Cropland expansion is a common strategy for boosting agricultural production in sub-Saharan Africa (SSA) even though it often leads to economic, environmental, and social trade-offs. Ensuring sustainable cropland use and their management is critical for improving food security and preserving ecosystem services. To develop policies and approaches that support sustainable cropland management at national and sub-national scales, there is a need to understand the spatial distribution of cropland expansion (/loss), and any resultant changes in cropland productivity. This is especially important in SSA countries such as Malawi, where spatially explicit assessments of changes in cropland area and cropland productivity are lacking. To address this gap in Malawi, we used multi-source satellite data and socio-economic data, combined with satellite image classification and trend analysis, firstly to quantify spatial changes in cropland area and productivity, and secondly to evaluate potentially available cropland for future expansion. We found evidence of unsustainable cropland use in Malawi, which was demonstrated by: (a) rapid cropland expansion between 2010 and 2019 (increase 8.5% of land area), characterized by an expansion of crop farming into upland areas which indicate increased land scarcity in Malawi; (b) limited potential for future expansion, as approximately only 5% of the total land remained as potentially available cropland (corresponding to 4671 000 ha); and (c) an overall reduction in cropland productivity and a prevalence of increase in soil erosion. Our findings underscore the urgent need for taking measures to promote sustainable cropland use, including by protecting current cropland from further degradation (e.g. Southern Malawi) and improving cropland use planning (e.g. Northern Malawi).

Accurate information on flood extent and exposure is critical for disaster management in data-scarce, vulnerable regions, such as Sub-Saharan Africa (SSA). However, uncertainties in flood extent affect flood exposure estimates. This study developed a framework to examine the spatiotemporal pattern of floods and to assess flood exposure through utilization of satellite images, ground-based participatory mapping of flood extent, and socio-economic data. Drawing on a case study in the White Volta basin in Western Africa, our results showed that synergetic use of multi-temporal radar and optical satellite data improved flood mapping accuracy (77% overall agreement compared with participatory mapping outputs), in comparison with existing global flood datasets (43% overall agreement for the moderate-resolution imaging spectroradiometer (MODIS) Near Real-Time (NRT) Global Flood Product). Increases in flood extent were observed according to our classified product, as well as two existing global flood products. Similarly, increased flood exposure was also observed, however its estimation remains highly uncertain and sensitive to the input dataset used. Population exposure varied greatly depending on the population dataset used, while the greatest farmland and infrastructure exposure was estimated using a composite flood map derived from three products, with lower exposure estimated from each flood product individually. The study shows that there is considerable scope to develop an accurate flood mapping system in SSA and thereby improve flood exposure assessment and develop mitigation and intervention plans.

Mapping plants traits on the Qinghai-Tibetan Plateau grassland is important for understanding ecosystem functions and how plants respond to global change. Detailed trait maps for the complete Qinghai-Tibetan Plateau are missing. Here, we addressed this issue by combining Sentinel-2 and Landsat-8 multispectral satellite data with field measurements to map and compare plant traits of meadow and steppe communities across the complete Qinghai-Tibetan Plateau. We measured in-situ plant-level traits of CHLorophyll content (CHL), specific plant area (SPA = plant area / plant dry mass), and plant dry matter content (PDMC = plant dry mass / fresh mass). We hypothesized that plant-level traits of SPA and PDMC are close to community-weighted means (CWMs) of specific leaf area (SLA) and leaf dry matter content (LDMC) because leaves represent the largest fraction of aboveground biomass in the Qinghai-Tibetan Plateau grasslands. Despite vastly different measurement methods, we found that the remotely sensed traits (SPA and PDMC) correlated with literature-derived leaf traits of CWMs of SLA and LDMC. Both remotely sensed and field-measured results showed that alpine meadow plants reveal a wider range and higher averages of CHL and SPA but lower PDMC compared with alpine steppe plants. These trait differences between vegetation types indicate faster growth of alpine meadow and higher resilience to harsh conditions of alpine steppe, representing differences in adaptation strategies to environmental conditions. Our study demonstrates that remote sensing can be used to estimate plant traits in alpine grasslands with potential applications to retrieve functional diversity and correlated ecosystem functions in future studies.

Satellite data provide high potential for estimating crop yield, which is crucial to understanding determinants of yield gaps and therefore improving food production, particularly in sub-Saharan Africa (SSA) regions. However, accurate assessment of crop yield and its spatial variation is challenging in SSA because of small field sizes, widespread intercropping practices, and inadequate field observations. This study aimed to firstly evaluate the potential of satellite data in estimating maize yield in intercropped smallholder fields and secondly assess how factors such as satellite data spatial and temporal resolution, within-field variability, field size, harvest index and intercropping practices affect model performance. Having collected in situ data (field size, yield, intercrops occurrence, harvest index, and leaf area index), statistical models were developed to predict yield from multisource satellite data (i.e., Sentinel-2 and PlanetScope). Model accuracy and residuals were assessed against the above factors. Among 150 investigated fields, our study found that nearly half were intercropped with legumes, with an average plot size of 0.17 ha. Despite mixed pixels resulting from intercrops, the model based on the Sentinel-2 red-edge vegetation index (VI) could estimate maize yield with moderate accuracy (R² = 0.51, nRMSE = 19.95%), while higher spatial resolution satellite data (e.g., PlanetScope 3 m) only showed a marginal improvement in performance (R² = 0.52, nRMSE = 19.95%). Seasonal peak VI values provided better accuracy than seasonal mean/median VI, suggesting peak VI values may capture the signal of the dominant upper maize foliage layer and may be less impacted by understory intercrop effects. Still, intercropping practice reduces model accuracy, as the model residuals are lower in fields with pure maize (1 t/ha) compared to intercropped fields (1.3 t/ha). This study provides a reference for operational maize yield estimation in intercropped smallholder fields, using free satellite data in Southern Malawi. It also highlights the difficulties of estimating yield in intercropped fields using satellite imagery, and stresses the importance of sufficient satellite observations for monitoring intercropping practices in SSA.