Sentinel-1 satellites provide temporally dense and high spatial resolution synthetic aperture radar (SAR) imagery. The open data policy and global coverage of Sentinel-1 make it a valuable data source for a wide range of SAR-based applications. In this regard, the Google Earth Engine is a key platform for large area analysis with preprocessed Sentinel-1 backscatter images available within a few days after acquisition. To preserve the information content and user freedom, some preprocessing steps (e.g., speckle filtering) are not applied on the ingested Sentinel-1 imagery as they can vary by application. In this technical note, we present a framework for preparing Sentinel-1 SAR backscatter Analysis-Ready-Data in the Google Earth Engine that combines existing and new Google Earth Engine implementations for additional border noise correction, speckle filtering and radiometric terrain normalization. The proposed framework can be used to generate Sentinel-1 Analysis-Ready-Data suitable for a wide range of land and inland water applications. The Analysis Ready Data preparation framework is implemented in the Google Earth Engine JavaScript and Python APIs.

Satellite-based near-real-time forest disturbance alerting systems have been widely used to support law enforcement actions against illegal and unsustainable human activities in tropical forests. The availability of multiple optical and radar-based forest disturbance alerts, each with varying detection capabilities depending mainly on the satellite sensor used, poses a challenge for users in selecting the most suitable system for their monitoring needs and workflow. Integrating multiple alerts holds the potential to address the limitations of individual systems. We integrated radar-based RAdar for Detecting Deforestation (RADD) (Sentinel-1), and optical-based Global Land Analysis and Discovery Sentinel-2 (GLAD-S2) and GLAD-Landsat alerts using two confidence rulesets at ten 1° sites across the Amazon Basin. Alert integration resulted in faster detection of new disturbances by days to months, and also shortened the delay to increased confidence. An increased detection rate to an average of 97% when combining alerts highlights the complementary capabilities of the optical and cloud-penetrating radar sensors in detecting largely varying drivers and environmental conditions, such as fires, selective logging, and cloudy circumstances. The most improvement was observed when integrating RADD and GLAD-S2, capitalizing on the high temporal observation density and spatially detailed 10 m Sentinel-1 and 2 data. We introduced the highest confidence class as an addition to the low and high confidence classes of the individual systems, and showed that this displayed no false detection. Considering spatial neighborhood during alert integration enhanced the overall labeled alert confidence level, as nearby alerts mutually reinforced their confidence, but it also led to an increased rate of false detections. We discuss implications of this study for the integration of multiple alert systems. We demonstrate that alert integration is an important data preparation step to make use of multiple alerts more user-friendly, providing stakeholders with reliable and consistent information on new forest disturbances in a timely manner. Google Earth Engine code to integrate various alert datesets is made openly available.

Tropical forest disturbances linked to fire usage cause large amounts of greenhouse gas (GHG) emissions and environmental damages. Supporting precise GHG estimations and counteracting illegal fire usages in the tropics require timely and thematically detailed large-scale information on fire-related forest disturbances. Multi-sensor optical and radar detection and ranging (radar) remote sensing data combined with active fire alerts shows the potential for a more in-depth characterization of fire-related forest disturbances. We utilized dense optical (Landsat-7, Landsat-8 and Sentinel-2) and radar (Sentinel-1) time series to individually map forest disturbances in the province of Riau (Indonesia) for 2018–2019. We combined the sensor-specific optical and radar forest disturbance maps with daily active fire alerts and classified their temporal relationship (predating, coinciding, postdating) into seven so-called archetypes of fire-related forest disturbances. The archetypes reflect sensor-specific sensitives of optical (e.g., changes in tree foliage) and radar (e.g., changes in tree structure) data to detect varying types of forest disturbances, ranging from either a loss of tree foliage and/or structure predating, coinciding or postdating fires. These can be related to different magnitudes of fire-related forest disturbances and burn severities and can be associated with specific land management practices, such as slash-and-burn agriculture and salvage logging. This can support policy development, local and regional forest management and law enforcement to reduce illegal fire usage in the tropics. Results suggest that a delayed or opposing forest disturbance detection in the optical and radar signal is not only caused by environmental influences or different observation densities but, in some cases, such as fire-related forest disturbances, can be related to their different sensitives to detect changes in tree foliage and structure. Multi-sensor-based forest monitoring approaches should, therefore, not simply combine optical and radar time series on a data level, as it bears the risk of introducing artefacts.

Precise and prompt information on forest disturbances in the tropics is critical to support law enforcement and protect tropical forests. In 2023, medium resolution ALOS-2 ScanSAR data (∼100 m spatial resolution) was made available for Southeast Asia, marking the first freely accessible large-area L-Band dataset. We assessed its potential for large-area forest disturbance mapping and its combination with high-resolution C-band Sentinel-1 data (∼20 m spatial resolution). We mapped forest disturbances in Sumatra, Indonesia for the year 2021 based on ALOS-2 ScanSAR data and Sentinel-1 data separately, and subsequently combined the mapped disturbances. Forest disturbances detected by both C-band and L-band SAR using a probabilistic change algorithm were combined at the product level by merging both sets of detections. The added benefit of combining both sensors was particularly evident in higher detection rates, as indicated by an improved producer accuracy (78.9 ± 11.9 %) compared to detections based on single sensor ALOS-2 ScanSAR (40.8 ± 6.3 %) and Sentinel-1 data (63.3 ± 9.6 %). Both combined and single sensor detections showed negligible false detections. ALOS-2 ScanSAR showed advantages for overcoming limited capability of Sentinel-1 to detect large-sized disturbance events characterized by post-disturbance tree remnants, occurring at locations with large-scale agricultural clearings. The medium resolution of approximately 100 m restricts the detection capability of ALOS-2 ScanSAR data for small-scale disturbances, resulting in missed detections. ALOS-2 ScanSAR detections showed a delay of up to 17.8 days compared to detections based solely on Sentinel-1 data. Combining ALOS-2 ScanSAR and Sentinel-1-based disturbances resulted in improved detection timeliness, with an average improvement of up to 16.5 days compared to Sentinel-1-based detections. Furthermore, we observed improved detection rates for our ALOS-2 ScanSAR-based detections compared to those of the ALOS-2 ScanSAR-based JICA-JAXA Forest Early Warning System in the Tropics (JJ-FAST) forest disturbance alerting product. This suggests that the potential of ALOS-2 ScanSAR data in operational forest monitoring systems is not currently fully realized. Comparing the SAR-based disturbance detections from this study with existing optical-based forest disturbance products (GFC and GLAD-L) suggested improved detection accuracies by overcoming sensor-specific omission errors when using combined SAR and optical data. The demonstrated potential of L-Band ALOS-2 ScanSAR data for improving operational forest monitoring efforts using C-band radar and optical satellites is expected to be amplified by upcoming L-Band satellite missions like NiSAR (2024) and ROSE-L (2028), which will provide freely accessible L-Band data with higher spatial resolution.

The Amazon Basin and its rivers play a vital role in regional biodiversity, the carbon cycle, and socio-economic security. Through erosion and deposition, river planforms change over time, affecting local infrastructure, food security, and changes to ecosystems. Long-term monitoring is essential for observing these dynamics. Synthetic Aperture Radar (SAR) provides a method to consistently map river planform dynamics across large areas because it is largely independent of atmospheric conditions. This study presents an approach for deriving river planform metrics across the entire Amazon Basin using Sentinel-1 C-band SAR data. This approach followed three main steps: water mask generation, validation of the data and river metrics extraction. Sentinel-1 imagery from 2017 to 2025 was composited into quarterly mean images, after which Otsu thresholding was applied to derive water classifications. Additional post-processing steps were applied to reduce terrain- and seasonal effects. The final water masks were divided into water-change classes, validated using stratified sampling and achieved an overall accuracy of 98.5%. Quarterly river planform metrics, including sinuosity, mean channel width and migration rate, were derived using channel centerline extraction, but due to a lack of in situ validation data the river metric values have not been validated. The resulting time series provide insights into how river planform changes across all Amazon sub-basins from 2017 to 2025 can be monitored using SAR-based methods. The results reveal spatial differences in river dynamics between tributaries, mostly depending on flow pattern, up- or downstream path and location in the upper, middle or lower Amazon Basin. These findings demonstrate the potential of SAR time series for monitoring large-scale river planform dynamics.