Remote Sensing for Mapping Soil Moisture and Drainage Potential in Semi-Arid Regions: Applications to the Campidano Plain of Sardinia, Italy

Sci Total Environ. 2016 Feb 1;543(Pt B):862-76. doi: 10.1016/j.scitotenv.2015.07.068. Epub 2015 Aug 5.

Abstract

The aim of this study is to investigate the potential of radar (ENVISAT ASAR and RADARSAT-2) and LANDSAT data to generate reliable soil moisture maps to support water management and agricultural practice in Mediterranean regions, particularly during dry seasons. The study is based on extensive field surveys conducted from 2005 to 2009 in the Campidano plain of Sardinia, Italy. A total of 12 small bare soil fields were sampled for moisture, surface roughness, and texture values. From field scale analysis with ENVISAT ASAR (C-band, VV polarized, descending mode, incidence angle from 15.0° to 31.4°), an empirical model for estimating bare soil moisture was established, with a coefficient of determination (R(2)) of 0.85. LANDSAT TM5 images were also used for soil moisture estimation using the TVX slope (temperature/vegetation index), and in this case the best linear relationship had an R(2) of 0.81. A cross-validation on the two empirical models demonstrated the potential of C-band SAR data for estimation of surface moisture, with and R(2) of 0.76 (bias +0.3% and RMSE 7%) for ENVISAT ASAR and 0.54 (bias +1.3% and RMSE 5%) for LANDSAT TM5. The two models developed at plot level were then applied over the Campidano plain and assessed via multitemporal and spatial analyses, in the latter case against soil permeability data from a pedological map of Sardinia. Encouraging estimated soil moisture (ESM) maps were obtained for the SAR-based model, whereas the LANDSAT-based model would require a better field data set for validation, including ground data collected on vegetated fields. ESM maps showed sensitivity to soil drainage qualities or drainage potential, which could be useful in irrigation management and other agricultural applications.

Keywords: Agriculture; Hydrology; LANDSAT imagery; Remote sensing; SAR C-band imagery; Soil moisture.

Publication types

  • Research Support, Non-U.S. Gov't