The retrieval of forest above-ground biomass (AGB, or biomass hereafter) using remote sensing data has received increasing attention in the last decades for several reasons: primarily, for the ability of remote sensing to spatially extrapolate point field information on forest parameters, enabling AGB mapping over large areas; for the increased availability of different Earth Observation data types; and, in a global scenario of climate change and biodiversity loss, for the relevance of forest biomass with respect to global carbon accounting and reporting, conservation and management of natural resources, and environmental modeling.¹ 2. 3. 4. 5. 6. ^{– 7}

During the southern summer season of 2015 and 2016, South Africa experienced one of the most severe meteorological droughts since the start of climate recording, due to an exceptionally strong El Niño event. To investigate spatiotemporal dynamics of surface moisture and vegetation structure, data from ESA’s Copernicus Sentinel-1/-2 and NASA’s Landsat-8 for the period between March 2015 and November 2017 were utilized. In combination, these radar and optical satellite systems provide promising data with high spatial and temporal resolution. Sentinel-1 C-band data was exploited to derive surface moisture based on a hyper-temporal co-polarized (vertical-vertical—VV) radar backscatter change detection approach, describing dynamics between dry and wet seasons. Vegetation information from a TLS (Terrestrial Laser Scanner)-derived canopy height model (CHM), as well as the normalized difference vegetation index (NDVI) from Sentinel-2 and Landsat-8, were utilized to analyze vegetation structure types and dynamics with respect to the surface moisture index (SurfMI). Our results indicate that our combined radar–optical approach allows for a separation and retrieval of surface moisture conditions suitable for drought monitoring. Moreover, we conclude that it is crucial for the development of a drought monitoring system for savanna ecosystems to integrate land cover and vegetation information for analyzing surface moisture dynamics derived from Earth observation time series.

This study aims at assessing the feasibility of automatically producing analysis-ready radiometrically terrain-corrected (RTC) Synthetic Aperture Radar (SAR) gamma nought backscatter data for ingestion into a data cube for use in a large spatio-temporal data environment. As such, this study investigates the analysis readiness of different openly available digital elevation models (DEMs) and the capability of the software solutions SNAP and GAMMA in terms of overall usability as well as backscatter data quality. To achieve this, the study builds on the Python library pyroSAR for providing the workflow implementation test bed and provides a Jupyter notebook for transparency and future reproducibility of performed analyses. Two test sites were selected, over the Alps and Fiji, to be able to assess regional differences and support the establishment of the Swiss and Common Sensing Open Data cubes respectively.

The REDD+ framework requires accurate es- timates of deforestation. To derive these estimates, ground measurements are supported by methods based on remote sensing data to automatically detect and delineate deforesta- tions over large areas. In particular in the tropics, optical data is seldom available due to cloud cover. As Synthetic Aperture Radar (SAR) data overcomes this limitation, we performed a separability analysis of two statistical metrics based on the Sentinel-1 SAR signal over forested and deforested areas. We compared the range between the 5th and 95th temporal percentiles (PRange) and the Recurrence Quantification Analysis (RQA) Trend metric. In contrast to the PRange, the RQA Trend considers the temporal order of the SAR data acquisitions. This enables the estimation of the timing of deforestation events. We assessed the impact of polarisation, acquisition orbit, and despeckling on the separability between forested and deforested areas, and between different deforestation timings for two test sites in Mexico. We found that the choice of the orbit impacts the detectability of deforestation. In all cases VH data showed a higher separability between forest and deforestations than VV data. The PRange slightly outperformed RQA Trend in the separation between forest and deforestation. However, the RQA Trend exceeded the PRange in the separation between different deforestation timings. In this study C-Band data was used, although it is commonly not considered as the most suitable SAR frequency for forestry applications Nevertheless, our approach shows, the potential of dense C-Band time series to support the REDD+ framework.