Ecosystem-scale measurements of biomass water using cosmic ray neutrons

Accurate estimates of biomass are imperative for understanding the global carbon cycle. However, measurements of biomass and water in the biomass are difficult to obtain at a scale consistent with measurements of mass and energy transfer, ~1 km, leading to substantial uncertainty in dynamic global vegetation models. Here we use a novel cosmic ray neutron method to estimate a stoichiometric predictor of ecosystem-scale biomass and biomass water equivalent over tens of hectares. We present two experimental studies, one in a ponderosa pine forest and the other in a maize field, where neutron-derived estimates of biomass water equivalent are compared and found consistent with direct observations. Given the new hectometer scale of nondestructive observation and potential for continuous measurements, we anticipate this technique to be useful to many scientific disciplines

Extracting Agronomic Information from SMOS Vegetation Optical Depth in the US Corn Belt Using a Nonlinear Hierarchical Model

Remote sensing observations that vary in response to plant growth and senescence can be used to monitor crop development within and across growing seasons. Identifying when crops reach specific growth stages can improve harvest yield prediction and quantify climate change. Using the Level 2 vegetation optical depth (VOD) product from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite, we retrospectively estimate the timing of a key crop development stage in the United States Corn Belt. We employ nonlinear curves nested within a hierarchical modeling framework to extract the timing of the third reproductive development stage of corn (R3) as well as other new agronomic signals from SMOS VOD. We compare our estimates of the timing of R3 to United States Department of Agriculture (USDA) survey data for the years 2011, 2012, and 2013. We find that 87%, 70%, and 37%, respectively, of our model estimates of R3 timing agree with USDA district-level observations. We postulate that since the satellite estimates can be directly linked to a physiological state (the maximum amount of plant water, or water contained within plant tissue per ground area) it is more accurate than the USDA data which is based upon visual observations from roadways. Consequently, SMOS VOD could be used to replace, at a finer resolution than the district-level USDA reports, the R3 data that has not been reported by the USDA since 2013. We hypothesize the other model parameters contain new information about soil and crop management and crop productivity that are not routinely collected by any federal or state agency in the Corn Belt