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The Biomass Proxy is a new cloud-free vegetation monitoring product that offers timely and analysis-ready data indicative of above-ground crop biomass dynamics at 10m spatial resolution. The Biomass Proxy links the consistent and continuous temporal signal of the Sentinel-1 Cross Ratio (CR), a vegetation index derived from Synthetic Aperture Radar backscatter, with the spatial information of the Sentinel-2 Normalized Difference Vegetation Index (NDVI), a vegetation index derived from optical observations. A global scaling relationship between CR and NDVI forms the basis of a novel fusion methodology based on static and dynamic combinations of temporal and spatial responses of CR and NDVI at field level. The fusion process is used to mitigate the impact on product quality of low satellite revisit periods due to acquisition design or persistent cloud coverage, and to respond to rapid changes in a timely manner to detect environmental and management events. The resulting Biomass Proxy provides time series that are continuous, unhindered by clouds, and produced uniformly across all geographical regions and crops. The Biomass Proxy offers opportunities including improved crop growth monitoring, event detection, and phenology stage detection. [ABSTRACT FROM AUTHOR]

Lineament mapping is a very important step in geological studies and mineral exploration, with the evolution of remote sensing, processing methods as well as the availability of several optical and radar satellite data, lineaments can be detected without using traditional methods. The objective of this work is to recommend the most suitable data for lineaments to be used in the field of geological science, by comparing the performance of three different RADAR data, namely ALOS PALSAR (The Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar), Radarsat-1 and Sentinel-1 for automatic lineament extraction using a combination of edge detection and line linking algorithms. The methodology consists to link between the length, number, orientation and density of lineaments with surface characteristics such as the slope, faults, lithology, discontinuities and mineral veins. The results obtained show that the extracted lineaments from Sentinel-1 VH polarization have a better correlation with geological units, tectonic system direction, as well as shading and slope maps, which is due to the effectiveness of VH polarization, which is independent of soil properties compared to the other polarizations that have shown an overestimation of the lineaments. [ABSTRACT FROM AUTHOR]

Selective logging is a major cause of forest degradation in the tropics, but its precise scale, location and timing are not known as wide-area, automated remote sensing methods are not yet available at this scale. This limits the abilities of governments to police illegal logging, or monitor (and thus receive payments for) reductions in degradation. Sentinel-1, a C-band Synthetic Aperture Radar satellite mission with a 12-day repeat time across the tropics, is a promising tool for this due to the known appearance of shadows in images where canopy trees are removed. However, previous work has relied on optical satellite data for calibration and validation, which has inherent uncertainties, leaving unanswered questions about the minimum magnitude and area of canopy loss this method can detect. Here, we use a novel bi-temporal LiDAR dataset in a forest degradation experiment in Gabon to show that canopy gaps as small as 0.02 ha (two 10 m × 10 m pixels) can be detected by Sentinel-1. The accuracy of our algorithm was highest when using a timeseries of 50 images over 20 months and no multilooking. With these parameters, canopy gaps in our study site were detected with a false alarm rate of 6.2%, a missed detection rate of 12.2%, and were assigned disturbance dates that were a good qualitative match to logging records. The presence of geolocation errors and false alarms makes this method unsuitable for confirming individual disturbances. However, we found a linear relationship ( r 2 = 0.74 ) between the area of detected Sentinel-1 shadow and LiDAR-based canopy loss at a scale of 1 hectare. By applying our method to three years' worth of imagery over Gabon, we produce the first national scale map of small-magnitude canopy cover loss. We estimate a total gross canopy cover loss of 0.31 Mha, or 1.3% of Gabon's forested area, which is a far larger area of change than shown in currently available forest loss alert systems using Landsat (0.022 Mha) and Sentinel-1 (0.019 Mha). Our results, which are made accessible through Google Earth Engine, suggest that this approach could be used to quantify the magnitude and timing of degradation more widely across tropical forests. [ABSTRACT FROM AUTHOR]

Spatiotemporal distribution of soil moisture is important for hydrometeorological and agricultural applications. There is growing interest in monitoring soil moisture in relation to soil- and land-based natural flood management (NFM), to understand the soil’s ability, via land-use and management changes, and to delay the arrival of flood peaks in nearby watercourses. This article monitors relative surface soil moisture (rSSM) across the Thames Valley, U.K., using Sentinel-1 data, and the Vienna University of Technology (TU-Wien) Change Detection Algorithm, with a novel exploration of monthly and annual normalization factors and spatial averaging. Two pairs of normalization factors are introduced to remove impacts from varying local incidence angles through direct and multiple regression slopes. The spatiotemporal distribution of rSSM values at various spatial resolutions (1000, 500, 250, and 100 m) is assessed. Comparisons with in situ soil moisture data from the COSMOS-UK network show that, while general temporal trends agree, the difference in effective depth of measurements, coupled with vegetation impacts during the growing season, makes comparison with soil moisture observations difficult. Temporal rSSM trends can be retrieved at spatial resolutions down to 100 m, and the rSSM RMSE was found to decrease as the spatial resolution increases. The vegetation effects upon the rSSM are further explored by comparing the two dominant land cover types: Arable and Horticulture, and Improved Grassland. It was found that, while the rSSM retrieval for these land covers was possible, and the general soil moisture trend is clear, overlying vegetation during the summer artificially increased the rSSM values. [ABSTRACT FROM AUTHOR]

Delineation of shellfish beds through field surveys is time consuming. Remote sensing can help in detecting the location and boundaries of shellfish beds. This can be achieved through the use of aerial photographs and optical satellite sensors during cloud-free and low-tide conditions. Cloud penetrating radar is an alternative, but still requires careful selection of low tide imagery. Manual selection becomes cumbersome for large areas, such as the entire Dutch Wadden Sea. We therefore developed a method that automatically combines dense time series of Sentinel-1 radar imagery into a useful mosaic that allows for effective delineation of shellfish beds. The method consists of temporal compositing many images in the cloud-based Google Earth Engine. We evaluated different combinations of 1) compositing options (average backscatter and various percentiles), 2) temporal periods for compositing, 3) different polarizations, and 4) imagery from ascending versus descending satellite paths. The resulting composite images were visually compared with in-situ records to identify which composite visually best allowed for shellfish bed delineation. Although the average VH-polarized backscatter for one full year provided effective demarcation, an RGB color composite containing three average VH images for four months each provided improved visibility of the shellfish beds. We then quantitatively compared which compositing option was most effective in separating pixels with shellfish beds from those without. For this purpose, the unsupervised classification approach of K-means clustering was applied to the different composite images, and outcomes (i.e. selected classes) were compared with field data derived from the annual in-situ survey. The multi-season image composites (i.e. Jan–Apr, May–Aug, and Sep–Dec) that were made by combining VH image acquisitions of only descending and of combined ascending and descending paths resulted in higher accuracies than other compositing options tested. The producer and user accuracy of the multi-season descending composite were respectively 40% and 70% for the full Dutch Wadden Sea, against 59% and 73% for a smaller subset. These accuracies are similar to what is previously achieved for small study areas with carefully selected radar images acquired around low tide. Moreover, our approach could identify various shellfish beds that were initially missed by the in-situ surveys, but for which later the presence was confirmed (so the actual producer accuracy would increase to about 82%). The composite images can be created on the fly with Google Earth Engine shortly before a field campaign and used as a base for sampling design. • We performed rapid temporal compositing of Sentinel-1 images in Google Earth Engine. • We evaluated different compositing options for the entire Dutch Wadden Sea. • A multi-season VH composite performed best for shellfish bed classification. • Producer and user accuracies reached 82% and 73% respectively, for a smaller subset. • Our approach allows for effective monitoring and already guides field inventories. [ABSTRACT FROM AUTHOR]