Soil Moisture Retrieval over a Vegetation-Covered Area Using ALOS-2 L-Band Synthetic Aperture Radar Data

Abstract

Soil moisture (SM) plays a significant part in regional hydrological and meteorological systems throughout Earth. It is considered an indispensable state variable in earth science. The high sensitivity of microwave remote sensing to soil moisture, and its ability to function under all weather conditions at all hours of the day, has led to its wide application in SM retrieval. The aim of this study is to evaluate the ability of ALOS-2 data to estimate SM in areas with high vegetation coverage. Through the water cloud model (WCM), the article simulates the scene coupling between active microwave images and optical data. Subsequently, we use a genetic algorithm to optimize back propagation (GA-BP) neural network technology to retrieve SM. The vegetation descriptors of the WCM, derived from optical images, were the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the normalized multi-band drought index (NMDI). In the vegetation-covered area, 240 field soil samples were collected simultaneously with the ALOS-2 SAR overpass. Soil samples at two depths (0–10 cm, 20–30 cm) were collected at each sampling site. The backscattering of the ALOS-2 with the copolarization was found to be more sensitive to SM than the crosspolarization. In addition, the sensitivity of the soil backscattering coefficient to SM at a depth of 0–10 cm was higher than at a depth of 20–30 cm. At a 0–10 cm depth, the best results were the mean square error (MAE) of 2.248 vol%, the root mean square error (RMSE) of 3.146 vol%, and the mean absolute percentage error (MAPE) of 0.056 vol%, when the vegetation is described as by the NDVI. At a 20–30 cm depth, the best results were an MAE of 2.333 vol%, an RMSE of 2.882 vol%, a MAPE of 0.067 vol%, with the NMDI as the vegetation description. The use of the GA-BP NNs method for SM inversion presented in this paper is novel. Moreover, the results revealed that ALOS-2 data is a valuable source for SM estimation, and ALOS-2 L-band data was sensitive to SM even under vegetation cover.

Bias-Corrected RADARSAT-2 Soil Moisture Dynamics Reveal Discharge Hysteresis at An Agricultural Watershed

“…Compared to L-band microwave radiometer-based satellite missions with relatively low spatial resolution (25~40 km), Synthetic Aperture Radar (SAR) satellites provide high-resolution surface soil moisture estimates at the spatial scale of several meters. For example, various microwave sensors, including Terra SAR-X, the Advanced Land Observing Satellite (ALOS), Sentinel-1, Gaofen-3 (GF-3) and RADARSAT Constellation, are available to provide SAR data at high spatial resolutions (~100 m) in various frequencies, such as X-band (9.6 GHz), C-band (3.9-5.75 GHz) or L-band (0.39-1.55 GHz), with single, dual or full polarization [14][15][16][17][18].…”

Soil Water Content Estimation With the Presence of Vegetation Using Ultra Wideband Radar-Drone

“…After the estimated soil permittivity is obtained, the estimated value of the soil water content can be determined based on the Top model equation in (12). Fig.…”

“…However, the high measurement accuracy of Gravimetric methods then becomes a consideration as ground truth. Remote sensing methods such as satellite imagery [8][9][10] and SAR [11][12][13][14] can support collecting the soil water content data from a large area. However, to increase detection accuracy in the presence of vegetation effects, it is necessary to develop a fairly complex processing technique.…”

“…The wave propagation phenomenon is generally used as a basic concept in the remote sensing method. These remote sensing methods include satellite imaging [8][9][10], Synthetic Aperture Radar (SAR) [11][12][13][14], radiometry [15], Time domain reflectometry (TDR) [16][17][18][19] and Ground Penetrating Radar (GPR) [20][21][22][23][24][25][26]. The measurement result from a contact or invasive sensor such as a TDR is limited to a certain point in representing the soil water content.…”

Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered Area

“…They used tenfold cross-validation to show that DBN performed more stably with different data when combined with the WCM model [17]. Using ALOS-2 microwave data, Gao et al (2021) developed a genetic algorithm-based method for optimizing BP neural networks (GA-BP) to estimate SM in vegetated areas [18]. Cui et al (2022) established an ANN SMC retrieval algorithm combined with the water cloud model, the advanced integral equation model, and the Oh model to perform SM inversion in high spatial resolution over agroforestry areas [19].…”