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.
Highlights
In order to improve the sensitivity of the radar signal, HH and HV were used as inputs in the Back Propagation (BP) neural network model
The results show that the water cloud model (WCM) (V 1 = V 2 = normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized multi-band drought index (NMDI)) can effectively eliminate the backscattering effect of vegetation, and the results of the genetic algorithm to optimize back propagation (GA-BP)
The potential of ALOS-2 L-band radar data for Soil moisture (SM) calculation was investigated over vegetation-covered fields
Summary
Soil moisture (SM) is an important state variable that significantly affects the water cycle, ecosystem, and energy exchange between the land and the atmosphere. SM information is important in different fields, such as agriculture, meteorology, hydrology, weather, and evapotranspiration forecasting [1,2,3,4,5,6,7]. In the large-scale domain, the development of remote sensing technology has provided more opportunities. Surface SM inversion based on remote sensing technology has become a hotspot of research [8,9,10,11]. Optical remote sensing cannot penetrate clouds and rain and is restricted by weather and solar illumination conditions.
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