Soil moisture content assessment based on Landsat 8 red, near ...

Soil moisture content assessment based on Landsat 8 red, nearinfrared, and thermal channels Mohammad Reza Mobasheri Meisam Amani

Mohammad Reza Mobasheri, Meisam Amani, “Soil moisture content assessment based on Landsat 8 red, near-infrared, and thermal channels,” J. Appl. Remote Sens. 10(2), 026011 (2016), doi: 10.1117/1. JRS.10.026011.

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Soil moisture content assessment based on Landsat 8 red, near-infrared, and thermal channels a

Mohammad Reza Mobasheria,* and Meisam Amanib K.N. Toosi University of Technology, Photogrammetry and Remote Sensing Department, Faculty of Geodesy and Geomatics Engineering, Tehran 19697-15433, Iran b Memorial University of Newfoundland, C-CORE, St. John’s, Newfoundland, A1B 3X5, Canada

Abstract. Soil moisture content (SMC) plays an important role in different environmental. In this study, four different soil moisture indices, namely, SOMID, SOMID-FS, SOMID-FT, and CSOMID-FT, were introduced. In this work, the following parameters were used to estimate SMC at a depth of 5 cm: (a) the distance of pixels from the origin in the scatter-plot of near-infrared (NIR) and red bands (SNIR-R), (b) the fraction of soil cover in each pixel, and (c) the land surface temperature. It was concluded that the CSOMID-FT was the most accurate index for estimation of SMC (RMSE ¼ 0.045, R ¼ 0.92). This index divides the SNIR-R into three separate regions based on the pixels’ normalized difference vegetation index (NDVI) values and assigns a specific regression equation to each region. The results showed that as the NDVI values increase, the accuracy of the proposed indices decreases. Furthermore, the SOMID-FT and CSOMID-FT were used to estimate SMC at five different depths of 5, 10, 20, 50, and 100 cm. It was concluded that the satellite-estimated SMC was highly correlated with the field-measured data at 5-cm soil depth. © 2016 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/1.JRS.10.026011]

Keywords: remote sensing; soil moisture; land surface temperature; red; near-infrared; Landsat 8. Paper 15761 received Nov. 13, 2015; accepted for publication Mar. 7, 2016; published online Apr. 26, 2016.

1 Introduction Soil moisture content (SMC) is a critical parameter in many environmental studies, such as drought monitoring,1,2 water-budgeting processes and irrigation scheduling,3,4 runoff and soil erosion,5,6 evapotranspiration,7,8 and forest ﬁre hazard and forest management.9,10 Field measurements of SMC provide the most accurate values; however, they are very expensive, time consuming, and only valid for limited areas.11 On the other hand, remote sensing (RS) has the ability to collect information over a large area in short and repeated time intervals. So far, RS research has introduced various methods for SMC assessment, in which the visible, infrared, thermal, and microwave bands are deployed. Generally, these methods can be classified into three main categories: active RS, passive RS, and hybrid methods based on a combination of active and passive RS data.11 Each of these methods has its own advantages and limitations. Active RS methods for SMC estimation are mainly dependent on the relationship between SMC, dielectric characteristics of a specific target, and radar backscatters. Synthetic aperture radar (SAR), which is an active remote sensor, is the most widely used technique for estimation of SMC. The privilege of the SAR is its ability to acquire data under almost any meteorological conditions and without an external source of illumination. Active RS gives more accurate results in bare soil areas compared to the other methods.11 However, this technique is not effective when dealing with the retrieval of SMC data over vegetated surfaces. This is mainly due to the surface roughness in vegetation cover.12 Generally, under the same land cover conditions as SMC in the soil surface decreases, the radar backscattering coefficient will increase.11 There are mainly three ∗

Address all correspondence to: Mohammad Reza Mobasheri, E-mail: [email protected]

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Mobasheri and Amani: Soil moisture content assessment. . .

groups of models that use active RS data for SMC estimation: backscattering models, such as the integral equation model13 and Oh et al.14; statistical analysis techniques; and neural network methods. Passive RS methods usually apply the visible, near-infrared (NIR), shortwave infrared (SWIR), and thermal data for SMC modeling. Different researchers have used different vegetation indices to assess SMC.3,15 In this regard, several studies have reported that the normalized difference vegetation index (NDVI) had a noticeable utility for estimating SMC.16,17 High correlation has been identified between the annual or monthly time integrated NDVI and droughtrelated climate factors, such as precipitation.18,19 Thermal RS approaches used for vegetation studies and SMC estimation are categorized into three groups: the thermal inertia method, the vegetation evapotranspiration method, and the crop water stress index. These methods are established using the relationship between the surface emissivity, temperature, and SMC, by mainly taking advantage of the principles of water circulation and energy balance.20 Passive RS retrieves SMC independently even when there is a vegetation canopy available.21 However, it should be noted that the impact of surface roughness, soil structure, and organic matters on the reflectance of visible and NIR data is the main limitation in optical RS methods for SMC estimation.11 The main passive RS methods for SMC estimation are the universal triangular relationship method,22,23 brightness models,24 statistical analysis techniques, and neural networks. Integrating both active and passive RS can empower the accurate assessment of SMC. The most widely used methods in which active and passive data are combined are microwave combined algorithms,25 statistical analysis techniques, and neural network methods. Effective soil depth for SMC measurements in RS methods has been a controversial issue. Chauhan et al.,25 Finn et al.,26 and Dunne et al.27 reported that RS methods have been relatively successful in measuring SMC at a depth of 5 cm from the top soil surface in bare soil or in soil with less vegetation cover. Liu et al.28 found that the effective soil depth for SMC estimation by visible and NIR data was 10 cm. Guo et al.29 examined the relationship between satellite-derived NDVI, brightness temperature, and SMC. They reported that the satellite data have a strong correlation with SMC at 20-cm soil depth. In the next section, a brief description of soil spectral characteristics, the scatter-plot of NIR and red bands (SNIR-R), and land surface temperature (LST), as well as their applications in SMC estimation, is provided.

1.1 Soil Spectral Properties Generally, the spectral reflectance of soil is controlled by six variables: moisture content, organic matter, particle size distribution, iron oxide content, soil mineralogy, and soil structure.30 Among these variables, moisture content is the most important of all because of its dynamic nature and its overall impact on the soil reflectance. Generally, soil reflectance increases from the visible to the SWIR. The water absorption bands occur around 1.4 and 1.9 μm. SMC creates a group of parallel curves in the reflectance spectra.31 When the NIR against the red reflectance values is plotted for pixels fully covered by bare soil with different amounts of moisture, it is seen that the points are distributed around a certain line called the soil line (Fig. 1). This line is characterized by Eq. (1): ρNIR ¼ γρred þ b;

(1)

EQ-TARGET;temp:intralink-;e001;116;207

where b, γ, ρNIR , and ρred are the intercept, slope, NIR, and red reflectance values, respectively.

1.2 Scatter-Plot of Near-Infrared and Red Bands In the case where the scene contains both soil and vegetation, the points scatter inside a triangular region in the SNIR-R, as shown in Fig. 1. Depending on the amount of vegetation, soil, SMC, vegetation species, and even the plant growth stage in each pixel, its corresponding position in the SNIR-R varies. Pixels with high NIR and low red reflectance values are populated around the upper vertex of the triangle and represent dense vegetation cover. The base of this triangle Journal of Applied Remote Sensing

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Fig. 1 Distribution of reflectance values in the SNIR-R.

represents the soil line connecting water-saturated soil (the lower left vertex) to dry soil (the upper right vertex).32 This scatter-plot has been used extensively to assess SMC and dryness. Zhan et al.33 established a new simple model called soil moisture monitoring by RS based on the distribution characteristics of SMC in the SNIR-R. However, in their work, the vegetation interference on SMC assessment has not been taken into account. Therefore, the model suffers from mixed information of soil and vegetation. In another work, Ghulam et al.1 developed an improved drought monitoring method called the modified perpendicular drought index in which the soil moisture and vegetation growing stage in the SNIR-R has been considered. Additional studies about the SNIR-R characterized with spectral behavior of vegetation and SMC can be found in the studies of Refs. 34–36.

1.3 Land Surface Temperature/Vegetation Index The LST is considered as a critical input parameter for SMC retrievals. In most studies, researchers have applied both the LST and vegetation information to estimate SMC.37,38 The application of the LST/VI concept for SMC estimation began with the research of Nemani et al.,38 who found a strong negative relationship between the LST and NDVI for all biomass types with a distinct change in the slope between dry and wet days. This idea was further developed by Carlson et al.,22 presenting the universal triangular method to explore the relationship between SMC, LST, and NDVI. Since then, a number of studies have suggested the application of the LST/VI concept for SMC estimation (e.g., Refs. 39 and 40). In this study, the position of each pixel in a three-dimensional space of LST, red, and NIR bands was used to retrieve SMC.

2 Data Preparation 2.1 Study Area and In Situ Data The in situ soil moisture data used in this study are those collected by the soil climate analysis network (SCAN). These data are downloaded from Ref. 41. SCAN focuses on agricultural areas in the United States and currently consists of more than 220 stations. SCAN stations are equipped with multitude sensors for measuring several parameters, such as air temperature, relative humidity, soil moisture at different depths, soil temperature at different depths, solar Journal of Applied Remote Sensing

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Fig. 2 SCAN sites locations throughout the United States. Circular points represent the stations’ locations. Zoomed image shows the locations of the 25 SCAN sites used in this study.

radiation, wind speed, and precipitation. The SCAN soil moisture data are measured hourly at the depths of 5, 10, 20, 50, and 100 cm. These hourly measurements are aggregated to a daily mean value. The sensor used for this purpose is Hydra Probe. This sensor sends an electromagnetic signal (50 MHz) into the soil in which the reflected wave is associated with the electrical properties of the soil and is used to determine SMC, conductivity, and salinity. The output of this sensor is the percentage of volumetric SMC (unit: %, cm3 ∕cm3 ). Figure 2 shows the locations of current SCAN sites throughout the United States and those used in this study. In this study, the hourly field volumetric soil moisture data from 25 different sites with various land cover were downloaded and used. The information regarding these 25 sites and the number of SMC measured data adopted in this research are given in Table 1. We tried to use those data collected close to the time of images’ acquisition. In general, the difference between the time of each image acquisition and the time of field data collection was less than half an hour.

2.2 Image Preparation To prove the applicability of the proposed soil moisture indices, it was important to show that these indices work well in different ecosystems, as well as in different times of a year. A valuable validation should include several images from different dates containing various amounts of SMC. To achieve this goal, 32 Landsat 8 images of level 1T products are used. These images were acquired between April 29, 2013, and September 16, 2014, and were downloaded from Ref. 42. The Landsat 8 L1T products are geometrically corrected with ∼12-m circular error and 90% confidence global accuracy. The Landsat 8 images consist of 11 spectral bands with a Journal of Applied Remote Sensing

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Table 1 Information about 25 SCAN sites used in this study. Station name

Location

(Lat, Lon)

Number of SMC data

Onward

Mississippi, Sharkey

(32.75, −90.93)

7

Mayday

Mississippi, Yazoo

(32.86, −90.52)

6

North Issaquena

Mississippi, Issaquena

(33, −91.06)

7

Beasley Lake

Mississippi, Sunflower

(33.38, −90.65)

7

Silver City

Mississippi, Humphreys

(33.09, −90.51)

5

Sandy Ridge

Mississippi, Sunflower

(33.66, −90.57)

6

Scott

Mississippi, Bolivar

(33.62, −91.1)

8

Perthshire

Mississippi, Bolivar

(33.97, −90.9)

8

Isbell Farms

Alabama, Colbert

(34.82, −87.99)

6

WTARS

Alabama, Madison

(34.9, −86.53)

9

Hartselle USDA

Alabama, Morgan

(34.43, −87)

9

Stanley Farm

Alabama, Morgan

(34.43, −86.68)

8

Allen Farms

Tennessee, Giles

(35.07, −86.89)

8

Sudduth Farms

Alabama, Winston

(34.18, −87.46)

7

Bragg Farm

Alabama, Madison

(34.89, −86.6)

9

Cullman-NAHRC

Alabama, Cullman

(34.19, −86.8)

8

AAMU-JTG

Alabama, Madison

(34.78, −86.55)

2

McAlister Farm

Tennessee, Lincoln

(35.06, −86.59)

9

Vance

Mississippi, Tallahatchie

(34.07, −90.35)

6

Uapb-Dewitt

Arkansas, Arkansas

(34.28, −91.35)

7

Uapb-Earle

Arkansas, Crittenden

(35.28, −90.45)

5

Uapb-Marianna

Arkansas, Lee

(34.78, −90.82)

5

Tunica

Mississippi, Tunica

(34.68, −90.42)

5

Goodwin Ck Pasture

Mississippi, Panola

(34.25, −89.87)

7

Goodwin Ck Timber

Mississippi, Panola

(34.23, −89.9)

7 Total: 171

spatial resolution of 30 m in bands 1 to 7 and 9, 15 m in band 8 (panchromatic), and 100 m in the thermal bands (bands 10 and 11), where the latter used for LST estimation.43 Radiometric correction for the red and NIR bands was carried out by ENVI/FLAASH software.44 The required parameters for atmospheric correction were obtained from the images’ metadata, as well as the data collected at the nearest weather stations to each site. Regarding radiometric correction of the thermal bands, the radiative transfer equation-based method45 was used. Reference 45 has provided an atmospheric correction tool, which is on a public access website, for the thermal bands of Landsat 5, Landsat 7, and Landsat 8.46 For Landsat 8 data, this applies only to band 10 for estimation of LST in the pixels. In this study, the digital numbers’ values were converted first into the top-of-atmosphere (TOA) radiance. Then, using the web-based method proposed by Ref. 45, atmospheric transmission, upwelling, and downwelling radiances for each pixel were derived. Using this information, and applying Eq. (2), the flux density in each pixel with kinetic temperature T (LT ) was derived. Journal of Applied Remote Sensing

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LTOA ¼ τεLT þ Lu þ τð1 − εÞLd ;

(2)


where τ is the atmospheric transmission, ε is the emissivity of the surface, LT is the radiance of a blackbody target with the kinetic temperature of T, Lu is the upwelling or atmospheric path flux density, Ld is the downwelling or sky flux density, and LTOA is the space-reaching or TOA flux density measured by the instrument. Radiances are in units of w∕ðm2 :sr:μmÞ, and the transmission and emissivity are unitless. To estimate the emissivity, the method proposed by Ref. 47, which is based on a linear equation between the NDVI and emissivity, was applied [Eq. (3)]: 8 > NDVI < −0.185∶emissivity ¼ 0.995; > > < −0.185 ≤ NDVI < 0.157∶emissivity ¼ 0.97; (3) 0.157 ≤ NDVI < 0.727∶emissivity ¼ 1.0098 þ 0.047 × LnðNDVIÞ; > > > : NDVI ≥ 0.727∶emissivity ¼ 0.99: EQ-TARGET;temp:intralink-;e003;116;630

Finally, using the Planck equation [Eq. (4)], the surface temperature was calculated using surface-leaving radiance (LT ). T¼


K2

Ln 1 þ KLT1

;

(4)

where T is the temperature in Kelvin, LT is the spectral flux density in w∕ðm2 :sr:μmÞ, and K 1 and K 2 are Planck constants (K 1 ¼ 774.89, K 2 ¼ 1321.08).

3 Methodology As described in Sec. 1.1, the reflectance values in both red and NIR bands decrease as the amount of SMC increases. In fact, regardless of vegetation cover in the SNIR-R, as the amount of SMC decreases, the position of each pixel gradually moves away from the origin in the scatter-plot. Therefore, a simple soil moisture index based on the distance from the origin of the SNIR-R (named the SOMID) can be defined using the linear regression technique [Eq. (5)]: SOMID ¼ a1 × D þ a2 ;

(5)


where a1 and a2 are the slope and offset in the regression equation, and D represents the distance of each pixel from the origin in the SNIR-R. The SOMID may only give accurate results where there is lack of vegetation or a low amount of vegetation cover in a pixel. This situation can be seen in the regions near the soil line in the SNIR-R, where there is sparse vegetation cover (Fig. 1). However, as the position of a pixel moves away from the soil line toward the upper vertex of the triangle, the amount of vegetation cover increases, and hence poses some uncertainties in SMC estimation. In these regions, the emitted or reflected radiation from a pixel will no longer represent the actual soil surface emission, because part of the emitted/reflected radiation might be either absorbed or intensified by the soil cover type. Therefore, the second index for estimation of SMC was developed, which takes into account the amount of vegetation cover. In this index, the fraction of soil cover in each pixel was calculated through an unmixing process using Eq. (6) and then was added to the SOMID as a modification parameter: 8 < ρred ¼ Fs :ρsred þ Fv :ρvred ρ ¼ Fs :ρsNIR þ Fv :ρvNIR ; : NIR Fs þ Fv ¼ 1 and Fs ≥ 0; Fv ≥ 0

(6)


where ρred and ρNIR are the reflectance values in the red and NIR bands, respectively; Fs and Fv are, respectively, the fraction of soil cover and vegetation cover in each pixel; and Rsred , Rvred , ρsNIR , and ρvNIR are the reflectance values for bare soil and full covered vegetation pixels in the red and Journal of Applied Remote Sensing

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NIR bands, respectively. In this research, these four values were determined by finding the fully vegetated and bare soil pixels in the time series of satellite images. We named this index the SOMID based on distance and fraction of soil cover (SOMID-FS) [Eq. (7)]:

SOMID-FS ¼ b1 × D þ b2 × Fs þ b3 ;

(7)


where b1 , b2 , and b3 are constants to be determined using known in situ measured data. As described in Sec. 1.3, using both vegetation information and LST increases the accuracy of SMC estimation, as LST and vegetation have a complicated dependence on SMC. Therefore, the accuracy of SOMID-FS was improved by including a term related to the LST to the regression equation. The resulted index was named the SOMID based on distance, fraction of soil cover, and soil temperature (SOMID-FT), as given in Eq. (8):

SOMID-FT ¼ c1 × D þ c2 × Fs þ c3 × LST þ c4 ;

(8)


where c1 , c2 , c3 , and c4 are constants to be determined using known in situ measured data. Finally, it is possible to define a more accurate model for retrieving SMC by partitioning the SNIR-R and fitting a particular regression equation to the data in each region. For this, in Sec. 4.4, the available in situ data are divided into three different regions based on the pixels’ NDVI values, and then the classified soil moisture index using three parameters of D, Fs , and LST (CSOMID-FT) is introduced. As explained above, the least square method was applied to Eqs. (5), (7), and (8) using assigned training data, and the coefficients (ai ; bi ; ci ) for each equation were calculated. Finally, each index was applied to the test data to evaluate its performance in SMC estimation. The soil line used in all above-mentioned indices is the one introduced by Amani and Mobasheri.36

4 Results and Discussion About 75% of all field SMC data (129 sample points) was used randomly for modeling, and the rest (42 sample points) was used for evaluation. To evaluate the accuracy of the suggested SMC indices, the correlation coefficient (R), root mean square error (RMSE), and percentage of relRMSE × 100) were used. The outputs of the indices were ative RMSE (i.e., RRMSE ¼ averageðSMMeasuredÞ compared with the field measured data. Then, for high values of R and low values of RMSE and RRMSE, the models were considered as acceptable. In Secs. 4.1, 4.2, 4.3, and 4.4, the in situ SMC data at the depth of 5 cm are used, and in Sec. 4.5, the assessment of SMC at five different depths using the SOMID-FT and CSOMID-FT is discussed.

4.1 Soil Moisture Index Based on Distance As described before, there is almost a negative correlation between SMC and reflectance values in the red and NIR bands. It generally shows that as reflectance values in the red and NIR bands increase, the SMC decreases. However, this for highly vegetated pixels cannot be confirmed, because the soil is not exposed to the sensor. If one plots the distance of each point from the origin in the SNIR-R (D) against in situ measured SMC, a negative relation will be deduced, as shown in Fig. 3(a). Figure 3(b) shows the results and the corresponding statistical parameters for the SOMID [Eq. (5)]. As can be seen in this figure, there is not a strong 1:1 relation between the SOMID values and measured SMC. Journal of Applied Remote Sensing

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Fig. 3 (a) Relationship between the distance from the origin of the NIR–red spectral space (D) and in situ SMC data. (b) Field soil moisture measured data versus SOMID predicted values (diagonal is 1:1).

4.2 Soil Moisture Content Based on Distance and Fraction of Soil Cover As described in Sec. 3, when a pixel is a mixture of vegetation and bare soil, the estimation of SMC faces several limitations. This gets more complicated when the vegetation cover is denser. In this section, we try to deal with this kind of pixels. As can be seen in Fig. 4(a), there is a negative correlation between the fraction of soil cover (FS ) and field SMC data. To build up SOMID-FS, using Eq. (6), the fraction of soil in each pixel was calculated, and then using Eq. (7), a linear regression equation was fitted to the field SMC data [Eq. (7)]. To evaluate the SOMID-FS, it was applied to the test data, where the result is shown in Fig. 4(b). The SOMID-FS showed a correlation of 0.7 and a RMSE of 0.075 compared to the field data.

4.3 Soil Moisture Content Based on Distance, Fraction of Soil Cover, and Soil Temperature As described in Sec. 1.3, the LST is an important parameter for SMC estimation. Our investigation showed that there was a negative correlation between SMC in each pixel and the pixels’ temperature values obtained through Eqs. (2)–(4). The results are shown in Fig. 5(a). To incorporate the LST into SMC estimation index [as described in Eq. (8)], a regression equation was Journal of Applied Remote Sensing

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Fig. 4 (a) Relationship between F s and in situ SMC data. (b) SMC measured data versus predicted values by SOMID-FS (diagonal is 1:1).

calculated using the LST, D, and Fs , and then SOMID-FT was developed [Eq. (8)]. To evaluate this index, it was applied to the test data, and then the results were compared with the field SMC data [Fig. 5(b)]. From the results, it is evident that by including the LST, the performance of the soil moisture index is considerably improved. By including the LST, the RMSE dropped from 0.075 in the previous index to 0.05 in the SOMID-FS, while the R increased from 0.7 to 0.87.

4.4 Classified Soil Moisture Index Since the task of estimating SMC becomes more difficult as the pixel gets more vegetated, we tried to introduce an approach to defining a more accurate soil moisture index by partitioning the triangle in the SNIR-R into three different regions of “no or very low vegetation” (NDVI < 0.2), intermediate vegetation (0.2 < NDVI < 0.4), and dense vegetation (NDVI > 0.4), as shown in Figs. 1 and 6. In fact, we intended to show that by dividing the data in terms of their NDVI values and defining different regression equations for different regions, we could improve the results. For this, the available field data were divided into three regions based on the pixels’ NDVI values


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Fig. 5 (a) Negative correlation between the LST and in situ measured SMC data. (b) Observed SMC versus SOMID-FT predicted SMC values (diagonal is 1:1).

(NDVI < 0.2, 0.2 < NDVI < 0.4, and NDVI > 0.4, as shown in Figs. 1 and 6). Then a different SOMID-FT was assigned to each region. The results are shown in Fig. 6. Analysis of Fig. 6 showed that as the amount of vegetation decreases (lower NDVI), the performance of the index in estimation of SMC increases. Figure 7 shows the whole results of the estimated SMC by the CSOMID-FT compared to the field data. According to the figure, and based on the results, it was concluded that the CSOMIDFT had increased the estimation accuracy. Compared to the SOMID-FT, the overall RMSE decreased from 0.05 to 0.045, overall R increased from 0.87 to 0.92, and overall RRMSE decreased from 17% to 14%.

4.5 Assessing Soil Moisture Content at Five Different Depths In this section, the ability of the SOMID-FT and CSOMID-FT for estimation of SMC at five different depths of 5, 10, 20, 50, and 100 cm is assessed. Table 2 shows the RMSE, RRMSE, and R between predicted and field collected SMC data at different depths. As can be seen in Table 2, both the SOMID-FT and CSOMID-FT calculated SMC at 5-cm depth had better correlation with those collected in the field. However, moving to the lower Journal of Applied Remote Sensing

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Fig. 6 Results of CSOMID-FT in three different regions (a, b, and c) in the SNIR-R (diagonal is 1:1).

Fig. 7 Calculated SMC values by CSOMID-FT against in situ measured data (diagonal is 1:1). Journal of Applied Remote Sensing

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Table 2 Correlation between the SOMID-FT and CSOMID-FT estimated SMC and those measured at five different depths. Model SOMID-FT

CSOMID-FT

Parameters

5 cm

10 cm

20 cm

50 cm

100 cm

RMSE

0.05

0.062

0.057

0.066

0.066

RRMSE

17%

27%

24%

30%

30%

R

0.87

0.56

0.62

0.34

0.38

RMSE

0.045

0.054

0.054

0.071

0.064

RRMSE

14%

17%

17%

35%

29%

R

0.92

0.82

0.79

0.48

0.45

layers, the correlation decreases gradually. Furthermore, it was concluded that the CSOMID-FT had better accuracy compared to the SOMID-FT at all depths.

5 Conclusion A large number of researchers have developed a variety of methods for retrieving SMC using different types of RS data, including optical and radar data. In most studies, both vegetation indices and LST are widely used to estimate SMC. In this study, using the red and NIR reflectance values along with the LST, four different soil moisture indices were proposed. First, a simple index using the distance of pixels from the origin in the SNIR-R (SOMID) was proposed, where the results were not satisfactory. Then, the SOMID was modified by adding the fraction of soil cover in each pixel (SOMID-FS), and including the LST (SOMID-FT). It was concluded that the assessment of SMC gets more difficult when the area is covered by vegetation. The SOMIDFS improved the performance of the SOMID by taking into account the fraction of soil cover in each pixel, which caused RMSE to drop from 0.09 to 0.075, and R increased from 0.56 to 0.7. Many studies have proved that using thermal bands is a promising approach for SMC estimation. Therefore, the SOMID-FT was developed by incorporating the LST in the SOMID-FS. The SOMID-FT showed a high performance in estimating of SMC (RMSE ¼ 0.05, R ¼ 0.87). Finally, the performance of the SOMID-FT was improved by partitioning the in situ measured data into three different regions based on their NDVI values. Then a particular SOMID-FT was fitted to the data in each region. The output index was named the CSOMID-FT. In general, using the CSOMID-FT resulted in a slight improvement in the SMC assessment. Analysis of the results showed that estimation of SMC for the pixels with dense vegetation cover was not satisfactory. However, as the NDVI values decrease, the accuracy of the prediction increases. In the last part of this study, the SOMID-FT and CSOMID-FT were validated by comparison with the field measured SMC data at five different depths. The results showed that the satellite estimated SMC had high correlation with the field measured data at 5-cm soil depth.

Acknowledgments These authors would like to express their gratitude and respects to the SCAN administration and campaign members for their valuable efforts in field collecting data, without which this research was not possible.

References 1. A. Ghulam et al., “Modified perpendicular drought index (MPDI): a real-time drought monitoring method,” ISPRS J. Photogramm. Remote Sens. 62(2), 150–164 (2007). 2. B. R. Parida et al., “Detecting drought-prone areas of rice agriculture using a MODISderived soil moisture index,” GISci. Remote Sens. 45(1), 109–129 (2008). Journal of Applied Remote Sensing

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27. S. C. Dunne, D. Entekhabi, and E. G. Njoku, “Impact of multiresolution active and passive microwave measurements on soil moisture estimation using the ensemble Kalman smoother,” IEEE Trans. Geosci. Remote Sens. 45(4), 1016–1028 (2007). 28. P. J. Liu et al., “A method for monitoring soil water contents using satellite remote sensing,” J. Remote Sens. 1(2), 135–138 (1997). 29. N. Guo et al., “Estimating farmland soil moisture in eastern Gansu province using NOAA satellite data,” Quart. J. Appl. Meteorol. 8(2), 212–218 (1997). 30. A. Ångström, “The albedo of various surfaces of ground,” JSTOR 7, 323–342 (1925). 31. S. A. Bowers and R. J. Hanks, “Reflection of radiant energy from soils,” Soil Sci. 100(2), 130–138 (1965). 32. J. R. Jensen, Remote Sensing of the Environment: An Earth Resource Perspective 2/e. Pearson Prentice Hall, Upper Saddle River, New Jersey (2009). 33. Z. Zhan et al., “NIR-red spectral space based new method for soil moisture monitoring,” Sci. China Ser. D 50(2), 283–289 (2007). 34. A. J. Richardson and C. L. Weigand, “Distinguishing vegetation from soil background information,” Photogramm. Eng. Remote Sens. 43(12), 1541–1552 (1977). 35. R. D. Jackson, “Spectral indices in n-space,” Remote Sens. Environ. 13(5), 409–421 (1983). 36. M. Amani and M. R. Mobasheri, “A parametric method for estimation of leaf area index using landsat ETM+ data,” GISci. Remote Sens. 52(4), 478–497 (2015). 37. S. J. Goetz, “Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site,” Int. J. Remote Sens. 18(1), 71–94 (1997). 38. R. Nemani et al., “Developing satellite-derived estimates of surface moisture status,” J. Appl. Meteorol. 32(3), 548–557 (1993). 39. W. Wang et al., “Estimation of soil moisture using trapezoidal relationship between remotely sensed land surface temperature and vegetation index,” Hydrol. Earth Syst. Sci. 15(5), 1699–1712 (2011). 40. I. Sandholt, K. Rasmussen, and J. Andersen, “A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status,” Remote Sens. Environ. 79(2), 213–224 (2002). 41. http://www.wcc.nrcs.usda.gov/scan/ 42. http://www.earthexplorer.usgs.gov 43. http://landsat.usgs.gov/landsat8.php 44. http://www.exelisvis.com/ 45. J. A. Barsi et al., “Validation of a web-based atmospheric correction tool for single thermal band instruments,” Proc. SPIE 5882, 58820E (2005). 46. http://atmcorr.gsfc.nasa.gov/ 47. A. A. Van de Griend and M. Owe, “On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces,” Int. J. Remote Sens. 14(6), 1119–1131 (1993). Mohammad Reza Mobasheri is an associate professor and currently a senior lecturer/ researcher at K.N. Toosi University of Technology, Tehran, Iran. He received his BSc degree in physics from Tehran University in 1975; his MSc degree in electromagnetics from Toledo University, Ohio, United States, in 1980; and his PhD from James Cook University, Australia, in 1995. He has published more than 150 papers, all in remote sensing, and has carried out more than 12 research projects funded by different bodies. Meisam Amani is a PhD student at Memorial University of Newfoundland. He received his BSc degree in geomatics engineering from the University of Tabriz in 2012 and his MSc degree in remote sensing from Khaje Nasir Toosi University of Technology in 2014. His current research interests include soil moisture estimation and drought monitoring using optical remote sensing. His PhD thesis is also about wetland mapping using a combination of optical and polarimetric SAR images.


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