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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. In the present study, we performed a systematic sensitivity analysis using various scenarios. The LINTUL-5 crop model implemented in the SIMPLACE modeling platform was applied during the period 2001–2016 to simulate maize yields across four provinces in South Africa using previously defined scenarios of sowing dates. As expected, the selected methodology and the selected threshold considerably influenced the estimated sowing dates (up to 51 days) and resulted in differences in the long-term mean maize yield reaching up to 1.7 t ha−1 (48% of the mean yield) at the province level. Using RS-derived sowing date estimations resulted in a better representation of the yield variability in space and time since the use of RS information not only relies on precipitation but also captures the impacts of socioeconomic factors on the sowing decision, particularly for smallholder farmers. The model was not able to reproduce the observed yield anomalies in Free State (Pearson correlation coefficient: 0.16 to 0.23) and Mpumalanga (Pearson correlation coefficient: 0.11 to 0.18) in South Africa when using fixed and precipitation rule-based sowing date estimations. Further research with high-resolution climate and soil data and ground-based observations is required to better understand the sources of the uncertainties in RS information and to test whether the results presented herein can be generalized among crop models with different levels of complexity and across distinct field crops.

Wetlands are abundant across the African continent and provide a range of ecosystem services on different scales but are threatened by overuse and degradation. It is essential that national governments enable and ensure the sustainable use of wetland resources to maintain these services in the long run. As informed management decisions require reliable, up-to-date, and large coverage spatial data, we propose a modular Earth observation-based framework for the geo-localisation and characterization of wetlands in East Africa. In this study, we identify four major challenges in spatial data supported wetland management and present a framework to address them. We then apply the framework comprising Wetland Delineation, Surface Water Occurrence, Land Use/Land Cover classification and Wetland Use Intensity for the whole of Rwanda and evaluate the ability of these layers to meet the identified challenges. The layers’ spatial and temporal characteristics make them combinable and the information content, of each layer alone as well as in combination, renders them useful for different wetland management contexts.

Explicit and large-scale information on farming systems is important for many applications such as crop production estimates, drought impact assessments or water footprint analysis. This contributi...

This study presents a new methodology for spatially explicit and globally applicable drought hazard, vulnerability and risk modelling. We focused on agricultural droughts since this sector affects the food security and livelihood situation of the often most vulnerable communities especially in developing countries. Despite recent advances in drought modeling, coherent and spatially explicit information on drought hazard, vulnerability is still lacking over wider areas. In this study a spatially explicit inter-operational drought hazard, vulnerability and risk modeling framework was investigated for agricultural land, grassland and shrubland areas. The developed drought hazard model operates on a higher spatial resolution than most available global drought models while also being scalable to other regions. Initially, a logistic regression model was developed to predict drought hazard for rangelands and cropland in the USA. The model results showed a good spatiotemporal agreement within the cross-verification with the United States Drought Monitor (USDM), using visual interpretation. Subsequently, the explicit and accurate drought hazard model was transferred and calibrated for South Africa and Zimbabwe, where a simplified drought risk indicator was calculated by the combination of drought hazard and drought vulnerability. The drought hazard model used time series crop yields data from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and biophysical predictors from satellite remote sensing (SPI, NDII, NDVI, LST, albedo). The McFadden’s Pseudo R² value of 0.17 indicated a good model fit for drought hazard in South Africa. Additionally, the plausibility of the model results in Southern Africa was evaluated by using regional climate patterns, published drought reports and through visual comparison to a global drought risk model and food security classification data. Drought risk and vulnerability were also assessed for Southern Africa and could be mapped in a spatially explicit manner, showing, for example, lower drought risk and vulnerability over irrigated areas. This developed modeling framework can be applied globally, since it uses globally available datasets and therefore can be easily modified to account for country-specific conditions. Additionally, it can also capture regional drought patterns on a higher spatial resolution than other existing global drought models. This model addressed the gap between global drought models, that cannot accurately capture regional droughts, and sub-regional models that may be spatially explicit but not spatially coherent. The approach of this study can potentially be used to identify risk and priority areas and possibly in an early warning capacity while enhancing existing drought monitoring routines, drought intervention strategies and the implementation of preparedness measures.