# Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping
###### Abstract  
This paper reports recent advancements in the field of Synthetic Aperture Radar (SAR) for forest mapping by using interferometric short-time-series. In particular, we first present how the interferometric capabilities of the Sentinel-1 satellites constellation can be exploited for the monthly mapping of the Amazon rainforest. Indeed, the evolution in time of the interferometric coherence can be properly modeled as an exponential decay and the retrieved interferometric parameters can be used, together with the backscatter, as input features to the machine learning Random Forests classifier. Furthermore, we present an analysis on the benefits of the use of textural information, derived from Sentinel-1 backscatter, in order to enhance the classification accuracy. These textures are computed through the Sum And Difference Histograms methodology and the final classification accuracy, resulting by adding them to the aforementioned features, is a thematic map that exceeds an overall agreement of 85%, when validated using the optical external reference Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) map. The experiments presented in the final part of the paper are enriched with a further analysis and discussion on the selected scenes using updated multispectral Sentinel-2 acquisitions. forest mapping; Sentinel-1; short-time-series; synthetic aperture radar; interferometric coherence; temporal decorrelation; Random Forests; spatial texture 2020  
sensors is their capability to acquire consistent data also in cloud-covered conditions, which hide the Amazon rainforest from view for most of the whole wet season. In the last five years innovative global forest/non-forest maps have been generated using radar sensors, such as the forest/non-forest maps derived from the ALOS/PALSAR SAR backscatter at L band [5] or from the single-pass volume correlation coefficient estimated from the Interferometric SAR (InSAR) coherence of the X-band bistatic TanDEM-X system [6]. In this context a novel methodology for large-scale land cover maps generation, based on the combination of both backscatter and interferometric information from repeat-pass short-time-series has been developed in [7] and tested on Sentinel-1 (S-1) data acquired over Europe. The Sentinel-1 constellation comprises two C-band Dual-pol SAR satellites sharing the same orbit plane with a 180\({}^{\circ}\) orbital phasing difference. The main goal of S-1 mission is to provide a frequent operational interferometric capability (6 days repeat-pass), neighbouring the acquisitions within a thin orbital tube of 100 m (root mean square) diameter [8] and covering large areas using the Interferometric Wide swath (IW) mode, a single-pass multi-swath scanning burst mode based on the Terrain Observation with Progressive Scans (TOPS) acquisition geometry [9; 10]. In this paper, we extend and improve the concepts presented in [7] to the rainforest mapping problem, by considering short-time-series acquired at six days revisit time over the Rondonia state, Brazil. Additionally to backscatter and temporal decorrelation contribution, we introduce new features for the classification, based on the analysis of the textural content from SAR backscatter, which allow for an improvement of the final accuracy. The results are validated using an independent external reference map and confirm the high potential of the developed methodology for forest monitoring purposes. The paper is organized in six sections. Section 2 recalls the theoretical basics presented in [7] and introduces the proposed textural information to be used as input classification feature. Section 3 describes the updated processing chain used for the generation of forest/non-forest maps from interferometric short-time-series. Then, Section 4 describes the materials used in this work, namely the Sentinel-1 short-time-series acquired over the state of Rondonia, in Brazil, and the external reference used for the training and validation of the classification algorithm.