Intercomparison of Downscaling Techniques for Satellite Soil Moisture Products

Daeun Kim,Heewon Moon,Jungho Im,Minha Choi,Hyunglok Kim

doi:10.1155/2018/4832423

Abstract

During recent decades, various downscaling methods of satellite soil moisture (SM) products, which incorporate geophysical variables such as land surface temperature and vegetation, have been studied for improving their spatial resolution. Most of these studies have used least squares regression models built from those variables and have demonstrated partial improvement in the downscaled SM. This study introduces a new downscaling method based on support vector regression (SVR) that includes the geophysical variables with locational weighting. Regarding the in situ SM, the SVR downscaling method exhibited a smaller root mean square error, from 0.09 to 0.07 m3·m−3, and a larger average correlation coefficient increased, from 0.62 to 0.68, compared to the conventional method. In addition, the SM downscaled using the SVR method had a greater statistical resemblance to that of the original advanced scatterometer SM. A residual magnitude analysis for each model with two independent variables was performed, which indicated that only the residuals from the SVR model were not well correlated, suggesting a more effective performance than regression models with a significant contribution of independent variables to residual magnitude. The spatial variations of the downscaled SM products were affected by the seasonal patterns in temperature-vegetation relationships, and the SVR downscaling method showed more consistent performance in terms of seasonal effect. Based on these results, the suggested SVR downscaling method is an effective approach to improve the spatial resolution of satellite SM measurements.

Highlights

Sensed soil moisture (SM) offers increased spatial coverage and improved temporal continuity and has resulted in substantial changes in our understanding of the global water cycle [1, 2]
The relatively large spatial resolution of approximately 10 km for passive/active microwave satellite remote sensing datasets is the main reason they cannot be effectively applied to hydrological studies at a regional scale [3]. e issue of scale mismatch between remotely sensed and in situ SM has been considered unavoidable and has been critically evaluated using coarse satellite measurements, in areas with nonhomogeneous land cover [4]. us, downscaling techniques that focus on the spatial resolution of remotely sensed SM are important to match with an in situ dataset and enable practical applications
Synergistic approaches to disaggregate microwave remote sensing SM measurements using visible/infrared (VIS/IR) sensors with enhanced spatial resolution have been performed in previous studies [5,6,7,8,9]. is approach is based on the relationship of SM between the land surface temperature (Ts) and the normalized difference vegetation index (NDVI) that theoretically forms a triangular shape because of the evaporative cooling effect [10, 11]

Summary

Introduction

Sensed soil moisture (SM) offers increased spatial coverage and improved temporal continuity and has resulted in substantial changes in our understanding of the global water cycle [1, 2]. The relatively large spatial resolution of approximately 10 km for passive/active microwave satellite remote sensing datasets is the main reason they cannot be effectively applied to hydrological studies at a regional scale [3]. A methodology to downscale active microwave SM based on Ts and NDVI using SVR is suggested to build an optimized regression model that considers the spatial pattern of the original dataset to obtain finer, more accurate SM distribution relative to the conventional VIS/IR downscaling methods. A methodology to downscale active microwave SM based on Ts and NDVI using SVR is suggested to build an optimized regression model that considers the spatial pattern of the original dataset to obtain finer, more accurate SM distribution relative to the conventional VIS/IR downscaling methods. is research is unique because it offers a cross comparison between the newly suggested SVR downscaling method and conventional methods. e downscaled SM was evaluated by taking in situ measurements from nine measurement sites within a 150 km × 125 km study area of the Korean Peninsula from March to November 2012. e polynomial regression downscaling method was applied in the same study area for comparative evaluation

Study Area and Dataset Descriptions

Methods

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