Field-Scale Soil Moisture Retrieval Using PALSAR-2 Polarimetric Decomposition and Machine Learning

Xiaodong Huang,Michael H Cosh,Jinfei Wang,Michele Reba,Nathan Torbick,Beth Ziniti

doi:10.3390/agronomy11010035

Abstract

Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R2 of 0.60.

Highlights

Soil moisture is identified as an essential climatic variable by the Global Observing System for Climate given its role in energy flux and climate–land feedback [1,2]
It is evident that in September (Day of Year (DoY) around 255), both the double-bounce and volume scattering over corn fields decrease sharply which might be because the Synthetic Aperture Radar (SAR) signal can penetrate the dry corn canopy when it was harvested at the end of the August and in early September
It is evident that in September (Day of Year (DoY) around 255), both the double-bounce and volume scattering over corn fields decrease sharply which might be because the SAR signal can penetrate the dry corn canopy when it was harvested at the end of the August and in early Septem6 obfe1r3

Summary

Introduction

Soil moisture is identified as an essential climatic variable by the Global Observing System for Climate given its role in energy flux and climate–land feedback [1,2]. The Group on Earth Observations (GEO) Global Agricultural Monitoring (GEOGLAM) initiative has further amplified soil moisture as an Essential Agricultural Variable (EAV) given its role in driving processes such as drought, irrigation, and crop production. Programs such as NASA’s Soil Moisture Active Passive (SMAP) and ESA’s Soil Moisture Ocean Salinity (SMOS) missions have advanced science for land surface monitoring and agricultural decision support tools. These missions have made tremendous advances in synoptic monitoring and assessment of soil moisture. A theme in these downscaling studies is that as the spatial resolution increases, the accuracy decreases [3]

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Conclusion