Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives

Long-lasting precipitation deficits or heat waves can induce agricultural droughts, which are generally defined as soil moisture deficits that are severe enough to negatively impact vegetation. However, during short soil moisture drought events, the vegetation is not always negatively affected and sometimes even thrives. Due to this duality in agricultural drought impacts, the term “agricultural drought” is ambiguous. Using the ESA’s remotely sensed CCI surface soil moisture estimates and MODIS NDVI vegetation greenness data, we show that, in major European droughts over the past two decades, asynchronies and discrepancies occurred between the surface soil moisture and vegetation droughts. A clear delay is visible between the onset of soil moisture drought and vegetation drought, with correlations generally peaking at the end of the growing season. At lower latitudes, correlations peaked earlier in the season, likely due to an earlier onset of water limited conditions. In certain cases, the vegetation showed a positive anomaly, even during soil moisture drought events. As a result, using the term agricultural drought instead of soil moisture or vegetation drought, could lead to the misclassification of drought events and false drought alarms. We argue that soil moisture and vegetation drought should be considered separately.

Ambiguous Agricultural Drought: Characterising Soil Moisture and Vegetation Droughts in Europe from Earth Observation

Disentangling the relative effects of soil moisture and vapor pressure deficit on photosynthesis in dryland Central Asia

There are extensive and fragile mountain-oasis-desert ecosystems in Central Asia. Lakes are not only the most important water resources in arid Central Asia, but also important resources for maintaining the unique ecosystem of the region. As most lakes in Central Asia lack in situ monitoring data, and satellite altimetry records are relatively short-term, the continuous long-term monitoring of lake water storage change (LWSC) in Central Asia is inadequate. To this end, Shuttle Radar Topography Mission digital elevation model data were used in conjunction with all available Landsat images to construct an empirical model of area–elevation–storage changes and estimate the maximum LWSC (maxLWSC) and minimum LWSC (minLWSC) of 9,619 lakes (watershed area > 10 km2) in Central Asia from 1990 to 2020. Subsequently, the random forest (RF) algorithm was used to quantitatively evaluate the associations among climate change, human activities, and LWSC. It was revealed that maxLWSC and minLWSC decreased by 27.94 and 144.21 Gt, respectively. The results also revealed that LWSC varied by elevation, whereby lakes below 3,500 m shrunk and those above 3,500 m expanded. The RF findings demonstrated that human activities are more important than climate change for LWSC in Central Asia. Up to 96 % of LWSC can be explained by human activities. This study comprehensively evaluates the characteristics of LWSC in Central Asia, providing the latest data to help alleviate the contradiction between water supply and demand in Central Asia and resolve cross-border water resource disputes, so as to realize the protection of fragile regional ecosystems. 

Unravelling Lake Water Storage Change in Central Asia: Rapid Decrease in Tail-end Lakes and Increasing Risks to Water Supply

Water and wind erosion response to ecological restoration measures in China's drylands

Comparison of ESA climate change initiative land cover to CORINE land cover over Eastern Europe and the Baltic States from a regional climate modeling perspective

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Comparison of ESA climate change initiative land cover to CORINE land cover over Eastern Europe and the Baltic States from a regional climate modeling perspective