not represent rainwater that actually reaches to land surface and accumulates in soil, especially in drought-prone regions where virga (dry rains, dry thunderstorms, or dry lightning) are prevalent. The United Nations’ World Meteorological Organization stressed the need for undertaking a comprehensive review to develop common indices for better early drought warnings in the agricultural and water sectors [World Meteorological Organization, 2009]. For hydrological and agricultural drought assessment and monitoring, water on land surface and in soil is the most relevant, and thus soil moisture data must have a key role. Nevertheless, the current in-situ station network is inadequate, and soil moisture measurements are too sparse for effective use or even non-existent in many areas [NIDIS, 2007].

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For county-level monitoring, an important goal of the National Integrated Drought Information System (NIDIS) [Western Governors’ Association, 2004; NIDIS 2006, 2007], the National Weather Service (NWS) has determined that an effective Cooperative Observer Network would require a minimum spatial density of one observing site per 1000 km2 across the country, or a separation of about 24 to 32 km [NIDIS 2007]. The location of each in-situ sensor must be carefully selected such that the measured soil moisture is representative of the surrounding area. Furthermore, consistency and persistency in data collections are important in terms of data quality and data availability across different agencies and across different nations.

3.2. Uses of Satellite Data In view of the above issues in drought monitoring, recent efforts have enabled certain use of soil moisture measurements derived from satellite remote sensing data for enhancing drought monitoring systems [Nghiem et al., 2010]. Several specific results are presented in this section to illustrate various uses of satellite data with different temporal and spatial scales.

3.2.1. Temporal Data at Local Scale Regarding the issue of the lack of accurate and objective data from limited in-situ station measurements, we use temporal QSCAT observations together with in-situ station measurements in the local station area to illustrate how satellite data can help to enhance the drought monitoring capabilities. Figure 3 presents results at the NCDC Global Summary Of the Day (GSOD) Station 727760 in Great Falls, Montana (47.467oN, 111.383oW). The NCDC GSOD station dataset consists of meteorological data measured by weather stations in the World Meteorological Organization and Global Telecommunications System network, including precipitation, temperature, humidity, dew point, pressure, and other meteorological data

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measured by a global network of weather stations [NCDC, 2010a]. Time series of QSCAT data together with in-situ measurements around this station are constructed with the Special SatelliteStation Processor (SSSP) [Nghiem et al., 2003]. SSSP uses an innovative algorithm based on pointers in computer coding, which enables rapid satellite data extraction and collocation with in-situ data over numerous global stations represented by a global station mask allowing satellite data selection to any radius within 60 km around each station. In Figure 3, daily QSCAT data whose centroids within 25 km around Station 727760 are selected for the horizontal polarization (more sensitive to soil moisture compare to the vertical polarization) from ascending orbits (~6 am local overpass, and more correlated with soil moisture compared to data from descending orbits). QSCAT σ0HH data around Great Falls (top panel in Figure 3) clearly identify rain events before and after the dry period between 9 July and 5 September 2000 when there was very little rain. In August 2000, the long-term Palmer Drought Index for the region around Great Falls was −4 or below, indicating long-term extreme drought conditions.

The U.S. Department of

Agriculture (USDA) issued Natural Disaster Determinations for drought for the entire state of Montana in 2000, when severe and persistent drought caused significant losses to agriculture and other sectors [Resource Management Service, 2004]. The summer drought period observed by QSCAT is validated by the lack of rain in in-situ precipitation data (bottom panel in Figure 3) from Station 727760 in the same period, when there were several heat waves indicated by spells of high temperatures (bottom panel in Figure 3). Both before and after the drought period in the summer of 2000, QSCAT detected a number of significant rain events which increased backscatter by about 3 dB, which is equivalent to 26.8% in volumetric soil moisture increase per the Lonoke rating value of a' = 8.921%/dB. Thus, rainwater from these rain events reached the

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land surface and significantly increased the moisture in soil. However, precipitation data from the station rain gauge corresponding to these significant rain events disparately ranged across one order of magnitude from low values (< 0.2 cm) to high values (> 2.0 cm). In contrast to SMC, which has a consistent relationship with backscatter change (section 2.3), the inconsistency in in-situ precipitation data measured by a rain gauge at this station demonstrates accuracy issues causing difficulties in assessing drought conditions in an objective manner, especially for hydrological droughts. The inconsistency of precipitation data compared to SMC exists not only in satellite measurements of SMC but also in in-situ data of SMC from SCAN, which also has rain gauges to measure local precipitation.

This problem can be

compounded by the measurement location where data may be affected by localized effects (e.g., wind effects, shading effects, equipment maintenance) and by the point-based nature of rain gauge measurement itself where a local rainfall event may not be recorded if the rain does not fall over the exact station location.

These issues can be addressed with the satellite data to

enhance the drought monitoring in an objective and consistent manner. Moreover, similar time series of QSCAT signature can be obtained at all grid cells for a full and continuous spatial coverage while station data are only available at local station points, which may or may not be representative of the surrounding area.

3.2.2. Spatial Data at Regional Scale Satellite microwave remote sensing data, such as AMSR-E or QSCAT, can be used to monitor drought and water resources at regional to global scales. Both have swath widths of 1400 km or larger [Njoku et al., 2003; Tsai et al., 2000], which allows a nearly daily coverage across the world and as many as two times per day at high latitudes. Several attributes related to water can

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be obtained from microwave satellite data for drought monitoring from local areas to large scales. Results are presented here to illustrate the use of satellite data for Texas (TX), an important agricultural state. Pertaining to the regional climatic regime, drought is inevitable in TX, where water demand will significantly increase and drought will become more critical [Texas Water Development Board, 2007]. A relevant attribute for water resource and drought assessments is precipitation frequency, which quantifies the recurrence of rain events in a given period [González and Valdés, 2004]. Instead of an apparent precipitation frequency (APF) derived from in-situ rain gauge data or surface rain radar data, a different measure of precipitation frequency is derived from satellite scatterometer data. This measure is defined as effective precipitation frequency (EPF) because it accounts for rainwater that effectively reaches land surface and increases soil moisture as opposed to APF that may have problems with AP, virga, or inconsistent point data.

For

applications to QSCAT data, EPF = 100 (NW / NC) is defined as the percentage of the number of wet days (NW), when soil moisture increase in the top soil layer (5 cm) is ≥5 % such that the corresponding backscatter increase is ≥0.56 dB for σ0HH or ≥0.60 dB for σ0VV above the background level, over the total number of satellite coverage days (NC) without counting missing data days in a given period. EPF is retrieved from QSCAT data across the state of TX over the period of 1 June to 31 August 2009 (left panel in Figure 4). In summer 2009, TX suffered exceptional drought over most of the southern region of the state as observed in the USDM maps from June to August 2009 (right panels in Figure 4). By August 2009, extreme and exceptional drought conditions (D3 and D4, respectively) remained persistent across south central TX, where the topsoil conditions were very dry and river levels were near historic lows [NCDC, 2009]. Consistent

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with these drought conditions, QSCAT EPF showed little to no rain event across most of southern TX (black to magenta areas, left panel of Figure 4). In contrast, the soil in a part of the TX Panhandle was shown to be wetted by rainwater the most often during this time period (light blue to green and yellow areas, left panel of Figure 4), where abnormally dry (D0) depicted in the June USDM map had improved to no drought by late August 2009. While EPF carries information on wet precipitation frequency or how often land surface becomes wet due to rainwater, daily SMC from QSCAT data represents the quantitative change in soil moisture or the amount (intensity) of rainwater that accumulates on land surface each day. Therefore, SMC is an attribute relevant to monitoring hydrological drought because it is related to water on land rather than rain drops in the atmosphere (a meteorological parameter). Figure 5 presents maps of daily SMC compared to the semi-monthly average over the state of TX from early September to early October 2009. Intense SMC (yellow areas in maps), which reflect large increases in soil moisture conditions occurred across large areas of central TX on 10, 11, 13, and 14 September, as well as 4 October. The SMC results on these days are consistent with torrential rainfall events reported across central and south TX (rainfall totals up to 20 inches were recorded in some locations)in September 2009, causing flash flooding [NWS, 2009]. With this new water input, drought conditions in central and south TX had significantly improved by early October 2009 (as shown in the USDM map for October 6 in Figure 6). Complementary to the transient change observed in the QSCAT daily SMC, AMSR-E passive microwave data provide good measurements of seasonal soil moisture (as discussed in section 2.3). Figure 6 (left panel) shows the difference in seasonal soil moisture between the September-October and June-July periods in 2009. AMSR-E seasonal soil moisture results reveal a large region of increased soil moisture in south and south central TX (blue areas). This

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corresponds to the marked improvement in drought conditions in September compared to that in July 2009 as seen on the USDM drought maps (right panels in Figure 6). In contrast, an area in western TX has a substantial reduction in soil moisture by the September-October period (redbrown areas in left panel of Figure 6) compared to more moist conditions in the June-July period. This area had a larger EPF observed by QSCAT in the earlier months as seen in the left panel of Figure 4 for June-August 2009. Therefore, the independent attributes derived from different remote sensing datasets (QSCAT and AMSR-E) are consistent and thereby cross-verifying the results. Although the USDM map (lower right panel in Figure 6) suggests a slight improvement by 6 October 2009, this improved condition was a result of recent transient wetting events observed in daily SMC from QSCAT (e.g., SMC map for 4 October 2009 in Figure 5). The above results demonstrate the capability and consistency of different parameters to depict the state of soil moisture and its transient as well as seasonal changes, which are relevant to drought monitoring at the regional scale.

3.2.3. Spatial Data at Continental Scale A major advantage of satellite data is its large spatial coverage across the continental scale to the global scale compared to surface in-situ measurements from station networks. Here, the pattern of soil moisture change, observed by QSCAT and AMSR-E satellites, is examined across the contiguous United States (CONUS) and compared to rainfall patterns from the regional multisensor precipitation analysis assembled into a national product (stage 4). Maps of stage-4 daily precipitation (SDP) are available from the National Mosaic and Multi-Sensor QPE [NMQ, 2009]. With large swaths of measurements, QSCAT and AMSR-E can provide coverage over CONUS on a near daily basis. Nonetheless, data gaps exist and a full coverage of the entire

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CONUS is not possible within a day, especially when ascending and descending orbit data are used separately. Figure 7 includes daily SMC maps in May 2009 from QSCAT ascending-orbit data (upper panel) at about 6 am local overpass time, and from AMSR-E descending-orbit data (lower panel) at about 1:30 am local overpass time. Both maps have data gaps, which are larger in AMSR-E data due to its narrower swath compared to that of QSCAT at the vertical polarization. There are also missing AMSR-E data in most of West Virginia due to invalid conditions (e.g. vegetation cover) for soil moisture retrieval. Overall, the patterns of daily SMC from QSCAT and AMSR-E are similar. Both reveal precipitation water on land surface in the Midwest and the Great Lakes states extending toward the northeastern U.S., whereas most of the western U.S. was dry. An extensive wet region is observed across Kansas (KS) and Nebraska (NE) in both SMC maps (marked by the circles in Figure 7). Interestingly, a well-defined dry area is detected by both QSCAT and AMSR-E just south of Lake Michigan at the Illinois-Indiana border. Nevertheless, there are discrepancies. First, the amount of SMC observed by QSCAT can be more than volumetric 10 % (yellow areas in the upper panel of Figure 7) compared to AMSR-E SMC that barely exceeds 5 % (blue areas, lower panel, Figure 7). In New York, the region east of Lake Ontario had a large positive SMC (wet) in the QSCAT map while the AMSR-E SMC showed a slightly negative value (dry). These differences, given the better sensitivity of QSCAT data to transient SMC, are not surprising as discussed earlier in section 2.3. In the case of the discrepancy between QSCAT and AMSR-E SMC in New York, it could be hypothesized that the difference was due to the different observation times of the two instruments (6 am for QSCAT and 1:30 am for AMSR-E). However, SDP maps indicate significant rainfall on 27 May continuing to 28 May 2009 in New York (Figure 8). Thus, the

25

lower sensitivity in AMSR-E data to transient SMC is likely the cause of the differences in SM results. The SDP map on 28 May 2009 (Figure 8a) also shows a large-scale overall pattern similar to the SMC observed by both satellites with band of heavier rainfall across the upper Midwest and Great Lakes region extending into the northeastern states. However, the 28-May SDP map indicates no precipitation in KS and NE where both QSCAT and AMSR-E detected rainwater on land surface resulting from the intense rainfall on the prior day (marked by circles in Figures 7 and 8). This case illustrates that SMC can represent the rainwater accumulated from preceding strong precipitation events so that the corresponding large amount of water can stay in the top soil for a period of time after the rain events. As such, SMC is also an indicator of the intensity or amount of rainwater on land surface in terms of the SMC duration. There are discrepancies between SMC and SDP. For example, in New Mexico (NM), SDP observed extensive precipitation across the state while SMC from both QSCAT and AMSR-E found wetness only in some areas of NM (such as in northeastern NM). This difference suggests either the rainwater did not fully reach land surface (virga problem) or SDP has uncertainties in surface radar data (AP problem).

Similarly, the SDP pattern was much more widespread

compared to the SMC pattern (Figures 7 and 8) in TX, where AP problems can cause significant difficulties in precipitation mapping [Story, 2009]. These observations suggest that SMC is more relevant to hydrological drought monitoring while SDP is for meteorological drought monitoring.

3.2.4. Soil Moisture Products for Drought Monitoring and Forecasting In an operational environment, science results need to be transitioned into data and image products with appropriate formats and protocols that can be used by drought experts, such as the

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USDM authors, in the process of making drought monitoring maps in conjunction with a suite of attributes from multiple data sources (e.g., vegetation conditions, stream flow, and precipitation). Here, examples of various SMC products are presented and compared with other traditional products to identify their advantages and limitations. Data from the QuikSCAT satellite are processed at the Jet Propulsion Laboratory in Pasadena, California (CA), which are then automatically and routinely uploaded to the NOAA Physical Science Division (PSD) in Boulder, Colorado (CO), where multiple SMC products are generated. For the data link, a file-transfer-protocol (ftp) is established between JPL using an automated code and PSD using a semi-automatic approach. In the data transfer process, human interventions are needed to account for anomalies in satellite data collections and operations; therefore, a fully automated system requires a full integration with the satellite operation and data processing systems in the future. The development of various SMC products for use in USDM is an iterative process where many interim products are made for different attributes of SMC in different formats with various color protocols. In this process, three SMC attributes, including daily SMC, weekly maximum SMC, and weekly mean SMC, in five different formats (gif, jpg, png, NetCDF, and kmz) using five different color protocols, result in 75 different products when all combinations are produced. Several raster sizes were also considered so that images could be displayed on a drought author’s computer terminal without a missing section in each of the images. The timing of the routine SMC product delivery is important in the operational environment so that updated results can be used in time. This timing dictates which period of SMC data should be used to compile weekly results. For USDM, the weekly SMC products are made ready by Monday afternoon to meet the

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operational timing requirement for USDM authors to have the inputs by late Monday or early Tuesday for the preparation of the drought monitor for the current week. The various SMC products have been evaluated and specific products useful for USDM have been identified. We present an example in Figure 9, which compares 8-day mean and 8-day maximum SMC products with the rain-gauge precipitation (RGP) product used in USDM for the period of 14 October 2008 and the ensuing seven days. The RGP product is made by CPC with an Oracle database that houses observations from a multitude of sources such as surface weather measurements from the Automated Surface Observing Systems (ASOS) and from co-operative observers. These data are run through several quality-control steps and through Geographic Information System (GIS) processing to obtain RGP maps. About 7000 daily reports of in-situ rain gauge data are included in the making of the RGP product [Higgins et al., 2000]. RGP maps are made with different time periods from 5 to 8 days for operational use in the USDM drought assessment.

In this particular example, we compare the full 8-day RGP product with the

corresponding 8-day SMC map. The mean and maximum SMC maps (Figures 9a and 9b), based on the color protocol with yellow-to-brown for drier conditions and green-to-blue for wetter conditions, are shown together with the corresponding USDM D-level contours from the USDM for 14 October 2008. This color protocol is selected to enhance the visibility of the different SMC levels against the colors of the D-level contours. The mean SMC map (Figure 9a) reveals a significant value of soil moisture increase (light blue area) extending from western KS toward its border with CO. In Figure 9a, noticeable increase in soil moisture (green areas) is observed around the TX Panhandle, in southeastern TX, central Oklahoma (OK), eastern New Mexico (NM), and in some areas of Montana (MT). The mean SMC map also shows significant drying across several states in the upper midwest US.

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Compared to the mean SMC, the maximum SMC map (Figure 9b) indicates much more intensive soil moisture increase over extensive regions (blue to magenta areas) since it is the peak soil moisture increase detected at any time in the 8-day period, representing the largest value of any rainwater detectable on land surface on any given day including the remnant of rainwater from previous days. In the maximum SMC map, a caveat is that low SMC values (gray and light green) are noisy and have large uncertainties. While the maximum SMC corresponds to the peak water accumulation on land surface, the mean SMC provides an assessment of the persistency of the rainwater in soil because the more the number of days when a significant amount of soil moisture increase occurs the larger the mean SMC becomes in the given time period. Therefore, it possible to have a large peak SMC due to an intensive rain event in a day in an area (e.g., blue area between Indiana and Ohio in Figure 9b) where the mean SMC is low because there is no rain water accumulation in other days during the 8-day period. Since the persistency of SMC (how long rainwater accumulates and stays in soil) depends on soil type, infiltration, and runoff processes among other factors, the mean SMC carries hydrological information and is thus relevant for hydrological drought monitoring. For benchmarking, the traditional RGP product used in USDM is included in Figure 9c to compare with the mean and maximum SMC products.

The comparison of the RGP and SMC

products in Figure 9 clearly points to the different characteristics of these measurements: RGP consists of point data localized at each separated rain gauge station, while SMC are composed of 25-km pixel with a continuous spatial coverage within each satellite swath. Here, an important note is that a goal of NIDIS is to resolve county-level drought conditions because many important drought-related decisions are made at that spatial scale. The average size of a county

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or a county-equivalent unit for CONUS is approximately 50 km in linear scale (~2,500 km2 in area). Thus, to resolve the county scale of 50 km, the Nyquist sampling theorem requires a spatial scale of 25 km, which is satisfied by the SMC data. However, RGP can have hourly data while SMC is available only two times per day at most. Although SMC temporal scale is suitable for the weekly time scale of USDM, better temporal coverage can improve the overall result, especially in the tropics where current satellite data gaps are the largest. While the spatial patterns in both RGP and maximum SMC maps (Figures 9b and 9c) agree in general over the areas of extensive precipitation in the states of NM, TX,OK, KS, NE, and Iowa (IA), there is however a large area of discrepancy between maximum SMC and RGP in MT, Wyoming (WY), and a part of North Dakota (ND). A reason for this discrepancy is SMC has a memory of any precipitation water as long as it remains on land surface at the time of the satellite measurement as opposed to the instantaneous and temporally discrete characteristics of rain gauge data at the in-situ station location. In particular for the case in Figure 9, the wetness in the maximum SMC was observed the area of discrepancy only on the first day (14 October 2008). Another difficulty is the sparse number of stations in certain regions (e.g., south TX, MT, northwest NE, etc.). Regarding the mean SMC (Figure 9a), a high value requires sufficient rain water to accumulate on land surface over a significant duration during the period under consideration. As such, the mean SMC can truly represent the significance in both quantity and persistence of new precipitation water in soil that are most relevant to hydrological drought monitoring, compared to both maximum SMC and RGP. For example, maximum SMC and RGP show some precipitation pattern in NE and IA; however, the same region appears dry in the mean SMC, indicating that the

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transient rainwater may not be sufficient to sustain the presence of soil moisture over a significant fraction of the 8-day period to have any significant impact on the overall condition. For the 2009 growing season (i.e., June to early October), Figures 10 and 11 present a comparison of QSCAT SMC and USDM results across CONUS. There is an overall consistency between the two sets of results on a regional scale. For example, there was not much water from precipitation detected on land surface (Figure 10) in the West and the Southwest, where USDM maps (Figure 11) show either no improvement (e.g., CA) or more severe drought (e.g., Arizona). For south TX, no significant wet events occurred in the first part of the growing season, which is reflected by the severe to extreme drought conditions in the USDM, while the rainfall events in August and September that lessened the drought conditions are represented by the positive SMC in September and the corresponding reduction in drought classification in the USDM in September and early October. In the Midwest, Figure 10 reveals extensive SMC in South Dakota (SD), southern Minnesota (MN), eastern NE, and western IA in June and July 2009. During this same time period, USDM results consistently indicate some improvement in SD, NE, and IA (primarily change from D0 to no drought classification), and USDM maps suggest drought levels in MN remained unimproved or became slightly worse. In the first half of June 2009, SDP results showed extensive rain pattern in the Midwest, which supports the existence of rain water on land seen in SMC (top left two panels in Figure 10). Since the mean SMC represents a persistent amount of rainwater on land surface, SMC inherently reflects information about temperature, wind, insolation, and other parameters that affect the soil wetness. Therefore, SMC may supplement information in synergy with other parameters currently used in USDM to enhance the results, especially regarding hydrological drought conditions.

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In addition, with the participation from the NIDIS team, further advances have been made in order to demonstrate SMC products in the NIDIS operational environment. For this NIDIS demonstration, a set of nearly three years of SMC products (2007-2009) are made in the Network Common Data Form (NetCDF) and delivered for transition into the environment of the NIDIS portal. The first working prototype is available for the demonstration of SMC maps together with USDM drought overlay is at http://www.drought.gov/imageserver/NASADroughtViewer/. Currently working primary features include zoom, pan (via the grab tool), layer selection (both SMC and USDM and other layers are selectable separately), transparency control for each layer (via a dialog box), locator by geographic names, etc.

Figure 12 illustrates the SMC

demonstration at the national level with USDM and AHP River Gauge layers, and at the county level in Nebraska as an example. Further work is on going to include more operational features. Soil moisture change as measured by satellite can benefit not only drought monitoring, but also benefits drought forecasting. Skillful forecasts of drought or soil moisture would have significant uses for agriculture and hydrology (water planning). Recognizing the importance of seasonal forecasts of drought, NOAA CPC has been issuing such forecasts since March 2000. These forecasts are designed to indicate whether existing droughts will persist or improve, and whether a new drought will form. An important first step in creating an improved forecast would be better knowledge of existing conditions, and the SMC is an appropriate hydrological parameter to contribute to a more accurate depiction of near-surface moisture supplies. Secondly, improved knowledge of short-term moisture trends can contribute positively to drought forecasts. Although there is no certainty that short-term trends will persist, forecasters need to know if moisture conditions are deteriorating, and how fast they are deteriorating. Such trends serve to flag situations that require additional analysis.

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4. Concluding Remarks

As presented in this chapter, both passive and active microwave remote sensing have advantages and limitations for soil moisture monitoring, which necessitates using them in combination with each other, as well as in situ observations to more fully characterize soil moisture conditions. First, in-situ measurements can be obtained many times in a day (e.g., hourly measurements) whereas a satellite sensor typically collects data one or two times per day depending on latitudes. Most in-situ stations have a longer time-series compared to satellite data. In particular, AMSR-E data have been collected since 2002 to the present and QSCAT data were obtained over a decade (1999-2009), while many rain gauge stations were established several decades ago. Regarding spatial coverage, satellite data, such as seasonal soil moisture from AMSR-E or SMC from QSCAT, have two important advantages: (1) areal data represent the condition over the pixel size, and (2) continuous coverage from the local area to regional and continental scales, whereas in-situ RGP data or soil moisture network such as SCAN consists of sparse point data separated in space with different numbers of stations and different data quality in different areas. Furthermore, there are differences in the characteristics of different attributes measured by insitu gauges and by satellite sensors as presented in the benchmark study in the previous section. Given the advantages and limitations in both surface and remote sensing measurements, the data should be combined in an optimal synergistic manner to improve the quality of both kinds of data (in-situ versus remote sensing). Here, an approach is to use satellite data to assess the representativeness of in-situ point measurement in the surrounding area. For example, soil moisture data from SCAN can be compared or correlated in time (across months, seasons, or years) to satellite soil moisture signatures collected over areas with different radii away from the

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in-situ station, to determine whether and how far the different measurements are correlated. This is valuable in the selection of station location for long-term maintenance so that the surface data are valid over the largest area as possible (not just in a close proximity of the station) in view of the county-scale goal of NIDIS. Because of the differences in the different data types in time and in space, the role of data assimilation system [Mitchell et al., 2004; Kumar et al., 2008] is important to integrate various ground measurements and satellite observations using multiple community land surface models. The modeling approach allows the various time scales and spatial coverage to be incorporated in a systematic manner. Since in-situ networks are changing and improving while new satellite data and products are being developed, land data assimilation needs to be continuously evolved to account for changes in land surface data from all data sources and to provide enhanced products that can be used to advance drought monitoring. Furthermore, new measurements can allow better cross-verifications and validations among different models used in the land data assimilation systems in an effort to produce the most accurate and high quality products. Drought is a common climatic phenomenon throughout the world and is not stopped or limited at geo-political boundaries between different nations, and therefore it is a global problem requiring international efforts for drought assessment, forecast, and mitigation. In this regard, satellite data from different nations can contribute to the overall goal. The QSCAT antenna ceased to spin in November 2009 after its continuous operation collecting global data over a decade since July 1999. Meanwhile, the Indian Space Agency successfully launched another scatterometer similar to QSCAT aboard the Oceansat-2 Satellite [Jayaraman et al., 1999] in September 2009. These satellite events, together with scatterometer data agreement signed among the different nations, highlight the importance of international collaborations in the use of

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satellite data to extend the observational record for various applications including drought monitoring. Currently, QSCAT is still measuring good backscatter data along narrow tracks at a fixed azimuth, which are valuable to assist in the calibration and validation of OSCAT.

Once

consistently calibrated with QSCAT, OSCAT can continue the QSCAT time-series in producing SMC results, which are important for drought monitoring based on both present and long-term data for a consistent assessment of drought conditions within its climatic perspective. In the near future, China will launch the Haiyan-2 (HY-2) satellite carrying another scatterometer [Dong et al., 2005]. Moreover, the development of another advanced satellite scatterometer is being studied in U.S., stemming from the recommendation of the Decadal Survey [National Research Council, 2007]. Regarding soil moisture measurements, the current European Soil Moisture and Ocean Salinity (SMOS) mission [Kerr et al., 2010] and the proposed U.S. Soil Moisture Active and Passive (SMAP) mission [Entekhabi et al., 2010] to be launched in this decade could provide global measurements critical for drought monitoring. Collectively, these successive satellite missions would be important to provide multi-decadal data to address the non-stationary issue in climate change.

Moreover, long-term data are necessary for the probabilistic

standardized index approach with multiple time scales of soil moisture variability to be used for drought monitoring. Regarding drought monitoring systems, experiences in using satellite data to enhance USDM and NIDIS are valuable for the development and improvement of international drought monitoring systems, such as the North American Drought Monitor (NADM) that is a cooperative effort between drought experts in Canada, Mexico and the United States to monitor drought across the continent on an ongoing basis [NCDC, 2010b]. In developing countries, the lack of

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in-situ or surface measurement networks further emphasizes the value of satellite data for drought monitoring because satellite products such as AMSR-E soil moisture or QSCAT SMC can be retrieved from global satellite data across international boundaries. For example, Figure 13 presents SMC patterns over the African continent [Nghiem, 2009], from which it is possible to produce multiple SMC derivatives similar to those developed from USDM (Figure 9). Such products can contribute to the global drought monitoring as a common goal, for which the Global Earth Observation System of Systems (GEOSS) [Lautenbacher, 2006] will be crucial as an overall integrator. Regarding drought forecasting, another potential contribution of satellite moisture conditions to forecasting is the use of climatology. Given the limited skill of seasonal forecasts of temperature and precipitation, drought forecasters place considerable weight on projecting current conditions forward based on what has happened in the past. In this regard, careful attention should be paid to the issue of non-stationary due to significant changes in regional climatic trends in recent years. Once the SMC products are obtained for a suitable number of years to capture contemporary changes, forecasters may gain knowledge of the probabilities that soil moisture conditions will likely improve or deteriorate during the following season. One of the goals of drought forecasting is to cast the forecasts in terms of probabilities so as to provide a more accurate portrayal of confidence levels, and the statistics of historical soil moisture conditions can contribute to this effort. In short, improved knowledge of initial moisture conditions, short-term trends, and climatology has the potential to further the skill of current and future drought forecasts for not only the United States, but globally as well.

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Acknowledgments

The research carried out at the Jet Propulsion Laboratory, California Institute of Technology, was supported by the National Aeronautics and Space Administration (NASA) Water Resources Area of the NASA Applied Sciences Program. The contribution of the NIDIS team in the SMC product demonstration in the NIDIS portal is acknowledged. Thanks to Robert Rabin from the NOAA National Severe Storms Laboratory for his direction to Stage-4 daily precipitation products for comparison with SMC patterns.

References

Blanchard, B. J., and A. T. C. Chang, Estimation of soil-moisture from SEASAT-SAR data, Water Resources Bull., 19(5), 803-810, 1983. Cognard, A. L., C. Loumagne, M. Normand, P. Olivier, C. Ottle, D. Vidalmadjar, S. Louahala, and A. Vidal, Evaluation of the ERS-1 Synthetic Aperture Radar capacity to estimate surface soil-moisture – 2-year results over the Nazin watershed, Water Resources Res., 31(4), 975982, 1995. Dobson, M. C., F. T. Ulaby, M. T. Hallikainen, and M. A. Elrayes, Microwave dielectric behavior of wet soil, Part II: Dielectric mixing models, IEEE Trans. Geosci. Remote Sens., GE-23(1), 35-46, 1985. Dong, X., K. Xu, H. Liu, and J. Jiang, The radar altimeter and scatterometer of China’s HY-2 satellite,

Proc.

Geosci.

Remote

Sens.

doi:10.1109/IGARSS.2004.1370659, 2004.

Symp.,

IGARSS,

vol.

3,

1703-1706,

37

Dubois, P., J. J. van Zyl and T. Engman, Measuring soil moisture with imaging radars, IEEE Trans. Geosci. Remote Sens., 33(4), 916-926, 1995. Engman, E. T., Recent advances in remote-sensing in hydrology, Rev. Geophys., 33, Part 2, Suppl. S, 967-975, 1995. Entekhabi, D., E. G. Njoku, P. E. O'Neill, K. H. Kellogg, W. T. Crow, W. N. Edelstein, J. K. Entin, S. D. Goodman, T. J. Jackson, J. Johnson, J. Kimball, J. R. Piepmeier, R. D. Koster, N. Martin, K. C. McDonald, M. Moghaddam, S. Moran, R. Reichle, J. C. Shi, M. W. Spencer, S. W. Thurman, L. Tsang, Leung, and J. J. Van Zyl, The Soil Moisture Active Passive (SMAP) mission, Proc. IEEE, 98(5), 704-716, 2010. González, J., and J. B. Valdés, The mean frequency of recurrence of in-time-multidimensional events for drought analyses, Natural Hazards and Earth Sys. Sci., 4(1), 17-28, 2004. Hallikainen, M. T., F. T. Ulaby, M. C. Dobson, M. A. El-Rayes, and L.-K. Wu, Microwave dielectric behavior of wet soil, Part I: Empirical models and experimental observations, IEEE Trans. Geosci. Remote Sens., GE-23(1), 25-34, 1985. Higgins, R. W., W. Shi, E. Yarosh, and R. Joyce, Improved United States precipitation quality control system and analysis, NCEP/Climate Prediction Center ATLAS No. 7, U.S. Department

of

Commerce,

National

Weather

Service,

NOAA,

available

at

http://www.cpc.noaa.gov/products/outreach/research_papers/ncep_cpc_atlas/7/, 2000. Hutchinson, J. M. S., Estimating near-surface soil moisture using active microwave satellite imagery and optical sensor inputs, Trans. ASAE, 46(2), 225-236, 2003. Illston, B. G., J. B. Basara, and K. C. Crawford, Seasonal to interannual variations of soil moisture measured in Oklahoma, Int. J. Climatol., 24, 1883-1896, 2004.

38

Jackson, T. J., Laboratory evaluation of a field-portable dielectric soil-moisture probe, IEEE Trans. Geosci. Remote Sens., 28(2), 241-245, 1990. Jackson, T. J., Soil moisture estimation using SSM/I satellite data over a grassland region, Water Resources Res., 33, 1475-1484, 1997. Jayaraman, V., V. S. Hedge, M. Rao, and H. H. Gowda, Future Earth observation missions for oceanographic applications: Indian perspectives, Acta Astronautica, 44(7-12), 667-674, 1999. Justice, C. O, J. R. G. Townshend, B. N. Holben, and C. J. Tucker, Analysis of the phenology of global vegetation using meteorological satellite data, Int. J. Remote Sens., 6(8), 1271-1318, 1985. Kerr, Y. H. and E. G. Njoku, A semiempirical model for interpreting microwave emission from semiarid land surfaces as seen from space, IEEE Trans. Geosci. Rem. Sens., 28, 384-393, 1990. Kerr, Y. H., P. Waldteufel, J.-P. Wigneron, J.-M. Martinuzzi, J. Font, and M. Berger, Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission, IEEE Trans. Geosci. Remote Sens., 39(8), 1729-1735, 2001. Kerr, Y. H., P. Waldteufel, J. P. Wigneron, S. Delwart, F. Cabot, J. Boutin, M. J. Escorihuela, J. Font, N. Reul, C. Gruhier, S. E. Juglea, M. R. Drinkwater, A. Hahne, M. Martin-Neira, and S. Mecklenburg, The SMOS mission: New tool for monitoring key elements of the global water cycle, Proc. IEEE, 98(5), 666-687, 2010. Kumar, S. V., R. H. Reichle, C. D. Peters-Lidard, R. D. Koster, X. W., Zhan, W. T. Crow, J. B. Eylander, and P. R. Houser, A land surface data assimilation framework using the land

39

information system: Description and applications, Advances in Water Resources, 31(11), 1419-1432, 2008. Kummerow, C., W. S. Olson, and L. Giglio, A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors, IEEE Trans. Geosci. Remote Sens., 34, 1213–1232, 1996. Lautenbacher, C. C., The Global Earth Observation System of Systems: Science serving society, Space Policy, 22(1), 8-11, 2006. Li, Q., L. Tsang, J. Shi, and C. H. Chan, Application of physics-based two-grid method and sparse matrix canonical grid method for numerical simulations of emissivities of soils with rough surfaces at microwave frequencies, IEEE Trans. Geosci. Rem. Sens., 38, 1635-1643, 2000. Loew, A., R. Ludwig, and W. Mauser, Derivation of surface soil moisture from ENVISAT ASAR wide swath and image mode data in agricultural areas, IEEE Trans. Geosci. Remote Sens., 44(4), 889-899, 2006. Mitchell, K. E., D. Lohmann, P. R. Houser, E. F. Wood, J. C. Schaake, A. Robock, B. A. Cosgrove, J. Sheffield, Q. Y. Duan, L. F. Luo, R. W. Higgins, R. T. Pinker, J. D. Tarpley, D. P. Lettenmaier, C. H. Marshall, J. K. Entin, M. Pan, W. Shi, V. Koren, J. Meng, B. H. Ramsay, and A. A. Bailey, The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological

modeling

system.

doi:10.1029/2003JD003823, 2004.

J.

Geophys.

Res.,

109(D7),

D07S90,

40

Mo, T., T. J. Schmugge, and T. J. Jackson, Calculations of radar backscattering coefficient of vegetation covered soils, Rem. Sens. Environ., 15, 119-133, 1984. Moran, M. S., V. Vidal, D. Troufleau, J. Qi, T. R. Clarke, P. J. Pinter, Jr., T. A. Mitchell, Y. Inoue, and C. M. U. Neale, , Combining multifrequency microwave and optical data for crop management, Remote Sens. Environ., 61, 96-109, 1997. Moran, M. S., A. Vidal, D. Troufleau, Y. Inoue, and T. A. Mitchell, Ku- and C-band SAR for discriminating agricultural crop and soil conditions, IEEE Trans. Geosci. Remote Sens., 36, 265-272, 1998. Myneni, R. B., C. D. Keeling, C. J. Tucker, G. Asrar, and R. R. Nemani, Increased plant growth in the northern high latitude from 1981 to 1991, Nature, 386(6626), 698-702, 1997. National Research Council, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. The National Academies Press, Washington, D.C., 2007. NCDC, State of the Climate – Drought, August 2009, National Climatic Data Center, NESDIS, NOAA, U.S. Department of Commerce, http://www.ncdc.noaa.gov/sotc/?report=drought& year=2009&month=8, 2009. NCDC, Global Surface Summary of the Day – GSOD, National Climatic Data Center, NESDIS, NOAA, U.S. Department of Commerce, http://www.data.gov/geodata/g600037/, accessed 2010a. NCDC, North American Drought Monitor, National Climatic Data Center, NESDIS, NOAA, U.S.

Department

of

Commerce,

precip/drought/nadm/index.html, accessed 2010b.

http://www.ncdc.noaa.gov/temp-and-

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Nghiem, S. V., M. Borgeaud, J. A. Kong, and R. T. Shin, Polarimetric remote sensing of geophysical media with layer random medium model, Progress in Electromagnetics Research - Polarimetric Remote Sensing, ed. by J. A. Kong, Elsevier, New York, Vol. 3, Chapter 1, pp. 1-73, 1990. Nghiem, S. V., T. Le Toan, J. A. Kong, H. C. Han, and M. Borgeaud, Layer model with random spheroidal scatterers for remote sensing of vegetation canopy, J. Electromag. Waves Applic., 7(1), 49-76, 1993a. Nghiem, S. V., S. H. Yueh, R. Kwok, and D. T. Nguyen, Polarimetric remote sensing of geophysical medium structures, Radio Sci., 28(6), 1111-1130, 1993b. Nghiem, S. V., R. Kwok, S. H. Yueh, and M. R. Drinkwater, Polarimetric signatures of sea ice, 1, Theoretical model, J. Geophys. Res., 100(C7), 13665-13679, 1995. Nghiem, S. V., J. J. Van Zyl, W.-Y. Tsai, and G. Neumann, Potential application of scatterometry to large-scale soil moisture monitoring, JPL Doc. D-19523, Jet Propulsion Lab., California Inst. Tech., Pasadena, California, 2000. Nghiem, S. V., Advanced scatterometry for geophysical remote sensing, JPL Doc. D-23048, Jet Propulsion Lab., California Inst. Tech., Pasadena, California, 2001. Nghiem, S. V., E. G. Njoku, J. J. Van Zyl, and Y. Kim, Global energy and water cycle – Soil moisture variability pattern over continental extent observed with active and passive satellite data, JPL Doc. D-26225, Jet Propulsion Lab., California Inst. Tech., Pasadena, California, 2003.

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Nghiem, S. V., E. G. Njoku, G. R. Brakenridge, and Y. Kim, Land surface water cycles observed with satellite sensors,” J6.14, Proc. 19th Conference on Hydrology and 16th Conference on Climate Variability and Change, 85th Amer. Met. Soc. Meeting, San Diego, California, 2005. Nghiem, S. V., NASA satellite data for applications to early warning of droughts across the world – Examples for Africa, Inter-Regional Workshop on Indices and Early Warning Systems for Drought, Lincoln, Nebraska, 8-11 December 2009. Nghiem, S. V., J. Verdin, M. Svoboda, D. Allured, J. Brown, B. Liebmann, G. Neumann, E. Engman, and D. Toll, Improved drought monitoring with NASA satellite data, EWRI Currents, 12(3), 7, Environ. Water Res. Inst., Amer. Soc. Civil Eng., 2010. NIDIS, U.S. Public Law 109-430, 109th Congress, 2nd sess., National Integrated Drought Information System Act of 2006, 20 Dec.2006. NIDIS, The National Integrated Drought Information System Implementation Plan – A Pathway for National Resilience, NIDIS Implementation Team, 28 pp., July 2007. Njoku, E. G., and L. Li, Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz, IEEE Trans. Geosci. Rem. Sens., 37, 79-93, 1999 Njoku, E. G., T. J. Jackson, V. Lakshmi, T. K. Chan, and S. V. Nghiem, Soil moisture retrieval from AMSR-E, IEEE Trans. Geosci. Remote Sens., 41(2), 215-229, 2003. Njoku, E., AMSR-E/Aqua L2B Surface Soil Moisture, Ancillary Parms, & QC EASE-Grids V002, http://nsidc.org/data/docs/daac/ae_land_l2b_soil_moisture.gd.html, Boulder, Colorado USA: National Snow and Ice Data Center, 2004. NMQ, Stage-4 24hr QPE Accumulation, National Mosaic & Multi-Sensor QPE, NOAA National Severe Storms Laboratory, http://nmq.ou.edu/, 2009.

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NWS, September 2009 Weather in Review, NOAA National Weather Service, Southern Region Headquarters, http://www.srh.noaa.gov/images/ewx/wxevent/sep2009.pdf, 2009. Owe, M., A. Chang, and R. E. Golus, Estimating surface soil moisture from satellite microwave measurements and a satellite-derived vegetation index, Rem. Sens. Environ., 24, 131-345, 1988. Prevot, M., M. Dechambre, O. Taconet, D. Vidal-Madjar, M. Normand, and S. Galle, Estimating the characteristics of vegetation canopies with airborne radar measurements, Int. J. Rem. Sens., 14, 2803-2818, 1993. Resource Management Services, State of Montana multi-hazard mitigation plan and statewide hazard assessment, Land and Water Consulting, Big Sky Management, 251 pp., 2004. Shrivastava, S. K., N. Yograjan, V. Jayaraman, P. P. N. Rao, and M. G. Chandrasekhar, On the relationship between ERS-1 SAR/backscatter and surface/sub-surface soil moisture variations in vertisols, Acta Astronautica, 40(10), 693-699, 1997. Shrivastava, H. S., P. Patel, Y. Sharma, and R. R. Navalgund, Large-area soil moisture estimation using multi-incidence-angle RADARSAT-1 SAR data, IEEE Trans. Geosci. Remote Sens., 47(8), 2528-2535, 2009. Shoshany, M., T. Svoray, P. J. Curran, G. M. Foody, and A. Perevolotsky, The relationship between ERS-2 SAR backscatter and soil moisture: Generalization from a humid to semi-arid transect, Int. J. Remote Sens., 21(11), 2337-2343, 2000. Smith, J. A., D. J. Seo, M. L. Baeck, and M. D. Hudlow, An intercomparison study of NEXRAD precipitation estimates, Water Resources Res., 32(7), 2035-2045, 1996.

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Sorooshian, S., K. L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, Evaluation of PERSIANN system satellite-based estimates of tropical rainfall, Bull. Amer. Meteorol. Soc., 81(9), 2036-2046, 2000. Story, G., The difficulty of achieving good precipitation estimates for use in real-time drought monitoring, 6th U.S. Drought Monitor Forum, Lower Colorado River Authority, Austin, TX, 7-8 Oct. 2009. Svoboda, M. D., M. J. Hayes, and D. A. Wilhite, The role of integrated drought monitoring in drought mitigation planning, Ann. Arid Zone, 40(1), 1-11, 2001. Svoboda, M., D. LeComte, M. Hayes, R. Heim, K. Gleason, J. Angel, B. Rippey, R. Tinker, M. Palecki, D. Stooksbury, D. Miskus, and S. Stephens, The Drought Monitor, Bull. Amer. Meteorol. Soc., 83(8),1181-1190, 2002. Takada, M., Y. Mishima, and S. Natsume, Estimation of soil surface properties in peatland using ALSO/PALSAR, Landscape and Ecological Engineering, 5(1), 45-58, 2009. Texas Water Development Board, Highlights of the 2007 State Water Plan, Water for Texas 2007, 39 pp., vol. I, Doc. No. GP-8-1., 2007. Teng, W. L., Wang, J. R., and Doriaswamy, P. C., Relationship between satellite microwave radiometric data, antecedent precipitation index, and regional soil moisture, Int. J. Rem. Sens., 14: 2483-2500, 1993. Tsai, W.-Y., S. V. Nghiem, J. N. Huddleston, M. W. Spencer, B. W. Stiles, and R. D. West, Polarimetric scatterometry: A promising technique for improving ocean surface wind measurements, IEEE Trans. Geosci. Remote Sens., 38, 1903-1921, 2000.

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USDA, SCAN - Soil Climate Analysis Network, SCAN Brochure, Natural Resources Conservation Service, National Water & Climate Center National Soil Survey Center, 2009a. USDA, SNOTEL And Snow Survey & Water Supply Forecasting, SNOTEL Brochure, Natural Resources Conservation Service, National Water & Climate Center, NWCC Rev. 3, 2009b. van de Griend, A. A. and M. Owe, Microwave vegetation optical depth and inverse modelling of soil emissivity using Nimbus/SMMR satellite observations, Meteorol. Atmos. Phys., 54, 225-239, 1994. Verdin, J., C. Funk, R. Klaver, and D. Roberts, Exploring the correlation between Southern Africa NDVI and Pacific sea surface temperatures: results for the 1998 maize growing season, Int. J. Remote Sens., 20(10), 2117-2124, 1999. Wagner, W., J. Noll, M. Borgeaud, and H. Rott, Monitoring soil moisture over the Canadian prairies with the ERS scatterometer, IEEE Trans. Geosci. Remote Sens., 37(1), 206-216, 1999. Wagner, W., and K. Scipal, Large-scale soil moisture mapping in western Africa using the ERS scatterometer, IEEE Trans. Geosci. Remote Sens., 38(4), Part 2, 1777-1782, 2000. Wang, J. R., and B. J. Choudhury, Remote sensing of soil moisture content over bare field at 1.4 GHz frequency, J. Geophys. Res., 86, 5277-5282, 1981. Wang, J. R., Passive microwave sensing of soil moisture content: the effects of soil bulk density and surface roughness, Remote Sens. of Environ., 13, 329-344, 1983. Wang, J. R., Effect of vegetation on soil moisture sensing observed from orbiting microwave radiometers, Rem. Sens. Environ., 17, 141-151, 1985.

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Western Governors’ Association, Creating a Drought Early Warning System for the 21st Century, The National Integrated Drought Information System, 13 pp., Denver, Colorado, 2004. Wigneron, J.-P., L. Laguerre, and Y. H. Kerr, A simple parameterization of the L-band microwave emission from rough agricultural soils, IEEE Trans. Geosci. Rem. Sens., 39, 1697-1707, 2001. World Meteorological Organization, Expert agree on a universal drought index to cope with climate risks, Press Release, WMO No.-872, United Nations, Copenhagen, Geneva, 2009. Zhang, X. Y., M. Goldberg, D. Tarpley, M. A. Friedl, J. Morisette, F. Kogan, and Y. Y. Yu, Drought-induced vegetation stress in southwestern North America, Environ. Res. Lett., 5(2), Art. 024008, 2010.

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List of Table

Table 1. Correlation results between seasonal TMI polarization ratio PR and seasonal SCAN volumetric soil moisture mv at 5-cm depth from linear regression analysis in the form of PR = α

⋅mv + β with a correlation coefficient ρ.

List of Figures

Figure 1. Seasonal TMI polarization ratio versus seasonal SCAN soil moisture at 5-cm depth in an agricultural area at Lonoke, Arkansas. All data are contemporaneous (collocated in time), and are 90-day running averages. The upper plot is for 10.7 GHz, and the lower one is for 19.3 GHz.

Figure 2. Seasonal QSCAT backscatter σ0VV versus seasonal TMI polarization ratio PR at 10.7 GHz in an agricultural area within 25 km around Lonoke, Arkansas.

All data are

contemporaneous (collocated in time), and are 90-day running averages.

Figure 3. Measurements around the NCDC GSOD Station 727760 (47.467oN 111.383oW) at Great Falls in Montana. Top panel is QSCAT σ0HH within 25 km around the station, middle panel is in-situ air temperature (magenta for minimum, black for average, and red for maximum), and bottom panel is precipitation from station rain gauge (there were missing data). Thin vertical lines align rain events to backscatter impulses. Yellow marks the period between 7/9/2000 and 9/5/2000 when there was very little rain.

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Figure 4. Effective precipitation frequency (%) measured by QSCAT the period of June-August 2009 (left panel), and drought levels from D0 to D4 from the U.S. Drought Monitor for weeks ending on the marked dates in 2009 (right panels). The USDM drought levels include D0 for abnormally dry, D1 for moderate drought, D2 for severe drought, D3 for extreme drought, and D4 for exceptional drought [Svoboda et al., 2001, 2002].

Figure 5. Soil moisture change (SMC) measured by QSCAT with the vertical polarization along ascending orbits in September to early October 2009. The color scale represents backscatter change in dB, and volumetric SMC in % with the Lonoke rating.

Figure 6.

Difference of AMSR-E monthly averaged soil moisture in % of mv(9/7/2009-

10/6/2009) − mv(6/29/2009-7/28/2009) showing seasonal SMC (left panel), and drought condition change between USDM drought maps in July and in September 2009 (right panels).

Figure 7. Soil moisture change (SMC) on 28 May 2009 compared to the two-week average between 14-28 May 2009 observed by: (a) QSCAT SMC represented by backscatter change in dB and by volumetric moisture change in % from the Lonoke rating, and (b) AMSR-E by volumetric moisture change in % with yellow-brown for drier and cyan-blue for wetter conditions.

Figure 8. Stage-4 24-hour precipitation measurements [NMQ, 2009] at 12:00 UTC in inches for: (a) 28 May 2009 in the upper panel, and (b) and 27 May 2009 in the lower panel.

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Figure 9. Comparison of QSCAT SMC with rain-gauge precipitation (RGP) for the period of 14 October 2008 and the ensuing 7 days: (a) mean SMC, (b) max SMC, and (c) RGP used in making USDM maps.

Figure 10. Weekly QSCAT mean SMC maps for the growing season in June-October 2009.

Figure 11. Weekly USDM maps for the growing season in June-October 2009.

Figure 12. SMC product demonstration in the NIDIS portal at the national level with USDM and AHP River Gauge layers (upper panel), and at the county level for Nebraska (lower panel).

Figure 13. Soil moisture change (SMC) across Africa [Nghiem, 2009] measured by QSCAT with the vertical polarization along ascending orbits on 16 October 2009. The color scale represents backscatter change in dB, and volumetric SMC in % with the Lonoke rating. Data gaps between orbits are seen in dark bands on land.

High SMC is observed in an extensive

pattern (yellow areas) across South Africa curving northward to Botswana, Namibia, Zambia, and Mozambique, with a wet region seen in Madagascar.

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Table 1. Correlation results between seasonal TMI polarization ratio PR and seasonal SCAN volumetric soil moisture mv at 5-cm depth from linear regression analysis in the form of PR = α

⋅mv + β with a correlation coefficient ρ.

Fall-winter Spring-summer All year

α 0.00109 0.00131 0.00124

10.7 GHz β 0.00766 -0.00108 0.00235

ρ 0.977 0.988 0.936

α 0.000931 0.000960 0.000894

19.3 GHz β 0.00587 -0.000908 0.00320

ρ 0.953 0.946 0.792

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(a)

(b) Figure 1. Seasonal TMI polarization ratio versus seasonal SCAN soil moisture at 5-cm depth in an agricultural area at Lonoke, Arkansas. All data are contemporaneous (collocated in time), and are 90-day running averages. The upper plot is for 10.7 GHz, and the lower one is for 19.3 GHz.

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Figure 2. Seasonal QSCAT backscatter σ0VV versus seasonal TMI polarization ratio PR at 10.7 GHz in an agricultural area within 25 km around Lonoke, Arkansas. All data are contemporaneous (collocated in time), and are 90-day running averages.

53

Figure 3. Measurements around the NCDC GSOD Station 727760 (47.467oN 111.383oW) at Great Falls in Montana. Top panel is QSCAT σ0HH within 25 km around the station, middle panel is in-situ air temperature (magenta for minimum, black for average, and red for maximum), and bottom panel is precipitation from station rain gauge (there were missing data). Thin vertical lines align rain events to backscatter impulses. Yellow marks the period between 7/9/2000 and 9/5/2000 when there was very little rain.

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Figure 4. Effective precipitation frequency (%) measured by QSCAT the period of June-August 2009 (left panel), and drought levels from D0 to D4 from the U.S. Drought Monitor for weeks ending on the marked dates in 2009 (right panels). The USDM drought levels include D0 for abnormally dry, D1 for moderate drought, D2 for severe drought, D3 for extreme drought, and D4 for exceptional drought [Svoboda et al., 2001, 2002].

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Figure 5. Soil moisture change (SMC) measured by QSCAT with the vertical polarization along ascending orbits in September to early October 2009. The color scale represents backscatter change in dB, and volumetric SMC in % with the Lonoke rating.

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Figure 6. Difference of AMSR-E monthly averaged soil moisture in % of mv(9/7/200910/6/2009) − mv(6/29/2009-7/28/2009) showing seasonal SMC (left panel), and drought condition change between USDM drought maps in July and in September 2009 (right panels).

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Figure 7. Soil moisture change (SMC) on 28 May 2009 compared to the two-week average between 14-28 May 2009 observed by: (a) QSCAT SMC represented by backscatter change in dB and by volumetric moisture change in % from the Lonoke rating, and (b) AMSR-E by volumetric moisture change in % with yellow-brown for drier and cyan-blue for wetter conditions.

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Figure 8. Stage-4 24-hour precipitation measurements [NMQ, 2009] at 12:00 UTC in inches for: (a) 28 May 2009 in the upper panel, and (b) and 27 May 2009 in the lower panel.

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Figure 9. Comparison of QSCAT SMC with rain-gauge precipitation for the period of 14 October 2008 and the ensuing 7 days: (a) mean SMC, (b) max SMC, and (c) precipitation used in making USDM maps.

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Figure 10. Weekly QSCAT mean SMC maps for the growing season in June-October 2009.

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Figure 11. Weekly USDM maps for the growing season in June-October 2009.

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Figure 12. SMC product demonstration in the NIDIS portal at the national level with USDM and AHP River Gauge layers (upper panel), and at the county level for Nebraska (lower panel).

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Figure 13. Soil moisture change (SMC) across Africa [Nghiem, 2009] measured by QSCAT with the vertical polarization along ascending orbits on 16 October 2009. The color scale represents backscatter change in dB, and volumetric SMC in % with the Lonoke rating. Data gaps between orbits are seen in dark bands on land. High SMC is observed in an extensive pattern (yellow areas) across South Africa curving northward to Botswana, Namibia, Zambia, and Mozambique, with a wet region seen in Madagascar.

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List of Publications (Section 1):

Journal Articles and Book Chapters

Nghiem, S. V., D. B. Wardlow, D. Allured, M. D. Svoboda, D. LeComte, M. Rosencrans, K. S. Chan, and G. Neumann, “Microwave Remote Sensing of Soil Moisture – Science and Applications,” book chapter, 53 pp., in Drought and Water Crises Book Series - Remote Sensing and Drought – New and Emerging Monitoring Approaches, Taylor and Francis Pub., in review, 2011. Nghiem, et al. (authors from JPL, USGS, NDMC, NOAA PSD, DFO, and others), “Pattern and Frequency of Soil Moisture Variability over the Continental United States,” manuscript in revision, 52 pp., 2011. Nghiem, S. V., J. Verdin, M. Svoboda, D. Allured, J. Brown, B. Liebmann, G. Neumann, E. Engman, and D. Toll, “Improved Drought Monitoring with NASA Satellite Data,” EWRI Currents, 12(3), 7, Amer. Soc. Civil Eng., Summer 2010. Nghiem, S. V., D. Balk, E. Rodriguez, G. Neumann, A. Sorichetta, C. Small, and C. D. Elvidge, “Observations of Urban and Suburban Environments with Global Satellite Scatterometer Data,”

ISPRS

Journal

of

Photogrammetry and

Remote Sensing,

64,

367-380,

doi:10.1016/j.isprsjprs.2009.01.004, 2009. Nghiem, S. V., and G. Neumann, “Remote Sensing of the Global Environment with Satellite Scatterometry,” keynote paper in Microwave Remote Sensing of the Atmosphere and

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Environment VI, ed A. Valinia, P. H. Hildebrand, and S. Uratsuka, Proc. of SPIE, Vol. 7154, 715402, doi:10.1117/12.804462, 11 pages, 2008.

Press Release

JPL Photo Journal, “Rapid Dry-Up of Rainwater on Land Surface Leading to the Santa Barbara Wildfire,” Internet article http://photojournal.jpl.nasa.gov/catalog/PIA12006, Jet Propulsion Laboratory, Pasadena, California, 8 May 2009.

Conference and Meeting Presentations

Nghiem, S. V., “NASA Satellite Data for Applications to Early Warning of Droughts across the World – Examples for Africa,” Inter-Regional Workshop on Indices and Early Warning Systems for Drought, Lincoln, Nebraska, 8-11 December 2010. Nghiem, S. V., “Satellite Observation of Soil Moisture Change and Applications to Drought Monitoring,” 6th U.S. Drought Monitor Forum, Lower Colorado River Authority, Redbud Center, Austin, Texas, 7-8 October 2009. Nghiem, S. V., “Geophysical Information from NASA Satellite Scatterometry – Western U.S. and California,” ESRI International User Conference, San Diego Convention Center, San Diego, California, 13-17 July 2009. Nghiem, S. V., “Satellite Remote Sensing of Soil Moisture for Drought Applications,” invited paper, National Integrated Drought Information System Knowledge Assessment Workshop –

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Contribution of Satellite Remote Sensing to Drought Monitoring, Boulder, Colorado, USA, 6-7 February 2008. Nghiem, S. V., G. R. Brakenridge, and G. Neumann, “Drought, wetland, and Flood Monitoring with Satellite Scatterometer,” EOS Trans, AGU, 88(23), Jt. Assem. Suppl., Abst. U53B-05, May 2007. Nghiem, S. V., G. R. Brakenridge, D. Cline, M. Dettinger, R. M. Dole, P. R. Houser, G. Neumann, E. G. Njoku, D. K. Perovich, K. Steffen, M. Sturm, J. Verdin, D. A. Wilhite, S. H. Yueh, and T. Zhang, “Global Observations of Land Surface Water with Satellite Active and Passive Microwave Sensors,” Satellite Observations of the Global Water Cycle, Irvine, California, 7-9 March 2007. Nghiem, S. V., “Drought Monitoring with NASA Satellite Data,” National Integrated Drought Information System (NIDIS) Implementation Plan, Longmont, Colorado, Sept., 2006.

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Section 2 (Led by U.S. Geological Survey)

Vegetation Drought Response Index

J. Brown, Y. Gu, and J. Verdin U.S. Geological Survey, EROS Data Center, Sioux Falls, SD 57198, (605) 594-6018

Summary New soil moisture and vegetation monitoring products were developed for integration into the weekly production of the U.S. Drought Monitor (USDM) map (http://drought.unl.edu/dm), the recognized national reference for drought conditions in the United States. A soil moisture change (SMC) product was developed using NASA scatterometer and radiometer data (led by JPL, see Section 1). The Vegetation Drought Response Index (VegDRI), originally formulated to use imagery from NOAA AVHRR instruments, was updated to use imagery from NASA MODIS instead, allowing us to integrate superior radiometric and geometric characteristics. Production of VegDRI with MODIS imagery was implemented on a very fast turn-around (less than 12 hours after satellite acquisition) by taking advantage of the new NASA LANCE system for rapid delivery of swath surface reflectance data, and the implementation of a new expedited MODIS (eMODIS) processing chain at USGS EROS. Frequency and timing of production were designed to meet USDM author weekly schedules, and automated procedures were implemented to ingest SMC and VegDRI products into the GIS environment used by authors to make weekly adjustments to drought category zones. Based on positive feedback from USDM authors, USGS

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EROS is now investing in the transition of VegDRI production from science to operations. Systems engineering staff are implementing a robust, automated operational VegDRI production chain to support the U.S. drought monitoring community on into the future, beyond the life of this project. The operational VegDRI system is expected to be actively supporting USDM authors throughout the 2011 growing season.

VegDRI Project Background and History

Initial development of the Vegetation Drought Response Index (VegDRI) began in 2002, funded by a seed grant from the U.S. Geological Survey (USGS). The grant funded prototype efforts, in collaboration with the National Drought Mitigation Center (NDMC), to develop methods to improve drought monitoring in the north central plains of the U.S. The initial study (outlined by Brown et al. 2008) presented an approach to synoptic monitoring of drought impacts on vegetation based on phenological indicators using data from a series of optical sensors—the Advanced Very High Resolution Radiometer (AVHRR).

This seed money also supported

writing several research proposals. Subsequent funding awarded in 2006 by the NASA Applied Sciences program (NASA Decisions/05-2-0000-0167) allowed for further model enhancements and geographic expansion of the VegDRI modeling approach to the conterminous U.S. (CONUS). The project goal was to improve drought early warning response and mitigation through the use of timely monitoring products designed to meet the needs of the U.S. community of drought-sensitive decision-makers. The VegDRI methodology builds upon the monitoring traditions of both the climate and remote sensing communities. The approach provides improvement in spatial detail, spatial coverage,

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and the timely delivery of information in a variety of accessible formats (for example, maps, descriptive text, and statistics), increasing the value, spatial detail, and relevancy of drought information available to decision makers. A key project goal was to integrate VegDRI into the weekly operational process of making the U.S. Drought Monitor (USDM) map by providing data sets to the USDM authors. Since its launch in 1999, the USDM (Svoboda et al. 2002) has been the state-of-the-practice drought monitoring tool used in the United States. The USDM provides a general assessment of weekly drought conditions (both agricultural and hydrologic) across the nation. It is produced using a hybrid approach that involves a number of variables including short- and long-term climate-based drought indicators, hydrologic indices, and remote sensing information. Ten years ago, the USDM map was produced at the effective scale of climate divisions (multi-county aggregations with similar climate), however developments in recent years, including finer resolution point source and gridded climatic data, have led to improvement in resolution approaching individual county scale or better when we incorporate remote sensing products (Svoboda, pers. communication). Climate and meteorological data have long been the primary sources for creating drought indices for monitoring, including the USDM. Climate-based drought indices characterize the intensity of dryness as compared to long-term average or normal condition, and are usually calculated from one or more of the following variables: rainfall, temperature, snow pack, stream flow, soil moisture, and other water supply indicators. The spatial coverage and detail of climate-based drought indices is limited as these depend upon meteorological data collected at stations that are non-uniformly distributed across the country. As a result, climate-based monitoring tools characterize relatively broad-scale drought patterns and the level of accuracy and spatial detail in

70

the information they provide depends on the density and geographic placement of stations across the landscape.

Time-series Vegetation Index (VI) Applied to Operational Monitoring

Satellite-based observations have proven very useful for assessing broad-scale vegetation condition anomalies (that is, apparent changes in vegetation health), but the specific cause or causes for the anomalies may not always be determined solely from the remotely sensed data. A number of natural (for example, drought, flooding, fire, pest infestation, and hail damage) and anthropogenic (for example, land cover/land use conversion) events can produce these anomalies (Asner et al. 2000; Breshears 2005; Goetz et al. 2006; Kogan 1990; Parker et al. 2005; Peters et al. 2000; Peters et al. 2002; Wang et al. 2003). Therefore, effective drought monitoring approaches must consider both climate station and satellite-based information, as well as other environmental parameters that may influence the effects of drought and its severity on vegetation, such as soil properties or land cover type. It follows that the integration of coarserresolution climate data and higher-resolution satellite-based vegetation observations will provide an improvement to monitoring and characterization of the spatial extent, intensity, and local variability of drought’s effect on vegetation conditions.

Product Requirements for Drought Decision Support

Because the effects of drought vary from region to region and season to season, there are many approaches for early warning and monitoring that range from sub-national, to national, regional,

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and even global in geographic scope. To be successful, monitoring approaches must deal with a multitude of challenging drought characteristics (e.g., the difficulty in determining drought onset and termination, the multiple, varying definitions of drought, and the existence of multiple indicators --climatic, phenologically-related, VI, hydrologic, etc.). The drought community in the United States, largely through the coordination and leadership of the U.S. Drought Monitor (USDM) authors (Svoboda et al. 2002) and the National Integrated Drought Information System (WGA 2004), has suggested product/data requirements for successful drought monitoring shown in Table 1.

Table 1. Operational National Level Drought Monitoring Product Requirements

Product geographic coverage

National synoptic (minimum 48 state coverage)

Product schedule

Weekly, available Monday a.m.

Spatial scale

1-4 km2, sub-county details

Product latency