S415 Soil Science Building & Zoom (Speaker will be virtual)
Earth Observation for Agriculture Monitoring
Harnessing the Synergy of Diverse Sensing Technologies from Field to Regional Scales
The past two decades have witnessed an explosion in availability of Earth Observations at increasingly higher spatial and temporal resolutions, sparking new applications in agriculture monitoring and modeling. In this seminar, I will present three studies in my lab that integrate diverse sensing technologies to advance understanding of interactions among agriculture, climate change, and anthropogenic activities, from the field to regional scales.
At the field scale, we aim to elucidate the dynamics of solar-induced chlorophyll fluorescence (SIF) and photosynthesis and their drivers towards precision agriculture. Specifically, to understand the dynamics of crop photosynthesis, we developed a novel approach that uses remotely sensed SIF to partition the net CO2 flux (i.e., NEE) into its two components, i.e., Gross Primary Production (GPP) and ecosystem respiration, at a corn site located in the Cornell Agriculture Research Station (Kira et al., 2021). We found that SIF-based NEE partitioning led to distinct diurnal and seasonal patterns that may impact the estimates of crop production and the total annual budgets of carbon sequestration.
At the regional scale, we developed a “scalable” approach that requires minimal calibration to estimate crop yields in the US corn belt and the Indo-Gangetic Plain. This approach builds upon the mechanistic linkage among SIF, electron transport rate (ETR), and photosynthesis (Han et al., 2022), and takes high-resolution satellite SIF as input. Our results show that this mechanistic model with minimal calibration with ground data has at least equal or better predictability compared to the commonly used machine learning models. Our results have important implications for applying this framework to regions that are most vulnerable to food insecurity yet have low quality and quantity of ground-truthing crop yield data (for model calibration).
The third study aims to understand how agriculture impacts water sustainability in Northwestern China (NWC), a typical dryland system that has been documented as one of the major global hotspots suffering from massive terrestrial water storage (TWS) depletion in the past two decades (Lai et al., 2022). Understanding such drivers is both essential and urgent as this vast dryland faces dual challenges in agricultural production and water scarcity. Utilizing a diverse array of satellite observations including land use/land cover (based on optical remote sensing), evapotranspiration (based on thermal and microwave remote sensing), and TWS (based on Earth’s gravity measurements), along with climate reanalyses and national-level census data, we found that irrigation-supported cropland expansion has been the main driver underlying the basin-wide secular decline of TWS in NWC. Such freshwater and agricultural trajectory in NWC is much like the perilous path in the Middle East. Our findings suggest that the agricultural water use in the dryland of NWC is highly unsustainable and have important implications for policy-making towards Sustainable Development Goals (SDGs).
Dr. Ying Sun, Assistant Professor, Cornell University