Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

• Published in: Remote sensing (Basel, Switzerland) Vol. 10; no. 10; p. 1647
• Main Authors: Molijn, Ramses, Iannini, Lorenzo, López Dekker, Paco, Magalhães, Paulo, Hanssen, Ramon
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2018
• Subjects: Algorithms; Backscattering; Classification; Earth science; Food security; incidence angle; Moisture content; Precipitation; Remote sensing; SAR signals; soil type; Soil types; Soils; Studies; Variation; Vegetation; vegetation classification; Vegetation growth; Brazil; United States > US; Netherlands
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs10101647

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.