Understanding and Modeling Tropical Grasslands Using Remotely Sensed Fluorescence and Soil Moisture

Seasonal grasslands account for a large area of Earth's land cover. Annual and seasonal changes in these grasslands have profound impacts on Earth's carbon, energy, and water cycles. In tropical grasslands, growth is commonly water-limited and the landscape oscillates between highly productive and unproductive. As the monsoon begins, soils moisten providing dry grasses the water necessary to photosynthesize. However, along with seasonal rains come clouds that obscure satellite products (MODIS fPAR/LAI) that are commonly used to quantify phenology and productivity in these areas. To mitigate this issue, we used solar induced fluorescence (SIF) products from GOSAT, GOME-2, and OCO-2 along with soil moisture products from SMAP which see through the clouds to monitor grassland productivity. To get a broader understanding of the vegetation dynamics, we used the Simple Biosphere Model (SiB) to simulate the seasonal cycles of vegetation. In conjunction with SiB, the remotely sensed SIF and soil moisture observations were utilized to paint a clearer picture of seasonal productivity in tropical grasslands. We focused on the growing season onset and senescence of vegetation in both SiB and remotely sensed observations. We investigated the threshold relationships between observed soil moisture and SIF during these "green-up" and "brown-down" periods. SIF and SMAP provide an unprecedented number of observations of these transitions and revealed substantial model biases in the treatment of grassland phenology. Comparing the observed thresholds to model phenology allowed us to improve SiB to more accurately represent the carbon cycle in tropical grasslands across the world.