Measuring biomass changes due to woody encroachment and deforestation/degradation in a forest-savanna boundary region of central Africa using multi-temporal L-band radar backscatter' Remote Sensing of

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Land cover mapping of large areas is challenging due to the significant volume of satellite data to acquire and process, as well as the lack of spatial continuity due to cloud cover. Temporal aggregation—the use of metrics (i.e., mean or median) derived from satellite data over a period of time—is an approach that benefits from recent increases in the frequency of free satellite data acquisition and cloud-computing power. This enables the efficient use of multi-temporal data and the exploitation of cloud-gap filling techniques for land cover mapping. Here, we provide the first formal comparison of the accuracy between land cover maps created with temporal aggregation of Sentinel-1 (S1), Sentinel-2 (S2), and Landsat-8 (L8) data from one-year and test whether this method matches the accuracy of traditional approaches. Thirty-two datasets were created for Wales by applying automated cloud-masking and temporally aggregating data over different time intervals, using Google Earth Engine. Manually processed S2 data was used for comparison using a traditional two-date composite approach. Supervised classifications were created, and their accuracy was assessed using field-based data. Temporal aggregation only matched the accuracy of the traditional two-date composite approach (77.9%) when an optimal combination of optical and radar data was used (76.5%). Combined datasets (S1, S2 or S1, S2, and L8) outperformed single-sensor datasets, while datasets based on spectral indices obtained the lowest levels of accuracy. The analysis of cloud cover showed that to ensure at least one cloud-free pixel per time interval, a maximum of two intervals per year for temporal aggregation were possible with L8, while three or four intervals could be used for S2. This study demonstrates that temporal aggregation is a promising tool for integrating large amounts of data in an efficient way and that it can compensate for the lower quality of automatic image selection and cloud masking. It also shows that combining data from different sensors can improve classification accuracy. However, this study highlights the need for identifying optimal combinations of satellite data and aggregation parameters in order to match the accuracy of manually selected and processed image composites.

Russia's forests play an important role in the global carbon cycle. Because of their scale and interannual variability, forest fires can change the direction of the net carbon flux over Eurasia. 2002 and 2003 were the first two consecutive years in the atmospheric record in which the carbon content rose by more than 2 ppm per year. Northern Hemisphere fires could be the reason. We show that 2002 and 2003 were the two years with the largest fire extent in Central Siberia since 1996 using new measurements of burned forest area in Central Siberia derived from remote sensing. To quantify the relationship between Siberian forest fires and climate variability, we compare these measurements with time‐series of large‐scale climatic indices for the period 1992–2003. This paper is amongst the first studies that analyse statistical relationships between interannual variability of forest fires in Russia and climate indices. Significant relationships of annual burned forest area with the Arctic Oscillation, summer temperatures, precipitation, and the El Niño index NINO4 were found (p < 0.1). In contrast, we find no significant relation with the El Niño indices NINO1, NINO3 or SOI (p > 0.1). Interannual forest fire variability in Central Siberia could best be explained by a combination of the Arctic Oscillation index and regional summer temperatures (r2 = 0.80).

Accurate crop yield estimates are important for governments, farmers, scientists and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate withinfield wheat yield variability in a single year. The impact of data resolution and availability on yield estimation is explored using different combinations of input data. This was achieved by combining Sentinel-2 with environmental data (e.g. meteorological, topographical, soil moisture) for different periods throughout the growing season. Yield was estimated using Random Forest (RF) regression models. They were trained and validated using a dataset containing over 8000 points collected by combine harvester yield monitors from 39 wheat fields in the UK. The results demonstrate that it is possible to produce accurate maps of within-field yield variation at 10m resolution using Sentinel-2 data (RMSE 0.66 tonnes/ha). When combined with environmental data further improvements in accuracy can be obtained (RMSE 0.61 tonnes/ha). We demonstrate that with knowledge of crop-type distribution it is possible to use these models, trained with data from a few fields, to estimate within-field yield variability on a landscape scale. Applying this method gives us a range of crop yield across the landscape of 4.09 to 12.22 tonnes/ha, with a total crop production of approx. 289000 tonnes.