Performing Department
(N/A)
Non Technical Summary
Agricultural row crops occupy over 240 million acres of land in the United States. Decisions regarding the implementation of management practices in these agricultural areas have a significant effect on other environmental outcomes, including soil erosion, water quality, and carbon sequestration. Fractional cover and biomass of the winter cover crop are the primary determinants of how effectively a cover crop protects the soil and immobilizes the nutrients, and while information regarding the presence or absence of cover crops is rare, information regarding the quality of cover crops is essentially non-existent. We propose to use remote sensing and modeling to extract detailed information on cover crops across large regions, and believe that this system will represent a cost-efficient solution.
Animal Health Component
50%
Research Effort Categories
Basic
10%
Applied
50%
Developmental
40%
Goals / Objectives
We will evaluate the feasibility of using fused multi-sensor observations for operational cover crop monitoring that will systematically provide detailed information about the dynamics of cover crop systems. Our initial focus will be on three agricultural test areas: the Maumee River Watershed within the western basin of Lake Erie, the Vermilion Headwaters and Mackinaw watersheds in Illinois, and the Choptank River Watershed in Chesapeake Bay region, with the ultimate goal of providing a continental scale system. We will assess the feasibility of mapping cover crop attributes (location, fractional cover, green-up date, kill date, and biomass) with high frequency time series information from a combination of modeling and multiple satellite sensors including Landsat 8, Landsat 7, Sentinel 2, and MODIS, and gain additional insights about the cover crops by integrating satellite remote sensing observations with a soil biogeochemical model.
Project Methods
We have constructed a project team that includes remote sensing experts, cover crop experts, and web programming experts. Through this project team, we have cover crop information, both ground and remote sensing-based, from diverse agricultural regions in the United States. Our technical objective will be met through the completion of three parallel tasks.Task 1. Demonstrate the technical feasibility of mapping characteristics of cover crops using multi-sensor remote sensing data, using our Geospatial Image Processing System (GIPS; open source software ) to acquire multi-temporal Landsat, Sentinel-2A and 2B, and MODIS data for the 2015-2017 locations. These data will be used to create a 5-day 30-m gap-filled NDVI time series product which will be a foundational data set for cover crop class, fractional cover, timing information, and biomass. The generation of cover crop class, fractional cover, and timing will be done with a decision tree algorithm. Biomass will be estimate via NDVI time series integration with the DNDC biogeochemical model. All estimates produced will be validated against ground observations.Task 2. Develop a protocol for systematic processing and validation of cover crop map products by building upon our open source GIPS system.Task 3. Understand user needs and design a prototype web-based data visualization and delivery system by working closely with an advisory team that will provide feedback on data requirements, including acceptable product accuracy and latency, as well as preferred data formats.