Digital Twin Infrastructure Model for Agricultural Applications

Fully coupled Earth system models are fundamental to timely and accurate weather and climate change information. These process-based estimates are then used as critical inputs in a variety of end-use earth system environments. For example, agricultural models require inputs of precipitation, temperature and moisture conditions along with historical data as key weather parameters to develop estimates of field operations schedules, from seeding to harvesting, with fertilizer and herbicide treatments in-between. Unexpected extreme weather and climate change has a major socioeconomic impact on agriculture and food security. Though important, integration of such end-use oriented and socio-economic models are currently not adequately captured in Earth system modeling efforts and need to be part of the vision for 'Digital Twin'. Here we propose the development of such a prototype by integrating land/hydrology process models, agricultural system models, and remote sensing information. The aim of this project is to develop an agriculture productivity modeling system over Continental United States as an example of incorporating representations of infrastructure oriented process, so that our understanding, prediction, and mitigation/response to Earth system process variability can be characterized. Crop growth, yield, and agricultural production information is critical for commodity market, food security, economic stability, and government policy formulation. The ability to model agriculture is important for NASA Earth Sciences Program towards its goals for providing timely and relevant information for "science decisions". The current USDA National Agricultural Statistics Service (NASS) crop yield is mainly based on agricultural survey which relies on a limited number of samples though linear regression models based on remote sensed NDVI and NASS historical yield records are also used as complementary. Further improvement of crop yield estimation and better crop progress monitoring are needed. This proposal will target Advanced and Emerging Technology (AET) by developing a modeling framework for Agricultural Decision Support System (ADSS). The specific goals of the proposal are to (1) establish a digital twin framework that enables the NASA remote sensing data products and land surface model products to be directly coupled with or assimilated into the crop growth model; (2) leverage the information from high-resolution remote sensing inputs (precipitation, temperature, solar radiation, soil moisture, snow water equivalent, ground water, leaf area index) through the NASA Land Information System (LIS) to estimate land surface variables (water and energy fluxes) at daily time scales; (3) implement crop growth models (APEX, RZWQM2 and DSSAT) to estimate crop growth stages, biomass, and crop yield under forecasted weather and projected climate conditions; (4) implement Bayesian Neural Network (BNN) model to predict final county level crop yield by using NASS historical yield reports, yield, biomass, and phenology outputs from APEX/ DSSAT model, and other variables from LIS; (5) develop tools to conduct 'what if' investigations to provide agricultural guidance; and (6) develop capability for disseminating non-confidential crop progress data, biomass and crop yield maps using an operational web application. The proposed environment will be developed in an interoperable manner to allow for future interactions with other ESDT efforts.

Advance Earth system science knowledge through the Identification, develop, and demonstrate innovative information systems technologies

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