Digital Phenotyping and Remote Sensing
– Quantifying crop photosynthesis and physiological status using sun-induced chlorophyll fluorescence (SIF), hyperspectral reflectance, and NIRv.
– Monitoring LAI, chlorophyll content, crop growth, and stress through integrated satellite, drone, ground-sensor, and high-resolution time-series data.
– Developing early detection methods for heat, drought, disease, and canopy-structure changes in rice, soybean, and maize.
– Assessing fine-scale crop physiological dynamics related to plant competition, planting density, and leaf-angle distribution.
Nature-based Climate Solutions (NbCS) and Low-Carbon Agriculture
– Evaluating carbon uptake and carbon–water–energy cycling in agroecosystems through crop and ecosystem photosynthesis/GPP observations.
– Investigating crop responses and yield-loss mechanisms under heat, drought, ozone, and other climate-related stresses.
– Applying climate-adaptive crop models to assess productivity and adaptation strategies under future climate scenarios.
– Improving sustainable irrigation and water-use efficiency to reduce resource inputs and climate risks in agriculture.
Crop Production and Management
– Predicting crop growth and yield spatially using remote sensing-based rice productivity models and crop growth indicators.
– Developing precision-irrigation decision-support systems that jointly consider soil-water availability and atmospheric evaporative demand.
– Evaluating how heat, drought, flooding, and disease stresses affect LAI, pod development, seed size, harvest index, and yield.
– Optimizing planting density and canopy structure while applying physiological early-warning signals for precision crop management.
