Freely available global digital elevation models (DEMs) are important inputs for many research fields and applications. During the last decade, several global DEMs have been released based on satellite data. ASTER and SRTM are the most widely used DEMs, but the more recently released, AW3D30, TanDEM-X and MERIT, are being increasingly used. Many researchers have studied the quality of these DEM products in recent years. However, there has been no comprehensive and systematic evaluation of their quality over areas with variable topography and land cover conditions. To provide this comparison, we examined the accuracy of six freely available global DEMs (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM) in four geographic regions with different topographic and land use conditions. We used local high-precision elevation models (Light Detection and Ranging (LiDAR), Pleiades-1A) as reference models and all global models were resampled to reference model resolution (1m). In total, 608 million 1x1 m pixels were analyzed. To estimate the accuracy, we generated error rasters by subtracting each reference model from the corresponding global DEM and calculated descriptive statistics for this difference (e.g., median, mean, root-mean-square error (RMSE)). We also assessed the vertical accuracy as a function of the slope, slope aspect, and land cover. We found that slope had the strongest effect on DEM accuracy, with no relationship for slope aspect. The AW3D30 was the most robust and had the most stable performance in most of the tests and is therefore the best choice for an analysis of multiple geographic regions. SRTM and NASADEM also performed well where available, whereas NASADEM, as a successor of SRTM, showed only slight improvement in comparison to SRTM. MERIT and TanDEM-X also performed well despite their lower spatial resolution.

Persistent forest loss in the Brazilian Legal Amazon (BLA) is responsible for carbon emission, reduction of ecosystem services, and loss of biodiversity. Combining spatial data analysis with high spatial resolution data for forest cover and forest loss, we quantified the spatial and temporal patterns of forest dynamics in the BLA. We identified an alarming trend of increasing deforestation, with especially high rates in 2016 and 2017. Moreover, the creation of forest cover fragments is faster than ever due to decreasing size and dispersion of forest loss patches. From 2001 to 2017, the number of large forest loss patches decreased significantly, accompanied by a reduction in the size of these patches. Enforcement of field inspections and of initiatives to promote forest conservation will be required to stop this trend.

Phenological responses of vegetation to global warming impact ecosystem gross primary production and evapotranspiration. However, high resolution and large spatial scale observational evidence of such responses in undisturbed core forest areas is lacking. Here, we analyse MODIS satellite data to assess monthly trends in gross primary productivity and evapotranspiration across undisturbed core forest areas in Europe between 2000 and 2020. Both parameters increased during the early spring and late autumn in nearly half of the total undisturbed core forest area (3601.5 km2). Enhanced productivity drove increased water-use-efficiency (the ratio of gross primary productivity to evapotranspiration). However, productivity increases during spring and autumn were not sufficient to compensate for summertime decreases in 25% of core forest areas. Overall, 20% of total gross primary productivity across all European forest core areas was offset by forest areas that exhibited a net decrease in productivity.

The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic’s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data.

Shifts in climate driven by anthropogenic land use and land cover change are expected to alter various land–atmosphere interactions. Evapotranspiration (ET) is one of these processes and plays a fundamental role in the hydrologic cycle. Using gridded reanalysis and remote sensing data, we investigated the spatiotemporal trends of precipitation, temperature, and ET for areas in the Baltic countries Lithuania, Latvia and Estonia where the land cover type had not changed from 2000 to 2018. We focused on ET but investigated the spatiotemporal trends for the three variables at monthly, seasonal, and annual time scales during this period to quantify trade-offs among months and seasons. We used the Mann-Kendall test and Sen’s slope to calculate the trends and rate of change for the three variables. Although precipitation showed fewer statistically significant increasing and decreasing trends due to its high variability, temperature showed only increasing trends. The trends were concentrated in late spring (May, +0.14°C annually), summer (June and August, +0.10°C), and early autumn (September, +0.13°C). For unchanged forest and cropland areas, we found no statistically significant ET trends. However, Sen’s slope indicated increasing ET in April, May, June, and September for forest areas and in May and June for cropland. Our results indicate that during the study period, the temperature changes may have lengthened the growing season, which affected the ET patterns of forest and cropland areas. The results also provide important insights into the regional water balance and complement the findings of other studies.

Applying remote sensing technology to map and monitor agriculture and its impacts can greatly contribute for the proper development of this activity, promoting efficient food, fiber and energy production. For that, not only remote sensing images are needed, but also ground truth information, which is a key factor for the development and improvement of methodologies using remote sensing data. While a variety of images are current available, inclusive cost-free images, field reference data is scarcer. For agricultural applications, especially in tropical regions such as Brazil, where the agriculture is very dynamic and diverse (recent agricultural frontiers, crop rotations, multiple cropping systems, several management practices, etc.), and cultivated over a vast territory, this task is not trivial. One way of boosting the researches in agricultural remote sensing is to stimulate people to share their data, and to foster different groups to use the same dataset, so distinct methods can be properly compared. In this context, our group created the LEM Benchmark Database (a project funded by the ISPRS Scientific Initiative project - 2017) from the Luiz Eduardo Magalhães (LEM) municipality, Bahia State, Brazil. The database contains a set of pre-processed multitemporal satellite images (Landsat-8/OLI, Sentinel-2/MSI and SAR band-C Sentinel-1) and shapefiles of agricultural fields with their correspondent monthly land use classes, covering the period of one Brazilian crop year (2017–2018). In this paper we present the first results obtained with this database.