Sentinel-1 interferometric coherence and backscattering analysis for crop monitoring

Abstract

Abstract In this study, we investigate the synergic use of synthetic aperture radar (SAR) backscattering (i.e., sigma nought σ0) and InSAR coherence (γ) maps as a tool for crop growth monitoring. Experiments were carried out using Sentinel-1 TOPS SAR data and field observations in one of the State General Directorate of Agriculture Enterprise farms in Konya (Central Turkey). The phenological stages of maize, sunflower, and wheat have been analyzed and compared to coherence and backscatter time series of Sentinel-1 data on multiple tracks and polarizations. The results evidence a strong correlation between different phenological stages of the crops and the InSAR coherence. Specifically, the observed coherence values are the highest for the maize (γasc, desc\xa0=\xa00.47) and sunflower (γasc\xa0=\xa00.49, γdesc\xa0=\xa00.48) after plowing the fields and seeding the crops. The coherence decreases with the plants growth and reaches the lowest values for maize, sunflower, and wheat (γ\xa0=\xa00.08, γ\xa0=\xa00.09 and γ\xa0=\xa00.07, respectively) when the ground is completely covered by plants. Then, a coherence increase is observed after the harvesting time (γ\xa0=\xa00.51, γ\xa0=\xa00.50 and γ\xa0=\xa00.42 for maize, sunflower, and wheat, respectively). In terms of multi-temporal SAR backscattering, we find significant changes of the σ0 values during the crops growing stages due to the changes in their leaf geometry and physical structure. The highest σ0 values for the maize, sunflower, and wheat are obtained as −9.18\xa0dB, −5.24\xa0dB and −10.05\xa0dB, respectively, for the ascending orbit, in mid growing stages. Results show the improved capacity of SAR-driven measurements for agriculture monitoring and precise farming activities when InSAR coherence and backscattering are synergistically used. Specifically, the coherence allows estimating the main growth stages of the different crop types. Moreover, SAR backscattering provides reliable information on the whole growth stages during the agricultural season, and it might be profitably exploited for crop assessment.