Soil Moisture Estimation with GNSS Interferometric Reflectometry and Multispectral Satellite Model

Abstract

During the last decade, GNSS Reflectometry (GNSS-R) together with its ground application, GNSS Interferometric Reflectometry (GNSS-IR), have been gaining momentum with satellite missions and ground campaigns. These passive remote sensing technique lies within the microwave remote sensing technology and makes use of satellite navigation signals for Earth Observation (EO) purposes.

Another passive EO technology is multispectral satellite imagery from missions such as Landsat-8. Multispectral imagery is an optical remote sensing technology and covers various wavelengths of the optical spectrum, allowing to analyze the spectral response of the surface materials.

This work focuses on GNSS Interferometric Reflectometry processing for soil moisture estimation, followed by a GNSS-IR aided multispectral model using Landsat-8 satellite data, with the aim of providing an accurate, cost-effective solution with wide-area coverage. In this research, a GNSS-IR processing chain is developed to estimate volumetric soil moisture surrounding static geodetic receivers. Results from this technique are used to fit a linear model with Landsat-8 data combining several optical indexes that have high correlation with soil moisture. Finally, the proof-of-concept of a multispectral imagery model, aided by local GNSS-IR results, is demonstrated and verified against data from the Soil Moisture Active Passive (SMAP) satellite mission for a wide-area coverage.