Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

Xiang Zhang,Hui Zhao,Xiaoming Gao,Xinming Tang

doi:10.1155/2018/7914581

Abstract

The objective of this research is to optimize the Alpha approximation model for soil moisture retrieval using multitemporal SAR data. The Alpha model requires prior knowledge of soil moisture range to constrain soil moisture estimation. The solution of the Alpha model is an undetermined problem due to the fact that the number of observation equations is less than the number of unknown parameters. This research primarily focused on the optimization of Alpha model by employing multisensor and multitemporal SAR data. The disadvantage of the Alpha model can be eliminated by the combination of multisensor SAR data. The optimized Alpha model was evaluated on the basis of a comprehensive campaign for soil moisture retrieval, which acquired multisensor time series SAR data and coincident field measurements. The agreement between the estimated and measured soil moisture was within a root mean square error of 0.08 cm3/cm3 for both methods. The optimized Alpha model shows an obvious improvement for soil moisture retrieval. The results demonstrated that multisensor and multitemporal SAR data are favorable for time series soil moisture retrieval over bare agricultural areas.

Highlights

Soil moisture is an essential parameter controlling many biophysical processes that impact water, energy, and carbon exchanges at the land-atmosphere interface
The rationality of Alpha model was evaluated on the basis of IEM and Oh model within a wide range of soil surface parameters. en, the applicability of Alpha model was further assessed in combination with time series Synthetic aperture radar (SAR) data and field measurements. e multitemporal Radarsat-2 data and measured soil moisture of the same sampling sites were employed for the theoretical analysis
The observation equations based on the Alpha model were constructed using multitemporal Radarsat-2 data. en, soil moisture was estimated in combination with the valid range of soil moisture content

Summary

Introduction

Soil moisture is an essential parameter controlling many biophysical processes that impact water, energy, and carbon exchanges at the land-atmosphere interface. Accurate soil moisture retrieval from SAR data is still a challenging task due to the fact that the radar backscatter is influenced by multiple parameters such as soil dielectric constant (related to soil moisture), surface roughness, and vegetation conditions [4,5,6,7,8,9,10]. E Alpha approximation model is appealing for soil moisture retrieval due to its simplicity, and this method requires the initial estimates of soil moisture boundary. Such bounds can be obtained from climate models, calibration on a speci c dataset [31,32,33,34], or juxtaposition method [35, 36]. The system of equations constructed using the Alpha model has more unknowns than equations; there exist an in nite number of solutions. erefore, these issues hampered the accurate estimation of soil moisture content using the Alpha model

Results

Discussion

Conclusion