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This study aimed to describe the interannual climate variability in the West Antarctic Peninsula (WAP) under austral summer conditions. Time series of January sea-surface temperature (SST) at 1 km spatial resolution from satellite-based multi-sensor data from Moderate Resolution Imaging Spectrometer (MODIS) Terra, MODIS Aqua, and Visible Infrared Imager Radiometer Suite (VIIRS) were compiled between 2001 and 2020 at localities near the Gerlache Strait and the Carlini, Palmer, and Rothera research stations. The results revealed a well-marked spatial-temporal variability in SST at the WAP, with a one-year warm episode followed by a five-year cold episode. Warm waters (SST > 0 °C) reach the coast during warm episodes but remain far from the shore during cold episodes. This behavior of warm waters may be related to the regional variability of the Antarctic Circumpolar Current, particularly when the South Polar Front (carrying warm waters) reaches the WAP coast. The WAP can be divided into two zones representing two distinct ecoregions: the northern zone (including the Carlini and Gerlache stations) corresponds to the South Shetland Islands ecoregion, and the southern zone (including the Palmer and Rothera stations) corresponds to the Antarctic Peninsula ecoregion. The Gerlache Strait is likely situated on the border between the two ecoregions but under a greater influence of the northern zone. Our data showed that the Southern Annular Mode (SAM) is the primary driver of SST variability, while the El Niño Southern Oscillation (ENSO) plays a secondary role. However, further studies are needed to better understand regional climate variability in the WAP and its relation with SAM and ENSO; such studies should use an index that adequately describes the ENSO in these latitudes and addresses the limitations of the databases used for this purpose. Multi-sensor data are useful in describing the complex climate variability resulting from the combination of local and regional processes that elicit different responses across the WAP. It is also essential to continue improving SST approximations at high latitudes.

Agricultural land abandonment is an increasing problem in Europe. The Comunitat Valenciana Region (Spain) is one of the most important citrus producers in Europe suffering this problem. This region characterizes by small sized citrus plots and high spatial fragmentation which makes necessary to use Very High-Resolution images to detect abandoned plots. In this paper spectral and Gray Level Co-Occurrence Matrix (GLCM)-based textural information derived from the Normalized Difference Vegetation Index (NDVI) are used to map abandoned citrus plots in Oliva municipality (eastern Spain). The proposed methodology is based on three general steps: (a) extraction of spectral and textural features from the image, (b) pixel-based classification of the image using the Random Forest algorithm, and (c) assignment of a single value per plot by majority voting. The best results were obtained when extracting the texture features with a 9 × 9 window size and the Random Forest model showed convergence around 100 decision trees. Cross-validation of the model showed an overall accuracy of the pixel-based classification of 87% and an overall accuracy of the plot-based classification of 95%. All the variables used are statistically significant for the classification, however the most important were contrast, dissimilarity, NIR band (720 nm), and blue band (620 nm). According to our results, 31% of the plots classified as citrus in Oliva by current methodology are abandoned. This is very important to avoid overestimating crop yield calculations by public administrations. The model was applied successfully outside the main study area (Oliva municipality); with a slightly lower accuracy (92%). This research provides a new approach to map small agricultural plots, especially to detect land abandonment in woody evergreen crops that have been little studied until now.

Agricultural land abandonment is an important environmental issue in Europe. The proper management of agricultural areas has important implications for ecosystem services (food production, biodiversity, climate regulation and the landscape). In the coming years, an increase of abandoned areas is expected due to socio-economic changes. The identification and quantification of abandoned agricultural plots is key for monitoring this process and for applying management measures. The Valencian Region (Spain) is an important fruit and vegetable producing area in Europe, and it has the most important citrus industry. However, this agricultural sector is highly threatened by diverse factors, which have accelerated land abandonment. Landsat and MODIS satellite images have been used to map land abandonment. However, these images do not give good results in areas with high spatial fragmentation and small-sized agricultural plots. Sentinel-2 and airborne imagery shows unexplored potential to overcome this thanks to higher spatial resolutions. In this work, three models were compared for mapping abandoned plots using Sentinel-2 with 10 m bands, Sentinel-2 with 10 m and 20 m bands, and airborne imagery with 1 m visible and near-infrared bands. A pixel-based classification approach was used, applying the Random Forests algorithm. The algorithm was trained with 144 plots and 100 decision trees. The results were validated using the hold-out method with 96 independent plots. The most accurate map was obtained using airborne images, the Enhanced Vegetation Index (EVI) and Thiam’s Transformed Vegetation Index (TTVI), with an overall accuracy of 88.5%. The map generated from Sentinel-2 images (10 m bands and the EVI and TTVI spectral indices) had an overall accuracy of 77.1%. Adding 20 m Sentinel-2 bands and the Normalized Difference Moisture Index (NDMI) did not improve the classification accuracy. According to the most accurate map, 4310 abandoned plots were detected in our study area, representing 32.5% of its agricultural surface. The proposed methodology proved to be useful for mapping citrus in highly fragmented areas, and it can be adapted to other crops.