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This work estimates yield losses due to drought events in the mountain grasslands in north-eastern Italy, laying the groundwork for index-based insurance. Given the high correlation between the leaf area index (LAI) and grassland yield, we exploit the LAI as a proxy for yield. We estimate the LAI by using the Sentinel-2 biophysical processor and compare different gap-filling methods, including time series interpolation and fusion with Sentinel-1 SAR data. We derive the grassland production index (GPI) as the growing season cumulate of the daily product between the LAI and a meteorological water stress coefficient. Finally, we calculate the drought index as an anomaly of the GPI. The validation of the Sentinel-2 LAI with ground measurements showed an RMSE of 0.92 [m2 m−2] and an R2 of 0.81 over all the measurement sites. A comparison between the GPI and yield showed, on average, an R2 of 0.56 at the pixel scale and an R2 of 0.74 at the parcel scale. The developed prototype GPI index was used at the end of the growing season of the year 2022 to calculate the payments of an experimental insurance scheme which was proposed to a group of farmers in Trentino-South Tyrol.

In the Alps, understanding how climate change is affecting evapotranspiration (ET) is relevant due to possible implications on water availability for large lowland areas of Europe. Here, changes in ET were studied based on 20 years of MODIS data. MOD16 and operational Simplified Surface Energy Balance (SSEBop) products were compared with eddy-covariance data and analyzed for trend detection. The two products showed a similar relationship with ground observations, with RMSE between 0.69 and 2 mm day−1, and a correlation coefficient between 0.6 and 0.83. A regression with the potential drivers of ET showed that, for climate variables, ground data were coherent with MOD16 at grassland sites, where r2 was 0.12 for potential ET, 0.17 for precipitation, and 0.57 for air temperature, whereas ground data agreed with SSEBop at forest sites, with an r2 of 0.46 for precipitation, no correlation with temperature, and negative correlation with potential ET. Interestingly, ground-based correlation corresponded to SSEBop for leaf area index (LAI), while it matched with MOD16 for land surface temperature (LST). Through the trend analysis, both MOD16 and SSEBop revealed positive trends in the south-west, and negative trends in the south and north-east. Moreover, in summer, positive trends prevailed at high elevations for grasslands and forests, while negative trends dominated at low elevations for croplands and grasslands. However, the Alpine area share with positive ET trends was 16.6% for MOD16 and 3.9% for SSEBop, while the share with negative trends was 1.2% for MOD16 and 15.3% for SSEBop. A regression between trends in ET and in climate variables, LST, and LAI indicated consistency, especially between ET, temperature, and LAI increase, but low correlation. Overall, the discrepancies in the trends, and the fact that none of the two products outperformed the other when compared to ground data, suggest that, in the Alps, SSEBop and MOD16 might not be accurate enough to be a robust basis to study ET changes.

Droughts during the growing season are projected to increase in frequency and severity in Central Europe in the future. Thus, area-wide monitoring of agricultural drought in this region is becoming more and more important. In this context, it is essential to know where and when vegetation growth is primarily water-limited and whether remote sensing-based drought indices can detect agricultural drought in these areas. To answer these questions, we conducted a correlation analysis between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) within the growing season from 2001 to 2020 in Bavaria (Germany) and investigated the relationship with land cover and altitude. In the second step, we applied the drought indices Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI) to primarily water-limited areas and evaluated them with soil moisture and agricultural yield anomalies. We found that, especially in the summer months (July and August), on agricultural land and grassland and below 800 m, NDVI and LST are negatively correlated and thus, water is the primary limiting factor for vegetation growth here. Within these areas and periods, the TCI and VHI correlate strongly with soil moisture and agricultural yield anomalies, suggesting that both indices have the potential to detect agricultural drought in Bavaria.

In this study, we evaluated three different downscaling approaches to enhance spatial resolution of thermal imagery over Alpine vegetated areas. Due to the topographical and land-cover complexity and to the sparse distribution of meteorological stations in the region, the remotely-sensed land surface temperature (LST) at regional scale is of major area of interest for environmental applications. Even though the Moderate Resolution Imaging Spectroradiometer (MODIS) LST fills the gap regarding high temporal resolution and length of the time-series, its spatial resolution is not adequate for mountainous areas. Given this limitation, random forest algorithm for downscaling LST to 250 m spatial resolution was evaluated. This study exploits daily MODIS LST with a spatial resolution of 1 km to obtain sub-pixel information at 250 m spatial resolution. The nonlinear relationship between coarse resolution MODIS LST (CR) and fine resolution (FR) explanatory variables was performed by building three different models including: (i) all pixels (BM), (ii) only pixels with more than 90% of vegetation content (EM1) and (iii) only pixels with 75% threshold of homogeneity for vegetated land-cover classes (EM2). We considered normalized difference vegetation index (NDVI) and digital elevation model (DEM) as predictors. The performances of the thermal downscaling methods were evaluated by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) between the downscaled dataset and Landsat LST. Validation indicated that the error values for vegetation fraction (EM1, EM2) were smaller than for basic modelling (BM). BM model determined averaged RMSE of 2.3 K and MAE of 1.8 K. Enhanced methods (EM1 and EM2) gave slightly better results yielding 2.2 K and 1.7 K for RMSE and MAE, respectively. In contrast to the EMs, BM showed a reduction of 22% and 18% of RMSE and MAE respectively with regard to Landsat and the original MODIS LST. Despite some limitations, mainly due to cloud contamination effect and coarse resolution pixel heterogeneity, random forest downscaling exhibits a large potential for producing improved LST maps.