Modeling and Mapping Soil Moisture of Plateau Pasture Using RADARSAT-2 Imagery

Abstract

Accurate soil moisture retrieval of a large area in high resolution is significant for plateau pasture. The object of this paper is to investigate the estimation of volumetric soil moisture in vegetated areas of plateau pasture using fully polarimetric C-band RADARSAT-2 SAR (Synthetic Aperture Radar) images. Based on the water cloud model, Chen model, and Dubois model, we proposed two developed algorithms for soil moisture retrieval and validated their performance using experimental data. We eliminated the effect of vegetation cover by using the water cloud model and minimized the effect of soil surface roughness by solving the Dubois equations. Two experimental campaigns were conducted in the Qinghai Lake watershed, northeastern Tibetan Plateau in September 2012 and May 2013, respectively, with simultaneous satellite overpass. Compared with the developed Chen model, the predicted soil moisture given by the developed Dubois model agreed better with field measurements in terms of accuracy and stability. The RMSE, R 2 , and RPD value of the developed Dubois model were (5.4, 0.8, 1.6) and (3.05, 0.78, 1.74) for the two experiments, respectively. Validation results indicated that the developed Dubois model, needing a minimum of prior information, satisfied the requirement for soil moisture inversion in the study region.

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

“…Our models (including SVR method) prediction error seems to be high in comparison to previous SAR/remote sensing based soil moisture prediction studies (e.g., [23][24][25][36][37][38]) and showed an overestimation of soil moisture in comparison to AWS observed values. This could be attributed to (i) the limited number of ground observed stations-with the limited number of observation stations, it is difficult to entirely characterize the spatial patterns of soil moisture over the study area, (ii) the use of soil moisture observed/simulated at 20/10 cm soil depth during model development, while microwave signals at the C-band are more sensitive to volumetric moisture to the top few centimeters of soil [81], (iii) spatial scale difference between ground observed points and satellite footprints/pixels, and (iv) sub-pixel heterogeneity of land surface conditions for lower scale analysis.…”

Evaluation of Normalized Difference Water Index as a Tool for Monitoring Pasture Seasonal and Inter-Annual Variability in a Mediterranean Agro-Silvo-Pastoral System

“…In fact, soil moisture content (SMC) controls the exchange of latent and sensible heat between land and atmosphere across the surface, which is the trigger of feedback mechanisms in land-atmosphere interactions [5]. Soil moisture has great importance in various applications such as natural risk assessment, hydrology, climatology, ecology, and agronomy, whereby the retrieval of spatial distribution of SMC on a large scale is an important research topic [6].…”

Climate Changes Challenges to the Management of Mediterranean Montado Ecosystem: Perspectives for Use of Precision Agriculture Technologies

“…The vegetation index (NDVI) obtained from measurements of this sensor showed strong correlations with parameters related to pasture quality (PMC, with R 2 of 0.91; CP, with R 2 of 0.82) or to pasture quality degradation (PQDI, with R 2 of 0.93; NDF with R 2 of 0.84). These results are justified by the principle of NDVI, based on reflectance at the near infrared and red regions, which is strongly related to the vegetation density [47] or to the chlorophyll content, and thus, with the plant growth [48]. Serrano et al [17], Albayrak [49], and Pullanagari et al [50] also found significant relationships between spectral measurements and pasture quality parameters, which can be attributed to the absorbance of visible radiance by the existing chlorophyll in green vegetation.…”