Information about forest structures is becoming crucial to earth's global carbon cycle, forest habitats, and biodiversity. The Global Ecosystem Dynamics Investigation (GEDI) provides 25-m diameter footprints of the surface for 3-D structure measurements. The main goal of this study is to compare 12 031 footprints of GEDI data with other airborne and spaceborne digital elevation models (DEMs) for Southwest Spain. Ground elevation differences [elevation of the lowest mode (ELM)] are analyzed by comparing GEDI measurements with airborne laser scanning (ALS) LiDAR- and TanDEM-X-derived DEMs. The vertical structure (RH100) is compared to the ALS LiDAR measurement. Ten zones are analyzed, considering different degrees of coverage and slopes. We achieved a root mean square error (RMSE) of 6.13 m for the ELM when comparing GEDI and LiDAR data and an RMSE of 7.14 m when comparing GEDI and TanDEM-X data. For some of the studied areas, these values were considerably smaller, with RMSE values even lower than 1 m. For the RH100 metric, an RMSE of 3.56 m was achieved when comparing GEDI and LiDAR data, but again with a minimum value of 2.09 m for one zone. The results show a clear relation to coverage and slope, especially for the latter. This work also evaluates the positional uncertainty of GEDI footprints, shifting them ±10 and ±5 m along and across the track of the satellite orbit and their intermediate angular positions. The outcomes reveal a strong tendency to obtain better results in the ELM when setting the footprint to 270° and displacing it within 10 m of its positional uncertainty in comparison with the LiDAR and TanDEM-X data.

The early and automatic identification of crops declared by farmers is essential for streamlining European Union Common Agricultural Policy (CAP) payment processes. Currently, field inspections are partial, expensive and entail a considerable delay in the process. Chronological satellite images of cultivated plots can be used so that neural networks can form the model of the declared crop. Once the patterns of a crop are obtained, the correspondence of the declaration with the model of the neural network can be systematically predicted, and can be used for monitoring the CAP. In this article, we propose a learning model with neural networks, using as examples of training the pixels of the cultivated plots from the satellite images over a period of time. We also propose using several years in the training model to generalise the patterns without linking them to the climatic characteristics of a specific year. The article also describes the use of the model in learning the multi-year pattern of tobacco cultivation with very good results.

Mapping land cover with high accuracy has become a reality with the application of current remote sensing techniques. Due to the specific spectral response of the vegetation, soil and vegetation indices are adequate tools to help in the discrimination of land uses. Additionally, the accuracy of satellite imagery classification can be improved using multitemporal series combined with LiDAR data. This datafusion takes advantage of the information provided by LiDAR for the vegetation cover density, and the capability of multispectral data to detect the type of vegetation. The main goal of this study is to analyze the accuracy enhancement in land cover classification of two forested watersheds when using datafusion of annual time series of Sentinel-2 images complemented with low density LiDAR. The obtained results show that overall accuracy is better if LiDAR data is included in the classification. This improvement can be a significant issue in land cover classification of forest watershed due to relationship and influence that vegetation cover has on runoff estimation.

The effects of global warming and climate change are being felt through more extreme and prolonged periods of drought. Multiple meteorological indices are used to measure drought, but they require hydrometeorological data; however, other indices measured by remote sensing and used to quantify vegetation vigor can be correlated with the former. This study investigated the correlation between both index types by vegetation type and season. The correlations were also spatially modeled in a drought event in southwestern Spain. In addition, three maps with different levels of detail in terms of vegetation categorization were compared. The results generally showed that grassland was the most well correlated category between the SPEI and the FAPAR, LAI, and NDVI. This correlation was more pronounced in autumn and spring, which is when most changes in vegetation senescence and growth occur. The spatiotemporal analysis indicated a very similar behavior for grasslands grouped in an area indicated by the climate change adaptation maps as having a high evapotranspiration forecast. Finally, in a forest-based forecast analysis, the indices that best explained the performance of the SPEI were again FAPAR, LAI, and NDVI, with a lag of up to 20 days. Therefore, the results showed that remotely sensed indices are good indicators of drought status and can be variably explanatory of traditional drought indicators. Moreover, complementing the study with spatiotemporal analysis made it possible to detect areas particularly vulnerable to climate change.