The Potential Use of Multi-Band SAR Data for Soil Moisture Retrieval over Bare Agricultural Areas: Hebei, China

Zhang, Xiang; Chen, Baozhang; Fan, Hongdong; Huang, Jianhui; Hui, Zhao

doi:10.3390/rs8010007

Cited by 32 publications

(23 citation statements)

References 57 publications

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“…In the last few years, there have also been other studies in the field of geo-/bio-physical parameter retrieval based on recent machine learning techniques, such as support vector regression (SVR) [35]. In this connection, different studies (e.g., [36][37][38]) have investigated the potential of a SVR model for soil moisture inversion using remote sensing data. Thus, an improved performance of the SVR algorithm (with RMSE = 1.98%), when compared to ANN (RMSE = 2.79%) and the conventional multiple linear regression approaches (RMSE = 2.84%) was achieved by Ahmad et al [36].In a different approach, stepwise-cluster analysis (SCA) is an alternative statistical method intended for modeling the nonlinear relationships between independent and dependent variables [39].…”

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confidence: 99%

Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

et al. 2019

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In this study, a residual soil moisture prediction model was developed using the stepwise cluster analysis (SCA) and model prediction approach in the Upper Blue Nile basin. The SCA has the advantage of capturing the nonlinear relationships between remote sensing variables and volumetric soil moisture. The principle of SCA is to generate a set of prediction cluster trees based on a series of cutting and merging process according to a given statistical criterion. The proposed model incorporates the combinations of dual-polarized Sentinel-1 SAR data, normalized difference vegetation index (NDVI), and digital elevation model as input parameters. In this regard, two separate stepwise cluster models were developed using volumetric soil moisture obtained from automatic weather stations (AWS) and Noah model simulation as response variables. The performance of the SCA models have been verified for different significance levels (i.e., α = 0.01 , α = 0.05 , and α = 0.1 ). Thus, the AWS based SCA model with α = 0.05 was found to be an optimal model for predicting volumetric residual soil moisture, with correlation coefficient (r) values of 0. 95 and 0.87 and root mean square error (RMSE) of 0.032 and 0.097 m3/m3 during the training and testing periods, respectively. While in the case of the Noah SCA model an optimal prediction performance was observed when α value was set to 0.01, with r being 0.93 and 0.87 and RMSE of 0.043 and 0.058 m3/m3 using the training and testing datasets, respectively. In addition, our result indicated that the combined use of Sentinel-SAR data and ancillary remote sensing products such as NDVI could allow for better soil moisture prediction. Compared to the support vector regression (SVR) method, SCA shows better fitting and prediction accuracy of soil moisture. Generally, this study asserts that the SCA can be used as an alternative method for remote sensing based soil moisture predictions.

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confidence: 99%

Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

et al. 2019

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“…Thus, significant work has been done towards the application of active microwave sensors to monitoring soil surface moisture content [3]. Among the active microwave sensors, the Synthetic Aperture Radar (SAR) sensor plays an important role in agricultural monitoring, especially in plant growth, yield, mapping, and soil moisture estimation [4]. With the aid of polarimetric SAR, far better information can be derived than with single polarized SAR.…”

Section: Introductionmentioning

confidence: 99%

“…The inversion models withstand three basic approaches in the literature, including the empirical/semi-empirical model [12][13][14], the theoretical model [10,15], and the machine learning model [4,[16][17][18][19][20]. In the first approach, the empirical/semi empirical models are based on the scattering attitude of experimental measurements [16] and build a basic relationship between soil surface features and backscattering coefficients reflected from the target point [4]. Among the empirical models, Oh [12] and Dubois [13] are the most used inversion models.…”

Section: Introductionmentioning

confidence: 99%

“…Among the machine learning models, Support Vector Regression (SVR) [4,6], Bayes Theorem [21], and Artificial Neural Network (ANN) [21][22][23][24][25] are commonly used inversion techniques for soil moisture retrieval. In the literature, a number of studies have been done using machine-learning-based inversion models.…”

Section: Introductionmentioning

confidence: 99%

“…The results indicated that the estimated data of the three methods were very close to the measured data and the accuracy of ANN was slightly higher than the other methods. Zhang et al [4] investigated TerraSAR-X and Radarsat-2 data for soil moisture retrieval over bare agricultural areas using both statistical (SVR) and semi-empirical (Modified Dubois) approaches. Their results indicated that the TerraSAR-X and Radarsat-2 were proper remote sensing tools for soil moisture estimation with a low RMSE value.…”

Section: Introductionmentioning

confidence: 99%

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Soil Moisture Estimation over Vegetated Agricultural Areas: Tigris Basin, Turkey from Radarsat-2 Data by Polarimetric Decomposition Models and a Generalized Regression Neural Network

2017

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Determining the soil moisture in agricultural fields is a significant parameter to use irrigation systems efficiently. In contrast to standard soil moisture measurements, good results might be acquired in a shorter time over large areas by remote sensing tools. In order to estimate the soil moisture over vegetated agricultural areas, a relationship between Radarsat-2 data and measured ground soil moistures was established by polarimetric decomposition models and a generalized regression neural network (GRNN). The experiments were executed over two agricultural sites on the Tigris Basin, Turkey. The study consists of four phases. In the first stage, Radarsat-2 data were acquired on different dates and in situ measurements were implemented simultaneously. In the second phase, the Radarsat-2 data were pre-processed and the GPS coordinates of the soil sample points were imported to this data. Then the standard sigma backscattering coefficients with the Freeman-Durden and H/A/α polarimetric decomposition models were employed for feature extraction and a feature vector with four sigma backscattering coefficients (σhh, σhv, σvh, and σvv) and six polarimetric decomposition parameters (entropy, anisotropy, alpha angle, volume scattering, odd bounce, and double bounce) were generated for each pattern. In the last stage, GRNN was used to estimate the regional soil moisture with the aid of feature vectors. The results indicated that radar is a strong remote sensing tool for soil moisture estimation, with mean absolute errors around

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Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district, Haryana (India)

et al. 2018

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We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (σ o RH), differences of circular vertical and horizontal σ o (σ o RV −σ o RH) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height (RMS height). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., σ o. Near surface SM measurements were related to σ o RH , σ o RV −σ o RH derived using 5.35 GHz (C-band) image of RISAT-1 and RMS height. The roughness component derived in terms of RMS height showed a good positive correlation with σ o RV −σ o RH (R 2 = 0.65). By considering all the major influencing factors (σ o RH , σ o RV −σ o RH , and RMS height), an SEM was developed where SM (volumetric) predicted values depend on σ o RH , σ o RV −σ o RH , and RMS height. This SEM showed R 2 of 0.87 and adjusted R 2 of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement (SM Observed) showed root mean square error (RMSE) = 0.06, relative-RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash-Sutcliffe efficiency (NSE) = 0.91 (≈1), index of agreement (d) = 1, coefficient of determination (R 2) = 0.87, mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences (S 2 d) = 0.004. The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on σ o. By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.

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The Potential Use of Multi-Band SAR Data for Soil Moisture Retrieval over Bare Agricultural Areas: Hebei, China

Cited by 32 publications

References 57 publications

Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

Soil Moisture Estimation over Vegetated Agricultural Areas: Tigris Basin, Turkey from Radarsat-2 Data by Polarimetric Decomposition Models and a Generalized Regression Neural Network

Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district, Haryana (India)

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