Covariation of Passive–Active Microwave Measurements over Vegetated Surfaces: Case Studies at L-Band Passive and L-, C- and X-Band Active

Abstract

The analysis of soil and land cover scattering properties and their connection with the parameters of microwave scattering is a longstanding research topic. Recently, the advent of modern space-borne microwave radiometers like SMAP in addition to the trend towards open data for scientific use fostered the development of enhanced models based on data fusion from different platforms permitting more accurate assessments. SMAP was designed to operate on an integrated combination of a radiometer and a radar, both operating in L-band. Unexpected failure of the radar component encouraged scientists to experiment various combination of data from the surviving radiometer with other sources of radar data, notably C-band Sentinel-1 data. In this work, we present a case study on a possible combination of SMAP radiometer data with X-band radar data from TerraSAR-X and COSMO-SkyMed, comparing results with those provided by NASA from their standard production procedures. The study was performed on two test sites, one at an agricultural site in Germany and one in the Brazilian Amazon, to explore very different vegetation conditions. This work is a part of a broader research effort addressing the combination of multiple sources of passive and active microwave sensing data. The research question defining this research effort is whether the use of data from multiple active sources affords either obtaining more accurate estimates of active–passive co-variation parameters for a given observation period, or shortening the minimum observation period by increasing the temporal density of active samples. In this framework, this paper addresses a preliminary comparison of fresh and past results obtained from C-, X-, and L-band active sensing data. The observed relations offer interesting clues on the impact of band selection on soil vegetation analysis.

A point-surface fusion method for vegetation water content retrievalconsidering optimization of GNSS sites and modeling elements

“…Moreover, optical remote sensing is easily affected by clouds and fog, resulting in the loss of information [12,13]. Although the VWC obtained by microwave remote sensing is not affected by clouds and fog, its spatial resolution is low, and the VWC information at small and medium scales is difficult to represent accurately [14][15][16]. Some scholars have tried combining optical remote sensing and microwave remote sensing to retrieve VWC.…”

Identification of Rice Fields in the Lombardy Region of Italy Based on Time Series of Sentinel-1 Data

“…This situation calls for specific research on space-based mapping of European rice, with an outlook to extracting information that may be useful for example to enhance food traceability by providing third-party (satellite-based) information on development stages, or on cross-checking compliance with production specifications. Spaceborne remote sensing data can indeed help monitoring crops at a large scale, providing precise and timely information on the phenological status and development of vegetation [4][5][6][7], detecting possible emerging problems, and also building independent records of the crop status. This may pave the way towards cross-checking of organic claims [8].…”