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Land cover (LC) is an important indicator to reach several of the targets under the Global Goals. Accurate global LC time-series are thus vital to monitor sustainable development. Although the number and quality of open-access, remotely sensed LC products is increasing, all products have uncertainties due to widespread classification errors. However, the relative magnitude of uncertainties among exiting LC products is largely unknown, which hampers their confident selection and robust use for sustainable development evaluation and planning. To close this gap, we quantified region-, time-period-, and coarse-LC class-specific data uncertainties for the 10 most widely used global LC time-series. To this end, we developed a novel multi-scale validation framework that accounts for differences in mapping resolutions and scale mismatches between the spatial extent of map grid cells and validation samples. We aimed for a fair validation assessment by carefully evaluating the quality of our validation samples with respect to landscape heterogeneity that LC products often fail to classify accurately. To address the issue, we supported the validation assessment with Landsat-based measures of cross-scale spectra similarity. The metric was computed by taking advantage of the full Landsat archive in Google Earth Engine. We base our assessment on more than 1.8 million globally integrated LC validation sites, where we mobilized around 2.8 million samples during the period 1980-2020 composed by hundreds of sampling effort of varied nature, from field surveys to crowdsourcing campaigns. Here, we will present the results of the assessment, providing insights on global and regional patterns of LC uncertainties. We found that no single product is more accurate over the others in mapping all LC classes, regions and time-periods. We will provide recommendations on the selection of fit-for-purpose LC time-series, and discuss future strategies for addressing their uncertainties in sustainable development evaluation and planning.

"1.Remote sensing data is a valuable tool in movement ecology. However, the way it's combined with animal movement data is far from optimal as most studies overlook differences in the spatial, temporal and thematic resolutions between both data types. 2.rsMove uses a pixel‐based approach to link animal tracking and remote sensing data that bridge the gap between these two disciplines while respecting the limitations of the latest. Additionally, if offers standardize methods to pre‐analyze the connection between animal movement and environmental change. 3.The package guides the choice of study sites, satellite data and environmental predictors though data mining and visualization tools. rsMove offers a simple methodology to analyze animal‐environment interactions that helps avoid unnecessary, time‐consuming satellite data processing through an informed decision process. 4.The package offers a basis for a better communication between ecologists and remote sensing experts contributing to the definition of clear remote sensing data requirements. Additionally, it provides future studies on animal movement tools for a more critical approach on the selection of environmental data supporting their reproducibility."

1. Visualizing movement data is challenging: While traditional spatial data can be sufficiently displayed as two‐dimensional plots or maps, movement trajectories require the representation of time in a third dimension. To address this, we present moveVis, an r package, which provides tools to animate movement trajectories, overlaying simultaneous uni‐ or multi‐temporal raster imagery or vector data.2. moveVis automates the processing of movement and environmental data to turn such into an animation. This includes (a) the regularization of movement trajectories enforcing uniform time instances and intervals across all trajectories, (b) the frame‐wise mapping of movement trajectories onto temporally static or dynamic environmental layers, (c) the addition of customizations, for example, map elements or colour scales and (d) the rendering of frames into an animation encoded as GIF or video file.3. moveVis is designed to display interactions and concurrencies of animal movement and environmental data. We present examples and use cases, ranging from data exploration to visualizing scientific findings.4. Static spatial plots of movement data disregard the temporal dimension that distinguishes movement from other spatial data. In contrast, animations allow to display relocation in both time and space. We deem animations a powerful way to visually explore movement data, frame analytical findings and display potential interactions with spatially continuous and temporally dynamic environmental covariates. published

Land cover is a key variable in the context of climate change. In particular, crop type information is essential to understand the spatial distribution of water usage and anticipate the risk of water scarcity and the consequent danger of food insecurity. This applies to arid regions such as the Aral Sea Basin (ASB), Central Asia, where agriculture relies heavily on irrigation. Here, remote sensing is valuable to map crop types, but its quality depends on consistent ground-truth data. Yet, in the ASB, such data are missing. Addressing this issue, we collected thousands of polygons on crop types, 97.7% of which in Uzbekistan and the remaining in Tajikistan. We collected 8,196 samples between 2015 and 2018, 213 in 2011 and 26 in 2008. Our data compile samples for 40 crop types and is dominated by “cotton” (40%) and “wheat”, (25%). These data were meticulously validated using expert knowledge and remote sensing data and relied on transferable, open-source workflows that will assure the consistency of future sampling campaigns., Measurement(s) crop type • land cover • Uncertainty • Normalized Difference Vegetation Index Technology Type(s) GPS navigation system • satellite imaging • computational modeling technique Factor Type(s) year of data collection • crop type label Sample Characteristic - Environment agricultural field Sample Characteristic - Location Uzbekistan • Tajikistan Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12613775

This paper presents an approach for retrieval of soil moisture content (SMC) from different satellite sensors with a focus on mountain areas. The novelties of the paper are: the extension of an already developed method to coarse resolution data (150 m) in mountain environment with high land heterogeneity, with only VV polarization and the proper selection of input features. During the result analysis, several algorithm characteristics were clearly identified: 1) the performances showed to be strongly related to input features such as topography and vegetation indices; 2) the algorithm needs a training phase; 3) the averaging window needs to be proper selected to take into account both the speckle noise and the characteristics of the area under investigation; and 4) the algorithm, being data driven, can be considered as site dependent. The experimental analysis is carried out on images acquired over the Sudtirol/Alto Adige Province in Italy during 2010–2011 from the RADARSAT2 and Envisat ASAR in Wide Swath mode. SMC maps were compared with spatially distributed ground measurements, resulting in a root mean squared error (RMSE) value ranging from 0.045 to $0.07\;{{\rm m}^3}/{{\rm m}^3}$ . Concerning the multiscale analysis, the results indicated that RADARSAT2 maps are able to detect the spatial heterogeneity and soil moisture dynamics at local scale, while ASAR WS SMC maps are able to identify mainly the two main classes of pasture and meadows. When these estimates are compared with SMC values from meteorological stations a RMSE value of $0.10\;{{\rm m}^3}/{{\rm m}^3}$ for both satellites indicated a reduced capability to follow the temporal dynamics.

In the last few years, in several parts of the world, under extreme weather conditions, several catastrophic fires have taken place. These fires are called mega-fires. They are not just large fires but extreme events in their behavior, difficulty to control and impacts. Portugal historically has little experience with mega-fires, but recent fire events exacerbated by climate change and landscape management has changed this situation. In the year 2003, the worst and largest wildfires were ever recorded in the Algarve region and in the central region of Portugal. However, this paper focuses on the Algarve region, using primary (i.e. questionnaires, interviews, satellite images, and fieldwork) and secondary data (i.e. Portuguese wildfire database, central and local governments’ fire damages reports). Using wildfire ecological and socio-economic impacts as an important criterion to classify an event as a mega-fire, this paper conclude that two mega-fires occurred in 2003 in the Algarve Region. Taking into account the weakness in Portuguese wildfire impacts reporting process, it is proposed a checklist of categories of damages and losses to support future data acquisition. Considering that mega-fires are a new reality in Portugal, this paper shows that increasing suppression capabilities in terms of means, readiness and effectiveness are important to avoid small fires to become mega-fires in days of critical fire danger. Nevertheless, facing the impossibility of changing the extreme weather conditions, given the complexity and uncertainty surrounding mega-fires occurrence and severity, the main challenge is to improve wildfire risk reduction instead of focusing only on fire suppression. This paper proposes a framework which conceptualizes fire impacts as a result of interaction between fire behavior, vulnerability and suppression capabilities. It can be used for promoting wildfire prevention and mitigation. It is urgent to enhance prevention measures in order to change fire behavior and reduce vulnerability of ecosystems and societies, as well as to increase the engagement of communities in wildfire risk management.

Portugal is a European country with highest forest fires density and burned area. Since beginning of official forest fires database in 1980, an increase in number of fires and burned area as well as appearance of large and catastrophic fires have characterized fire activity in Portugal. In 1980s, the largest fires were just a little bit over 10,000 ha. However, in the beginning of 21st century several fires occurred with a burned area over 20,000 ha. Some of these events can be classified as mega-fires due to their ecological and socioeconomic severity. The present study aimed to discuss the characterization of large forest fires trend, in order to understand if the largest fires that occurred in Portugal were exceptional events or evidences of a new trend, and the constraints of fire size to characterize fire effects because, usually, it is assumed that larger the fire higher the damages. Using Portuguese forest fire database and satellite imagery, the present study showed that the largest fires could be seen at the same time as exceptional events and as evidence of a new fire regime. It highlighted the importance of size and patterns of unburned patches within fire perimeter as well as heterogeneity of fire ecological severity, usually not included in fire regime description, which are critical to fire management and research. The findings of this research can be used in forest risk reduction and suppression planning.

On mountain areas, one main important pre-processing step for optical satellite imagery is the topographic correction. The illumination condition strongly depends on the area topography, sun elevation and azimuth and acquisition geometry of the sensor. These elements generate unequal light distribution over the observed surface. This effect needs to be corrected to improve classification and parameters retrieval performances. In this paper, a novel empirical approach for topographic correction is presented using the combined (direct and diffuse) solar radiation. The combined solar radiation has the advantage to take into account the brightening effect of topography on scene radiance. The approach aims at defining correction coefficients depending on the different land cover. We tested our approach using both high and medium resolution images and terrain effects in both data sets were considerably reduced.