Crop yields in smallholder systems are traditionally assessed using farmer-reported information in surveys, occasionally by crop cuts for a sub-section of a farmer's plot, and rarely using full-plot harvests. Accuracy and cost vary dramatically across methods. In parallel, satellite data is improving in terms of spatial, temporal, and spectral resolution needed to discern performance on smallholder plots. This study uses data from a survey experiment in Uganda, and evaluates the accuracy of Sentinel-2 imagery-based, remotely-sensed plot-level maize yields with respect to ground-based measures relying on farmer self-reporting, sub-plot crop cutting (CC), and full-plot crop cutting (FP). Remotely-sensed yields include two versions calibrated to FP and CC yields (calibrated), and an alternative based on crop model simulations, using no ground data (uncalibrated). On the ground, self-reported yields explained less than 1 percent of FP (and CC) yield variability, and while the average difference between CC and FP yields was not significant, CC yields captured one-quarter of FP yield variability. With satellite data, both calibrated and uncalibrated yields captured FP yield variability on pure stand plots similarly well, and both captured half of FP yield variability on pure stand plots above 0.10 hectare. The uncalibrated yields were consistently 1 ton per hectare higher than FP or CC yields, and the satellite-based yields were less well correlated with the ground-based measures on intercropped plots compared with pure stand ones. Importantly, regressions using CC, FP and remotely-sensed yields as dependent variables all produced very similar coefficients for yield response to production factors.

Worldwide, humans are facing high risks from natural hazards, especially in coastal regions with high population densities. Rising sea levels due to global warming are making coastal communities’ infrastructure vulnerable to natural disasters. The present study aims to provide a coupling approach of vulnerability and resilience through restoration and conservation of lost or degraded coastal natural habitats to reclamation under different climate change scenarios. The Integrated Valuation of Ecosystems and Tradeoffs (InVEST) model is used to assess the current and future vulnerability of coastal communities. The model employed is based on seven different bio-geophysical variables to calculate a Natural Hazard Index (NHI) and to highlight the criticality of the restoration of natural habitats. The results show that roughly 25 percent of the coastline and more than 5 million residents are in highly vulnerable coastal areas in China, and these numbers are expected to double by 2100. Our study suggests that restoration and conservation in recently reclaimed areas have the potential to reduce this vulnerability by 45 percent. Hence, natural habitats have proved to be a great defense against coastal hazards and should be prioritized in coastal planning and development. The findings confirm that natural habitats are critical for coastal resilience and can act as a recovery force of coastal functionality loss. Therefore, we recommend that the Chinese government prioritize restoration where possible and conservation of the remaining habitats for the sake of coastal resilience to prevent natural hazards from escalating into disasters.