Location: Hydrology and Remote Sensing Laboratory
Project Number: 8042-13610-030-071-R
Project Type: Reimbursable Cooperative Agreement
Start Date: Oct 1, 2021
End Date: Sep 30, 2025
Objective:
The overall objectives for this project are to 1) examine the linkages between regenerative pasture and rangeland management and overarching ecosystem function, and 2) understand how rural well-being affects management decisions. As pasture and rangelands are complex social-ecological systems, ARS propose that the study of people and land are necessary in these systems. To meet these goals, ARS will engage ranchers directly and employ the most current scientific landscape assessment tools to build integrated models that work to represent each component of grazing land social-ecological systems and their socioeconomic well-being.
Under this project, ARS NEA and PA propose to employ data fusion methods for 1) mapping Evapotranspiration (ET), Evaporative Stress Indes (ESI), Vegetation Index (VI), and Leaf Area Index (LAI) at target rangeland and pasture sites to assess changes in water use and vegetation health (ARS NEA), and 2) forage conditions – biomass and quality in response to land management and grazing strategies (ARS PA).
Approach:
The USDA-ARS Atmosphere-Land Exchange Inverse (ALEXI) energy balance model and associated flux disaggregation technique (DisALEXI) have been employed to retrieve diagnostic estimates of daily evapotranspiration (ET) at approximately 30-m spatial resolution. This modeling toolkit uses a multi-sensor data fusion approach (combining MODIS/VIIRS and Landsat/Sentinel-2) to achieve both spatial and temporal resolution relevant to management decision making, and has been evaluated over target sites in agricultural and forested biomes within the US (e.g., Anderson et al., 2018). In addition, an Evaporative Stress Index (ESI), reflecting anomalies in ET or crop water use with respect to long-term baseline conditions, has been generated at field-to-regional scales. A similar fusion of VSWIR data has enabled construction of daily vegetation index (VI) timeseries at 10-30m spatial resolution, used for extracting phenology metrics and biomass accumulation rates (Gao et al., 2017, 2020). Potential VIs of interest to this project include the leaf area index (LAI) and standard spectral indices (e.g., NDVI, EVI2).
In this project, we propose to employ these data fusion methods for 1) mapping ET, ESI, VI, and LAI at target rangeland and pasture sites to assess changes in water use and vegetation health (ARS NEA), and 2) forage conditions – biomass and quality in response to land management and grazing strategies (ARS PA). Maps will be generated over a time period encompassing the 5-year project period with at least two additional lead-in years. Accuracy of model estimates will be evaluated in comparison with field measurements collected by other partners in the project team. ARS NEA and ARS PA will also work with partners in interpreting spatiotemporal remotely sensed indicators as a potential tool for scaling metrics of rangeland performance and health developed in situ to larger areas.
