The objective of this study is to evaluate the ability of soil physical characteristics (i.e., texture and moisture conditions) to better understand the breeding conditions of desert locust (DL). Though soil moisture and texture are well-known and necessary environmental conditions for DL breeding, in this study, we highlight the ability of model-derived soil moisture estimates to contribute towards broader desert locust monitoring activities. We focus on the recent DL upsurge in East Africa from October 2019 though June 2020, utilizing known locust observations from the United Nations Food and Agriculture Organization (FAO). We compare this information to results from the current literature and combine the two datasets to create “optimal thresholds” of breeding conditions. When considering the most optimal conditions (all thresholds met), the soil texture combined with modeled soil moisture content predicted the estimated DL egg-laying period 62.5% of the time. Accounting for the data errors and uncertainties, a 3 × 3 pixel buffer increased this to 85.2%. By including soil moisture, the areas of optimal egg laying conditions decreased from 33% to less than 20% on average.

Kenya is highly dependent on precipitation for both food and water security. Farmers and pastoralists rely on rain to provide water for crops and vegetation to feed herds. As such, precipitation forecasts can be useful tools to inform decision makers and potentially allow the preparation for such events as drought. This study assessed the predictability of a seasonal forecast (CFSv2) and a short-term precipitation forecast (CHIRPS-GEFS) over Kenya. The short-term forecast was assessed on its ability to predict the onset date of the rainy season, and the skill of the seasonal forecast in predicting abnormal precipitation patterns. CHIRPS-GEFS provided a useful starting point to estimate the onset date, but during the long rains in the southwest, where agriculture is concentrated, differences between the predicted and actual onset dates were large (over 20 days). Assessments for CFSv2 generally displayed lower forecast skill over highlands and coastal regions at a seasonal scale. The CFSv2 forecast skill varied widely over individual months and lead times, but over whole rainy seasons, CFSv2 was more skillful than a random forecast at all lead times in the major agricultural areas of Kenya. This research fills a critical research and application gap in understanding the forecast precipitation skill for onset and sub-seasonal prediction.

In arid and semi-arid regions of Eastern and Southern Africa, drought can be devastating to pastoralists who depend on healthy vegetation for their herds. The Kenya Livestock Insurance Program (KLIP) addresses this challenge through its insurance program that relies on a vegetation index product derived from eMODIS NDVI (enhanced Normalized Difference Vegetation Index). Insurance payouts are triggered when index values fall below a certain threshold for a Unit Area of Insurance (UAI). The objective of this study is to produce an updated, cloud-based NDVI product, potentially allowing for earlier payouts that may help herders to prevent, minimize, or offset drought-induced losses. The new product, named reNDVI (rapid enhanced NDVI), provides an updated cloud filtering algorithm and brings the entire processing chain to the cloud. Access to the scripts used for the processing described and resulting data is openly available. To test the performance of the new product, we provide a robust evaluation of reNDVI and eMODIS NDVI and their derived payout indices against historical drought, payouts provided, and mortality data. The implications of potential payout differences are also discussed. The products show good comparability; the monthly average NDVI per UAI has correlation values over 0.95 and MAPD under 5% for most UAIs. However, there are moderate differences when assessing year-to-year payout amounts triggered. Because the payouts are currently calculated based on the 20th and first percentile of index values from 2003–2016, payouts are very sensitive to even small changes in NDVI. Where livestock mortality was available, payouts for reNDVI and eMODIS had similar correlations (r = 0.453 and r = 0.478, respectively) with mortality rates. Therefore, with the potential reduced latency and updated cloud filtering, the reNDVI product could be a suitable replacement for eMODIS in the Kenya Livestock Insurance Program. The updated reNDVI product shows promise as a vegetation index that could address a pressing drought insurance challenge.

Understanding where forest loss occurs and the resulting carbon emissions is a critical component of assessing national carbon budgets. To complement existing greenhouse gas (GHG) guidance and evaluate input datasets used in emissions estimation, SERVIR—a joint USAID and NASA initiative—implemented the SERVIR CArbon Pilot (S-CAP) project. This study focuses on the variability and reliability of land cover and biomass datasets that serve as inputs for such calculations. Seventeen aboveground biomass and twelve land cover change datasets were analyzed to characterize the variability in forest cover loss and biomass estimates for Guatemala, Nepal, and Zambia. Forest loss estimates varied substantially, ranging from 20,733 to 441,227 ha/yr in Guatemala, 1738 to 385,087 ha/yr in Nepal, and 6141 to 1,902,957 ha/yr in Zambia. Biomass estimates also differed widely depending on the dataset and forest mask applied: mean values ranged from 54.6 to 293.3 tons/ha across countries and periods. Accuracy assessments using national reference data for forest changes ranged from 67 to 97%, while National Forest Inventory biomass estimates diverged notably from global products. The ensemble approach highlights how differences in input datasets, particularly in forest extent and biomass magnitude, can propagate through emissions calculations. These findings underscore the importance of understanding and evaluating dataset variability prior to national carbon reporting and emissions estimation.

Due to large footprints of remotely sensed microwave brightness temperatures, accuracy of microwave observations in areas of large surface heterogeneity has always been a technological challenge. Microwave observations in areas dominated by waterbodies typically exhibit observed brightness temperature several tens of kelvins lower than areas having no surface water. The non-linearity between brightness temperature and other geophysical quantities such as soil moisture makes the accuracy of microwave observations a critical element for accurate estimation of these quantities. In retrieving soil moisture estimates, an error of 1 K in remotely sensed microwave brightness temperatures results in about 0.5–1% error in volumetric soil moisture. Large uncertainties in the observed brightness temperatures make such observations unusable in areas of large brightness temperature contrast. In this article, we discuss a deconvolution method to improve accuracy using the overlap in the adjacent microwave observations. We have shown that the method results in improved accuracy of 40% in brightness temperature estimation in regions of high brightness temperature contrast.