(Browne et al., 2021, PLOS ONE)

Food-Water-Climate Sustainability Monitoring Assessment, and Prediction:

• Harnessing Data

• Theories

• AI

Ying’s lab has led the satellite remote sensing aspects towards poverty/children malnutrition targeting and prediction. These novel SIF datasets were subsequently used as major input for poverty/malnutrition targeting, mapping and monitoring in FtF countries in Africa designated by USAID (McBride et al., 2021, Applied Economic Perspectives and Policy; Browne et al., 2021, PLOS ONE).

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In collaboration with Christopher Barrett, along with rangeland scientists and environmental biologists, we are currently utilizing multi-scale and multi-source (e.g., SIF, hyperspectral, microwave, etc) remote sensing data to evaluate “Rangeland Health and Index-Based Livestock Insurance” and identify “Local-to-Global Win-Win Solutions for Human Health and Sustainability through Infectious Disease Control”

Carbon Monitoring and MRV in Eastern Africa (EA). Develop the first CMS prototype in EA that integrates “bottom-up” land model simulations constrained by multiple satellite observations and “top-down” carbon inversion to quantify carbon budgets at the 0.5° x 0.625° resolution spatial resolution, a resolution preferred by policy makers. Under this project, and also in collaboration with economists in IFPRI, we utilized our novel SIF datasets for impact evaluation of the Sustainable Land Management Project (SLMP) in Ethiopia, one of the world’s most ambitious restoration efforts to date.

(Constenla-Villoslada et al., 2022, Nature Sustainability)