Salt pans are unique wetland ecosystems. In the Austrian Seewinkel region, salt pans are in an increasingly vulnerable state due to groundwater drainage and heightened climatic pressures. It is crucial to model how seasonal and long-term hydrological and climatological variations affect the salt pan dynamics in Seewinkel, yet a comprehensive understanding of the driving processes is lacking. The goal of this study is to develop random forest machine learning models driven by hydrological and meteorological data that allow us to predict in early spring (March) of each year the inundation state in the subsequent summer and fall. We utilize Earth observation data from Landsat 5 (L5), 8 (L8), and 9 (L9) to derive the time series of the inundation state for 34 salt pans for the period 1984–2022. Furthermore, we demonstrate that the groundwater level observed in March is the strongest predictor of the salt pan inundation state in summer and fall. Utilizing local groundwater data yields a Matthews correlation coefficient of 0.59. Models using globally available meteorological data, either instead of or in addition to groundwater data, provide comparable results. This allows the global transfer of the approach to comparable ecosystems where no in situ data are available.

Surface soil moisture (SSM) has proven to be an important variable for the yield prediction of main crops like maize and wheat, but its value for spring barley, the third most cultivated crop in Europe, has not yet been evaluated. This study assesses how much of spring barley yield variability can be explained by the commonly used model and satellite-based global SSM products ERA5 SWVL1 and H SAF. A Feed Forward Neural Network, SSM time series, and reference yield data are used to predict spring barley yield at NUTS level for Austria, Czechia, and Germany. A random train-test split is used to assess the explained variability and a cross-validation at the NUTS level for the spatial evaluation. The results indicate the following: (1) ERA5 SWVL1 achieved an R² of 0.37, H SAF an R² of 0.33; (2) Both products achieved the lowest RMSE and MAE in Czechia, high RMSE and MAE values are observed in Eastern Germany. (3) ERA5 SWVL1 performed better in areas with low sensitivity for microwaves like the Alpine region, but both products achieved similar results in 80% of the NUTS regions. These findings contribute to better utilization of SSM and more accurate yield predictions for spring barley and similar crops.

The increasing frequency and intensity of severe droughts over recent decades have significantly impacted crop production in the Pannonian Basin in southeastern Europe. Related crop yield losses can be substantial and require logistic compensation on an international level. To plan such compensations, seasonal crop yield forecasts have proven to be a valuable tool to support decision-makers in taking timely action. However, the impact of severe droughts on crop yields is often underestimated by such forecasts. To address this issue, we developed a maize and wheat yield forecasting system based on extreme-gradient-boosting machine learning for 42 regions in the Pannonian Basin. The used predictors describe vegetation state, weather, and soil moisture conditions derived from Earth observation, reanalysis, in-situ data, and seasonal weather forecasts. The wide range of predictors was selected to represent the state of the crops and the conditions they are facing and are expected to face. We expected it to be crucial, especially during severe drought years, to provide the model with sufficient information about the drought and its impacts. Afterwards, the model was validated, with a focus on drought years. Our results show that crop yield anomaly estimates in the two months preceding harvest have better performance than earlier in the year (relative root mean square errors below 17%) in all years. The models have their clear strength in forecasting interannual variabilities but struggle to forecast differences between regions within individual years. This is related to spatial autocorrelations and a lower spatial than temporal variability of crop yields. In years of severe droughts, there is a clear improvement in the forecasts with a 2-month lead time over longer forecasts too. The crop yield losses remain underestimated, but the wheat model performs in drought years better than for average years with errors below 12%. The errors of the maize forecasts in drought years are larger than for non-drought years: 30% two months ahead and 20% one month ahead. The feature importance analysis shows that in general wheat yield anomalies are controlled by temperature and maize by water availability during the last two months before harvest. In severe drought years, soil moisture is the most important predictor for the maize model and the seasonal temperature forecast becomes key for wheat forecasts two months before harvest. Going forward, a finer spatial resolution of the predictors will be tested to better distinguish the yields between the different regions. In addition, longer time-series of crop yield data, including more data during severe drought years, will help to test the findings of this study.

Monitoring drought helps to reduce their economic and environmental impacts by enabling early warnings and better resource management planning. In Europe, there are several operational monitoring systems operating at national and regional scales. However, such monitoring systems are rarely validated, which complicates the decision-making process. Therefore, we evaluated six national drought monitoring products in Central Europe using a novel extreme event impact database compiled from national newspaper reports over the period 2000–2023. The drought monitoring indices used in the countries include the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized relative soil moisture with different aggregation periods. The area under receiver-operating characteristic curve (AUC) is used to assess the ability of the drought indices to detect impact occurrence. Spearman correlation coefficients (r) between the severity of the drought index and the number of reported impacts are used to assess their ability to capture impact severity. The highest AUC values were obtained for the drought monitoring products of Czechia, Croatia, and Slovenia ( $$AUC>0.8$$ A U C > 0.8 ) while the lowest values were obtained for the monitoring product of Austria ( $$AUC<0.7$$ A U C < 0.7 ). Impact severity was best captured in Poland (for some indices $$r>0.6$$ r > 0.6 ), and worst in Slovakia, Slovenia, and Austria ( $$r<0.4$$ r < 0.4 ). With an increasing aggregation period, the correlation generally decreases, while the AUC values show a non-linear pattern, peaking at an intermediate integration time of three to 6 months. The results of this study help to understand the strengths and weaknesses of drought monitoring products in each country and support the development of a common drought monitoring framework for Central Europe.

Climate change is threatening food security. To ensure food security, we do not only have to safeguard agricultural production - crop yields also need to be optimally distributed. For that, decision-makers need reliable crop forecasts so that they can plan which regions are likely to experience crop yield losses and which regions will produce a surplus. Earth observation and machine learning are key tools to calculate such forecasts. However, extreme crop yield losses, for example caused by severe droughts, are often underestimated. To test this, we developed a machine learning-based crop yield anomaly forecasting system for the Pannonian Basin and examined its performance, with a focus on drought years. We trained the model (XGBoost) with crop yield data from 43 regions in southeastern Europe and predictors describing soil moisture, vegetation, and meteorological conditions. Maize and winter wheat yield anomalies were forecasted with different lead times (zero to three months) before the harvesting season. Our results show that the crop yield forecasts are significantly more reliable from 2 months before the harvest than before in both, drought and non-drought years. The models have their clear strength in forecasting interannual variabilities but struggle to forecast differences between regions within individual years. This is related to spatial autocorrelations and a lower spatial than temporal variability of crop yields. In years of severe droughts, the wheat yield losses remain underestimated, but the maize forecasts are fairly accurate. The feature importance analysis shows that in general wheat yield anomalies are controlled by temperature and maize by water availability during the last two months before harvest. In severe drought years, soil moisture is the most important predictor for the maize model and the seasonal temperature forecast becomes key for wheat forecasts two months before harvest.