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Methods and Examples for Remote Sensing Data Assimilation in Land Surface Process Modeling Heike Bach and Wolfram Mauser, Member, IEEE
Abstract—Land surface process models describe the energy, water, carbon, and nutrient fluxes on a local to regional scale using a set of environmental land surface parameters and variables. They need time series of spatially distributed inputs to account for the large spatial and temporal variability of land surface processes. In principle many of these inputs can be derived through remote sensing using both optical and microwave sensors. New approaches in four-dimensional data-assimilation (4DDA) form the basis to combine remote sensing data and spatially explicit land surface process models more effectively. This paper describes basic techniques for 4DDA in land surface process modeling. Two case studies were carried out to demonstrate different successful approaches of remote sensing data assimilation into land surface process models. The assimilation of surface soil moisture estimates from European Remote Sensing (ERS) synthetic aperture radar data in a flood forecasting scheme is presented, as well as the combination of a land surface process model and a radiative transfer model to improve the accuracy of land surface parameter retrieval from optical data [Landsat Thematic Mapper (TM)]. Index Terms—Biomass, canopy reflectance model, crop growth model, flood forecast, GeoSAIL, soil moisture.
I. INTRODUCTION
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HERE HAS BEEN considerable progress over recent years in regional spatially explicit models of bio-geophysical processes of the land surface. Initially, complex land surface process models of local validity were developed in plant ecology and boundary layer meteorology to understand the feedback between soil and atmosphere as well as to understand plant growth and carbon assimilation of single plants and plant canopies [1], [2]. The performance of these models can be checked against direct point measurements. They often use a large to very large number (sometimes more than 100) of variables and input parameters to accurately reproduce observations. Complexity is further increased when moving to spatially explicit models, which also take the heterogeneity of the land surface into account. Simplified models were derived from these complex models as early as in the late 80s driven by the need of meteorologists to better represent land surface processes in global circulation models (GCMs). Since the early 90s, spatial and temporal variability of land surface Manuscript received March 22, 2002; revised February 21, 2003. This work was supported in part by the European Space Agency under Contract GeoBIRD 12950/98/NL/GD. H. Bach is with VISTA Remote Sensing in Geosciences GmbH, D-80333 Munich, Germany (e-mail:
[email protected]). W. Mauser is with the Department of Earth and Environmental Sciences, University of Munich, D-80333 Munich, Germany (e-mail: W.Mauser@ iggf.geo.uni-muenchen.de). Digital Object Identifier 10.1109/TGRS.2003.813270
properties [mainly leaf area index (LAI)] has been incorporated in numerical weather prediction (NWP) and climate models [3]–[5]. In these implementations, LAI values were assigned to broad vegetation classes by means of a lookup table. As an alternative, algorithms to retrieve necessary information on vegetation presence and phenology using operational satellite data were developed [6], [7], in particular using data from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration satellites. Regional, spatially explicit land surface process models can be used to bridge the gap between the scale of GCMs and the locally verifiable land surface process descriptions. At the same time, they are becoming valuable tools for decision-making since most environmental management decisions in terms of land use, land management, water management, and environmental protection are made on the regional scale. Spatially explicit regional process models need time series of spatially explicit inputs with an adequate spatial and temporal resolution. The necessary inputs span a broad range of environmental variables and involve elevation, land use, meteorological drivers like temperature and rainfall, variables describing vegetation status like surface roughness, leaf area index, or chlorophyll content, and soil variables like moisture content or soil hydraulic properties. Conventional approaches to provide these inputs are usually based on point measurements and spatial interpolations. These methods have proved to be time consuming and, in many cases, impractical and insufficient. The deficient availability of spatial input variables is, therefore, a major stepping-stone in the further improvement of regional land surface process models [8]. In principle, remote sensing can provide spatially distributed measurements of electromagnetic land surface properties at spatial and temporal scales which are well suited for regional land surface process modeling. However the measured radiation fluxes are caused by scattering, absorption, and emission of radiation at the land surface and rarely represent a desired land surface variable in a straightforward way. Therefore, remote sensing measurements almost never can be used directly as input to land surface process models. Hence, techniques for the assimilation of time series of spatial remote sensing measurements into land surface process models are required. Four-dimensional data assimilation (4DDA), which denotes assimilation of temporal series of three-dimensional datasets, bridges the gap between the observed radiation properties of the land surface and the description of the land surface processes in the models.
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4DDA has a longer tradition in meteorology, where it has been especially developed to improve GCMs by utilizing all available observations [3]. Compared to meteorological applications, the operational status of 4DDA in regional land surface process models is still in an early stage. The level of utilization of remote sensing data in land surface process models rarely exceeds simple land use classifications. Pilot projects and research on this topic mostly concentrate on hydrological processes [9]–[11]. To fully utilize the potential of existing and future earth-observing instruments for 4DDA in land surface process models sophisticated approaches are necessary, which exploit synergies between the whole range of available and future optical and microwave sensors. New approaches should consider the following points. • Remote sensing sensors measure electromagnetic properties of a surface and land surface variables indirectly. The properties of the surface, which cause scattering, reflectance, transmission, absorption, or emission, must be converted into meaningful land surface variables such as leaf area or soil moisture using retrieval models. • The remote sensing signal contains undesired influences. In the optical spectral range the atmosphere strongly influences the measured signal. This must be eliminated using atmospheric correction schemes. SAR backscatter is strongly influenced by the speckle effect, that makes pixel-based land surface parameter retrieval difficult. • Spectral information can be ambiguous. Multiple solutions of an inversion of a radiative transfer model are possible especially if the equation system for the retrieval is underdetermined. The radar backscatter at the land surface is influenced by at least two factors: dielectric constant and surface roughness. A single observation without any further independent information is, therefore, not sufficient to determine both. Progress in the retrieval of land surface variables is, therefore, crucially dependent on the use of ancillary data, which ideally restrict ambiguities in remote sensing measurements to valid values for the variables of interest. • State variables in land surface process models may not be identical with the variables that can be derived from remote sensing. For instance, with C-band SAR surface soil moisture (down to approximately 2-cm depth) can be estimated. However, in the case of water balance calculations, the soil moisture of the whole root zone (which may reach down to 250 cm) is also required. Models, therefore, have to be developed to utilize the incomplete measurements available from remote sensing to improve results whenever no conventional measurements are available. II. DATA ASSIMILATION APPROACHES There are several possible options for assimilating remote sensing data into a land surface process model. The main differences between the different approaches lie in their goals and their degree of abstraction and sophistication. The following four approaches try to give a systematic overview. They correspond well with described interfaces for embedding remote sensing data in models for forest carbon dynamics [12].
A. Approach 1: Determination of Model Initialization Parameters From Remote Sensing Data to Substitute Classically Derived Parameter Values Statistical methods or parameter retrieval procedures are applied to transform remote sensing measurements into spatial distributions of parameter fields. Prominent examples are terrain elevation and land use derived from remote sensing data. During the initialization of the land surface process model, land use classes are parameterised through tables, e.g., on water use or photosynthetic efficiency, root density, or phenological response on environmental conditions. The values of these parameters change very slowly with time, and therefore remote sensing data with limited temporal resolution are sufficient for this purpose. Approach 1 represents the classical and most widely accepted use of remote sensing in land surface process models. B. Approach 2: Update of Model State Variables Through Remote Sensing Measurements In this case, remote sensing measurements are converted into temporally changing values of model state variables. Each time a remote sensing measurement takes place the state variables are updated instead of being derived from rate variables and relations inside the models, which are usually based on some reasonable physical assumption like conservation laws for mass and energy. The system described by the land surface process model is, therefore, forced into a realistic state each time a remote sensing measurement is carried out and the measured values are supplied. This prevents the uncontrolled accumulation of systematic errors. An example of this approach is given below by assimilating surface soil moisture estimated from European Remote Sensing (ERS) synthetic aperture radar (SAR) data into a flood forecasting model prior to a flood in order to improve the forecast of magnitude and volume of a flood. C. Approach 3: Parameter Adjustment Through Model Recalibration Recalibration is also based on the determination of values of state variables through remote sensing. It can be applied whenever calculated values of state variables deviate from the values of the same variables derived from remote sensing measurements. To recalibrate model initialization parameters, the process model is run again for the antecedent time steps. The values of a set of model initialization parameters are changed until the model reproduces the measured value of the state variable. This procedure results in a new set of values of the model initialization parameters, which are then used for the determination of the further development of the system. The challenge of this option lies in the selection of appropriate initialization parameters to be optimized until simulation and observations correspond best. For this purpose, sensitivity studies are required. A good example of this approach is given in [13], where multiple time series of microwave brightness temperature are used for the determination of soil hydraulic properties required for a soil water and energy balance model.
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D. Approach 4: Estimation and Optimization of State Variables Through Model Inversion In the case of model inversion, the procedure of Approach 3 is inverted. For this purpose, the original land surface process model is extended to be able to also simulate remote sensing observations at any given state. The state of the model is represented by a set of values of state variables (e.g., LAI, biomass, plant height, soil moisture), which are converted into simulated spectral reflectances or backscatter intensities. The computed reflectance values are then compared with the reflectance or backscatter values measured with remote sensing instruments. The values of the model state variables are varied systematically until modeled reflectances and the measured reflectances agree within a given error margin. While this procedure can be applied universally, the result is only valid if a unique solution exists for the inversion problem. This is hard to prove and rarely the case for complex models and arbitrarily chosen sets of parameters. There are several approaches to resolve the problem of uniqueness of inversion. One approach is to only change one parameter when inverting. The inversion should also produce physically and ecologically meaningful results. Using ancillary data and knowledge on reasonable values of state variables to restrict the solution space can ensure this. To illustrate this, an assumed value of 0.2 for the leaf area index of a 20-year old deciduous forest in Central Europe in May should be considered unreasonable, whereas the same value should be considered reasonable in December. Results of a case study for model inversion to retrieve state variables are given in the second case study. It demonstrates how the spatial heterogeneity of vegetation parameters can be determined on the regional scale by inverting an optical radiative transfer model for vegetation canopies in combination with a land surface process model, which describes vegetation growth. III. RESULTS OF CASE STUDIES In order to prove the validity and practicability of the different approaches to 4DDA of remote sensing data into land surface process models, two case studies were carried out. They shall demonstrate improvements in the performance of the models through the assimilation of remote sensing data. A. Case Study 1: The Use of Remotely Sensed Soil Moisture in Flood Forecasting The flood model integrated flood forecasting system (IFFS) [14] serves as an example for the first and second approach to 4DDA, exploiting synergies of Landsat and ERS. IFFS is a hydrological model used for the translation of rainfall into runoff. IFFS determines the hydrograph and the peak discharge of a flood, which is responsible for the extent of flooding and the discharge volume. The volume of a flood is important to manage reservoirs for flood retention purposes. The structure of IFFS is illustrated in Fig. 1. It consists of a static representation of the watershed properties (upper part of Fig. 1), which is initialized at start time, and a process model (lower part of Fig. 1), which converts rainfall into runoff depending on rainfall, storage capacity, and soil moisture.
Fig. 1. Methodology of the IFFS model. The shaded part of the static description of the watershed in the upper part of the scheme is derived from remote sensing data. Dynamic model variables are provided from the surface soil moisture distributions estimated from ERS SAR microwave data before the rainfall event. Together with rainfall data as driving variable the runoff is modeled. The hydrological kernel model is the SCS TR20 [15], a standard in hydrological practice.
B. Model Initialization Parameters IFFS is initialized using static watershed properties like land use, topography, river network, and soil hydraulic properties. In the case study the Ammer watershed, which covers an area of 729 km and is situated in the Bavarian alpine forelands, was selected. This watershed is equipped with a TDR soil moisture measurement network and serves as a test site for soil moisture retrieval with ERS SAR data. Land use is derived from Landsat Thematic Mapper (TM) data. River cross sections as well as reservoir volumes were determined in the field. An interferometrically derived elevation model was used as prime remotely sensed data source for IFFS to determine topographic information on the watershed. It was derived from phase information of a “Tandem” pair of ERS1 and ERS2 SAR data acquired within 24 h. The IFFS model is run on subwatersheds, which are automatically classified from the digital elevation model (DEM). The horizontal resolution of 30 m and the relative vertical accuracy of the DEM of 10 m are sufficient for the extraction
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of river network, flow patterns, and subwatersheds in hilly and mountainous regions. Together with a soil map, the land use and slope determine the spatial distribution of the storage capacity of the watershed, which is expressed in dimensionless CN values, stands for a perfectly imwhich ranges from 0 to 100. CN stands pervious surface with no storage capacity, and CN for the storage capacity of a dense forest on a deep sandy soil. The shaded boxes in Fig. 1 mark the watershed initialization parameters, which were determined using remote sensing data sources. C. Updating of Dynamic Model Variables Soil moisture content determines the extent of saturation of the watershed and thereby the amount of water that does not infiltrate into the soil but runs off the surface and creates a flood. The same amount of rainfall, which normally does not lead to a significant increase in water level, can cause a severe flood, if the soil has already been filled with water and the storage capacity is close to zero. Usually the actual soil moisture distribution before a storm is not known. As a surrogate, hydrologists use an antecedent precipitation index that is derived from rainfall measurements over the preceding days. However, this parameter usually does not adequately reflect the full temporal and spatial variability of soil moisture. Large errors in flood and inundated area forecast may occur as a result. SAR data in the Cor L-band region may be used to derive estimates of surface soil moisture distributions. Since the actual infiltration rate depends strongly on the soil water content at or close to the surface, surface soil water content derived from microwave data closely relates to the desired physical variable. With this information, the actual water storage capacity of the soil is determined as input into the rainfall-runoff model. D. Retrieval Method for Surface Soil Moisture From ERS SAR Data The retrieval of surface soil moisture from microwave data has been studied by a series of authors, e.g., [16]–[20]. The moisture content of the top soil-layer, which was used in IFFS, was retrieved from ERS SAR data by using a semiempirical IGGF model [21]. It is restricted to nonforested areas, since C-band microwaves cannot penetrate forest. This approach is shown in Fig. 2. The first step consists of a physically based correction of the influence of topography on backscatter and resolution of the ERS SAR image. This results in an image of values of flat terrain [22]. The approach then equivalent empirically corrects the influences of vegetation type, surface values. values are empiriroughness, and biomass on cally converted into dielectric constant of a soil-water mixture of an equivalent bare field with an rms roughness of 2.4 cm. For this particular situation, extensive model calculations using the physically based backscatter model MIMICS [23], [24] as well as ground truth measurements are available, which relate the dielectric constant to the backscattering coefficient . To establish a solid empirical dataset, ten to 12 agricultural fields in the Ammer watershed were equipped with TDR-profiles. Soil moisture together with biomass and plant height were continuously measured during the growing season from 1993 to 1997
Fig. 2. Schematic drawing of the influences on the SAR backscatter value, which are semiempirically corrected in the IGGF model. The semiempirical approach was developed to retrieve surface soil moisture from ERS SAR. It physically models the influence of topography on backscatter and resolution to convert backscatter into values of flat terrain. It empirically corrects influences on values from vegetation type and biomass. Finally, it converts dielectric constant into soil moisture using soil physical properties (from [32]).
and served as an empirical basis to set up the correction procedure. Table I gives the empirical relations, which were extracted from the combined analysis of ERS SAR backscatter values and ground truth measurements for different land uses and then used to convert backscatter into the dielectric constant of the soil-water mixture under different land uses. Finally the dielectric constant is converted into soil moisture using soil physical properties and relationships between backscatter and dielectric constants according to [25]. For three annual crops, soil moisture was measured on field separate from those which were used for calibration. In the Ammer-watershed the IGGF model shows good agreement between measured and estimated surface soil moisture values with an rms error of 4.6 Vol.%. The application of the IGGF model to a test site in Flevoland showed that this model works well for annual vegetation if all the ancillary data is available with the appropriate accuracy. The comparison of field-measured and modeled soil moisture values for different land covers in Flevoland is shown in Fig. 3 with an rms of 7.4 Vol.%. This indicates that the approach is transferable at least within the same climatic region without change of the empirical calibration relations. The result of the IGGF model for surface soil moisture retrieval in the Ammer catchment is shown in Fig. 4. Surface soil moisture is provided for May 18, 1999, two days before a heavy rainfall event, which caused a 200-year flood in the 729-km Ammer catchment in the Bavarian part of the Alps. This flood caused exceptional damage. The surface moisture derived from SAR was used to update dynamic model variables describing the infiltration capacity of the soils. The surface soil moisture values estimated on the forest-free parts of the watersheds are assigned to the forested parts according to a nearest neighbor procedure, assuming that soil moisture under forest does not differ from nonforested areas in humid temperate climates. Fig. 5 shows model results of IFFS for a 200-year flood, which started on May 20, 1999. In the graph the measured discharge is compared with modeled discharge produced with different soil moisture information. The dotted curve shows the model result under the assumption of a dry watershed, which means several dry days
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TABLE I EMPIRICALLY DETERMINED RELATION TO CONVERT ERS SAR BACKSCATTER MEASURED ON DIFFERENT LAND USES INTO A DIELECTRIC CONSTANT OF AN EQUIVALENT BARE SOIL WITH SURFACE ROUGHNESS OF 4.2 cm ACCORDING TO ULABY et al. [20]. THE CORRELATIONS ARE BASED ON TWO TO THREE FIELDS PER LAND USE AND 15–16 DATES WITH CONCURRENT ERS OBSERVATIONS AND SOIL MOISTURE MEASUREMENTS (FROM [21]). R : COEFFICIENT OF DETERMINATION FOR THE “BACKSCATTER VERSUS DC” FUNCTION. RMS HEIGHT: ESTIMATED ROUGHNESS (IN CENTIMETERS). OFFSET: ESTIMATED OFFSET FOR ROUGHNESS CORRECTION (IN DECIBELS). : BACKSCATTER COEFFICIENT (IN DECIBELS). DC: DIELECTRIC CONSTANT. BIOM: DRY BIOMASS (KILOGRAMS PER SQUARE METER). Tm: TRANSMISSIVITY OF VEGETATION CANOPY (IN PERCENT)
E. Case Study 2: Parameter Determination Through Model Inversion
Fig. 3. Comparison of the measured and estimated surface soil moisture using ERS SAR data from the Dutch test site Flevoland and the IGGF model [26]. The IGGF model was developed and parameterized in the Ammer test site in Upper Bavaria and transferred to the Flevoland test site without adaptation of model parameters. Field averages of the SAR signal were used to reduce speckle effects. The dashed line is the 1 : 1 line. The solid line shows the regression.
before the rainfall. The simulated discharge values are much too small and would not even be enough to produce a flood alert. The total discharge volume produced under this assumption is only about half of the observed discharged volume (27 versus 53 Mio m ). The broken curve shows the result of the model calculation using a wet watershed before the flood. In this case, the model overestimates the peak discharge as well as discharge volume. When using soil moisture information derived from ERS data taken the day before the flood the solid grey line shows the closest correspondence between measured and modeled peak discharge and discharge volume. This clearly demonstrates the potential for improved flood simulation, which lies in the use of remote-sensing-derived model state variables in integrated modeling environments like IFFS.
The concept of data assimilation using Approach 4 was tested within the European Space Agency (ESA) GeoBIRD project. The target of the study was to improve parameter retrieval from optical and microwave remote sensing data using coupled land surface process and radiative transfer models. The developed approaches were tested in three agricultural test sites in Central Europe (Ammer/Alpine Foreland, Flevoland/Holland, and the Upper Rhine Valley/Germany). Model inversion is a common technique to estimate values of internal model state variables from measurements. Whenever results of forward modeling can be measured, one can work ones way backward through the model to arrive at the unknown values of internal model state variables of interest, which would produce the measured result. This approach is valid if the model produces unique solutions. Microwave and optical radiative transfer models are available, which convert vegetation state variables (e.g., LAI, biomass, plant height) into reflectance/emissivity or the corresponding radiation fluxes at the position of the sensor whenever the vegetation type is known. If radiation flux or reflectance/emissivity is known the inversion of these radiative transfer models leads to values of vegetation state variables like leaf area, biomass, fraction of green and brown leaves, etc. The major drawback of this approach is that there is usually no unique solution of the inversion. This may lead to physically and ecologically invalid results for the retrieved state variables if a reflectance image is inverted. Therefore, a more sophisticated approach is needed, which draws on the results of a land surface process model to support the inversion process. A land surface process model simulates the development of a defined vegetation type as a result of environmental conditions such as rainfall, temperature, soil moisture, etc. For a given plant type, soil type and meteorological history and agricultural management practice a land surface process model can, for each day in the growing season and each vegetation state variable, provide a range of valid parameter values, within which the solution of
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Fig. 4. (Left) Land use map of the Ammer catchment in Upper Bavaria derived from a Landsat TM image classification. (Center) Terrain-corrected ERS SAR backscatter image. Land use and backscatter are used as input for the soil moisture estimation using the IGGF model for agricultural areas and meadows. (Right) ERS-derived surface soil moisture distribution as input to the flood model IFFS.
Fig. 5. Comparison of measured and modeled runoff for different soil moisture conditions at the outlet of the Ammer catchment. Wet and dry soil moisture situations illustrate the sensitivity of the modeled runoff (peak and volume) to soil moisture. After assimilation of ERS-derived soil moisture distributions into the flood model, the computed runoff peak and volume are very close to measurements.
the inversion of the radiative transfer model is then sought. This restriction of the solution space of the inversion largely avoids ambiguities and permits detection of the effects of pests or fertilizer deficits on the plants. The strategy, which is used in this case study, is schematically shown in Fig. 6. GeoSAIL [27] (see box in Fig. 6) is used as an optical canopy reflectance model. It considers vegetation type, leaf area and architecture, leaf optical properties, distribution of green and brown leaves in the canopy, soil background, and irradiance conditions. Model outputs are spectral reflectances of the vegetation canopy. GeoSAIL is combined with the Process-oriented Modular
Environment and Vegetation Model (PROMET-V) land surface process model [28], [29] (see box in Fig. 6). The particular implementation in the case study is based on two feedback-loops [see boxes GeoBIRD-INV1 (left) and GeoBIRD-INV2 (right)], which are established to ensure that a stable, most probable, and spatially variable output of the land surface process model (namely biomass, yield, plant height) is assured through the use of remote sensing data. Fig. 6 contains two branches, a remote-sensing-based retrieval of plant parameters through canopy reflectance modeling on the left side and a land surface process-model-based retrieval of plant parameters from meteo-data and geo-biophysical maps to the right. As an example, the case of LAI is demonstrated in Fig. 6. In the left branch optical remote sensing data from Landsat TM are first atmospherically corrected using the LOWTRANbased Procedure Using LOWTRAN for Reflectance (PULREF) atmosphere correction procedure [30]. The observed reflectance spectra are then compared with modeled spectra calculated with the optical remote sensing model GeoSAIL [27], [31] using the vegetation state variables LAI, fraction of brown leaves, and surface soil moisture, which are inverted from the measured spectra in GeoBIRD-INV1 taking into account the plausible range of parameters as given by the land surface process model PROMET-V. Since numerical inversion of the radiative transfer model GeoSAIL is not possible, an iterative inversion approach was chosen. The spectral matching error between remotely sensed and modeled spectra was minimized applying the golden section method to speed up inversion [32]. The vegetation state variables LAI, fraction of brown leaves, and surface soil moisture, which result from the GeoBIRDINV1-loop, are input to a second inversion loop. This inversion is shown to the right and compares the modeled values of LAI using PROMET-V with the observed LAI, which is
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Fig. 6. Methodology for the combination of a land surface process model (PROMET-V, right) and an optical canopy reflectance model (GeoSAIL, left) for improved retrieval of bio-geophysical land surface parameters [32]. In the left feedback loop, land surface parameters (LAI, fraction of brown leaves, surface soil moisture) are derived from optical remote sensing data. In the right feedback loop, the land surface process model is adjusted to allow the improved simulation of biomass, canopy height, and yield. Both loops are coupled.
derived from remote sensing data. Differences lead to an adjustment in the values of either the model initialization parameters or the state variables of the land surface process model until the measured LAI value is most closely reproduced by PROMET-V. If this procedure is conducted for each pixel in a Landsat image, this results in a spatial distribution of modeled LAI values, which best reproduces the observed reflectances. From extended sensitivity analyzes, it was found that, in the case of LAI, plant density is the most sensitive model initialization parameter for annual crops. In the case of meadows, the days of cutting proved to be the most crucial and at the same time the least known model initialization parameter. Both parameters are also spatially highly variable, depending on the management practices of the individual farmer. Fig. 7 illustrates that this feedback between the optical observations and the land surface process model considerably improves the representation of the natural spatial variability of bio-physical variables computed by the land surface process model PROMET-V. Fig. 7 compares simulation results for grain yield in a test site in the Upper Rhine Valley modeled without (left) and with (right) data assimilation using Landsat TM data as input. A comparison of measured and modeled grain yield for 19 test fields is summarized in Table II. The results clearly show a considerable improvement of yield estimates caused by 4DDA of remote sensing data as opposed to modeling using standard literature values. The average retrieved maize yield is close to measurements when using 4DDA, and the standard deviation is well captured using the satellite images for the characterization of the spatial variability. Without 4DDA, no correlation between measured and simulated grain yield was obtained for the 19 considered fields. However, with 4DDA, the degree of determination for grain yield increases to a significant value of 0.61. For dry biomass, a degree of determination of 0.87 was reached
Fig. 7. Comparison of model results on plant production using standard GIS map layers as input (left side) and 4DDA technique, which couples a canopy reflectance model and a land surface process model as described in Fig. 6 (right side). The application of remote sensing data leads to a representation of within field spatial heterogeneity and a proper process representation in the model.
with 4DDA across all three GeoBIRD test sites, as can be seen from Fig. 8.
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TABLE II PROMET-V MODEL RESULTS WITH AND WITHOUT 4DDA OF LANDSAT-DERIVED LAI-VALUES COMPARED WITH MEASUREMENTS OF GRAIN YIELD FOR MAIZE ON 19 FIELDS IN THE UPPER RHINE VALLEY
High temporal coverage is where demand for remote sensing data is largest. ASAR on ENVISAT with its potential temporal repeat cycle of 1–3 days using the wide swath mode will certainly strongly enhance the acceptance of microwave remote sensing data among the land surface modeling community. To fully exploit the potential of remote sensing, synergies between the whole range of available sensors should be evaluated and the complete information content, which accumulates over consecutive observations, should be assimilated into integrated model/observation environments. Data assimilation techniques using optical remote sensing data are currently more sophisticated than SAR-based methods. The maturity of these techniques should be balanced through research into combined applications and modified model structures, which can handle both data sources. ACKNOWLEDGMENT ERS SAR data were kindly provided by ESA in its PI-programme. The comments and input of the anonymous reviewers are gratefully acknowledged. REFERENCES
Fig. 8. Verification of the retrieved dry biomass of maize using Approach 4 based on ten remote sensing observations in the three GeoBIRD test sites. The dashed line is the 1 : 1 line. The solid line shows the regression (from [32]).
IV. CONCLUSION The case studies show that several procedures have already been established to assimilate remote sensing data into complex regional land surface process models. In each case, the potential for improving model performance toward a more realistic and accurate description of land surface heterogeneity was demonstrated. The proper treatment of spatial heterogeneity of the land surface is the key for the extended application of land surface process models in planning and decision making. Measurements of the spatial heterogeneity of the land surface are the domain of remote sensing. 4DDA of remote sensing data into land surface process models will, therefore, strongly improve land surface process modeling and should become a central future research task in remote sensing. Information from the land surface process models can be used in turn to improve the understanding and interpretation of remote sensing measurements. Land surface models can constrain the retrieval of land surface properties from remote sensing measurements to physically and ecologically meaningful values. Since SAR systems are largely weather independent they are an ideal data source for dynamic land surface process models.
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BACH AND MAUSER: METHODS AND EXAMPLES FOR REMOTE SENSING DATA ASSIMILATION IN LAND SURFACE PROCESS MODELING
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Heike Bach was born in Mettlach, Germany, in 1965. She received the M.S. degree in geography/hydrology from the University of Freiburg, Freiburg, Germany, in 1990, and the Ph.D. degree from the University of Munich, Munich, Germany, in 1995. From 1991 to 1994, she was a Project Scientist with the Institute for Geography and Institute for Applied Geology, University of Munich. Her main academic research activities were the determination of hydrological and agricultural parameters from hyperspectral remote sensing data. In 1995, she founded the company VISTA—Remote Sensing Applications in Geosciences, Munich, Germany. Since then, her work concentrates on the transfer of scientific results into practical applications, e.g., yield estimation and precision agriculture. Another key point of her work consists of the application of remote sensed information for hydrological water balance and flood models. She is Principal Investigator for the European Space Agency for the ERS-2, ENVISAT-program and a Principal Investigator for the German Aerospace Center (DLR), Oberpfaffenhofen, Germany, for the SRTM mission.
Wolfram Mauser (M’92) was born in Innsbruck, Austria, in 1955. He received the M.S. degrees in experimental physics and geography/hydrology and the Ph.D. degree in hydrology, in 1979, 1981, and 1984, respectively, all from the University of Freiburg, Freiburg, Germany. In 1981, he was with the University of Maryland, College Park, and the Goddard Space Flight Center, Greenbelt, MD, in the field of remote sensing and hydrology. From 1984 to 1991, he was an Assistant Professor with the Department of Hydrology, Institute for Physical Geography, University of Freiburg. In 1991, he was appointed Full Professor for geography and geographical remote sensing and head of the Institute for Geography, University of Munich, Munich, Germany. He has been working in the field of remote sensing and hydrology within the major European research programs. He was appointed Principal Investigator by the European Space Agency (ESA) for its ERS-1, ERS-2, and ENVISAT program, and is Principal Investigator for the SRTM mission. He is a member of the Mission Advisory Group of the Earth Explorer Surface Processes and Ecosystem Changes Through Response Analysis (SPECTRA) Mission planned by ESA. He took part in the MAC-Europe, E-MAC, and DAISEX-campaign. His special research interest is the development of image processing algorithms for radar and hyperspectral optical data and the development of land surface processes models. Dr. Mauser is Chairman of the German National Committee of Global Change Research.
