Maize yield prediction and condition monitoring at the sub-county scale in Kenya: synthesis of remote sensing information and crop modeling.

Agricultural production assessments are crucial for formulating strategies for closing yield gaps and enhancing production efficiencies. While in situ crop yield measurements can provide valuable and accurate information, such approaches are costly and lack scalability for large-scale assessments. Therefore, crop modeling and remote sensing (RS) technologies are essential for assessing crop conditions and predicting yields at larger scales.

Drought risk for agricultural systems in South Africa: Drivers, spatial patterns, and implications for drought risk management.

The regular drought episodes in South Africa highlight the need to reduce drought risk by both policy and local community actions. Environmental and socioeconomic factors in South Africa's agricultural system have been affected by drought in the past, creating cascading pressures on the nation's agro-economic and water supply systems. Therefore, understanding the key drivers of all risk components through a comprehensive risk assessment must be undertaken in order to inform proactive drought risk management.

Land-use changes in Northern Ukraine: patterns and dynamics of illegal amber mining during 1986-2016.

Political instability and economic crises may trigger informal, unsustainable and often illegal land use, leading to land degradation. One of the most recent and striking examples of such a phenomenon within Europe is demonstrated by the Polissia region in Northern Ukraine, famous for its amber deposits. Amid severe political disturbances and subsequent economic recession in Ukraine in 2014 and 2015, amber mining flourished in the region.

The use of remote sensing to derive maize sowing dates for large-scale crop yield simulations.

One of the major sources of uncertainty in large-scale crop modeling is the lack of information capturing the spatiotemporal variability of crop sowing dates. Remote sensing can contribute to reducing such uncertainties by providing essential spatial and temporal information to crop models and improving the accuracy of yield predictions. However, little is known about the impacts of the differences in crop sowing dates estimated by using remote sensing (RS) and other established methods, the uncertainties introduced by the thresholds used in these methods, and the sensitivity of simulated crop yields to these uncertainties in crop sowing dates.

Drought hazard in Kazakhstan in 2000-2016: a remote sensing perspective.

Droughts have significant negative impacts on livelihoods and economy of Kazakhstan. In this study, we assessed and characterized drought hazard events in Kazakhstan using satellite Remote Sensing time series for the period between 2000 and 2016. First, we calculated Vegetation Condition Index (VCI) and Standardized Enhanced Vegetation Index anomalies (Z) based on 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series.