SBIR-STTR Award

Agricultural row crops occupy over 240 million acres of land in the United States (USDA-NASS2012). Decisions regarding the implementation of management practices in these agricultural area shave a significant effect on other environmental outcomes including soil erosion water quality and carbon sequestration. There is currently no systematic and cost-effective method for documenting detailed information on cover crops over a large region. We propose to 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 sites with the Northeast and Southern Cover Crop Council with the ultimate goal of providing a continental scale system. Fractional cover and biomass of the winter cover crop are the primary determinants of how effectively the cover crop protects the soil and immobilizes the nutrients (Prabhakara et al. 2015) and while information regarding the presence or absence of cover crops is rare information regarding the quality of cover crops is essentially non-existent. Currently some information regarding cover crops is collected in a drive-by survey method as part of a tillage transect. This approach is time consuming expensive limited in spatial extent and temporal sampling and provides very limited information about the potential effectiveness of the cover crop. The use of remote sensing and modeling to extract detailed information on cover crops across large regions represents a cost-efficient solution. This project targets five primary decision-makers as users of a cover crop information system: private corporations and foundations (e.g. The Nature Conservancy Walton Family Foundation)the growing ecosystem services and water quality trading markets (e.g. Noble Research Institute Ohio River Basin WQ Trading program) watershed- and state-level water quality programs (e.g. Chesapeake Bay Program Partners such as Pennsylvania Dept. of Agriculture) the US Dept. of Agriculture (USDA; with a focus on the National Resource Conservation Service [NRCS] and National Agriculture Statistics Service [NASS]) and consumer packaged goods companies looking to track the supply chain. All of these groups need accurate timely and spatially comprehensive information about the dynamic state of cover crops across large regions. Studies have shown that cover crop presence can be mapped with moderate resolution remote sensing data (e.g. SPOT Landsat) on a case-by-case basis (Hively et al. 2009). However these studies have not demonstrated that mapping with remote sensing and modeling can be operationalized to produce detailed information on cover crops. The primary barrier to operational mapping with Landsat data is its limited temporal coverage in combination with the high temporal dynamics of the cover crop cycle. In this project 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.

Decisions regarding the implementation of conservation and soil health practices in agriculturalareas can have a significant effect on productivity and environmental outcomes including soilerosion water quality and carbon sequestration. In addition the effects of management can varydue to soil type and topographic conditions. While Dagan's OpTIS system produces wide areamaps of the presence of cover crops there is currently no systematic and cost-effective methodfor documenting winter cover crop quality or the resulting effects of these cover crops over alarge region. During Phase I activities we demonstrated the feasibility of using multi-sensorsatellite observations and biogeochemical modeling for operational cover crop monitoring thatwill systematically provide detailed information about the spatial and temporal dynamics ofcover crop adoption and vigor. In Phase II we propose to operationalize these algorithms tofacilitate deployment of the system over wide areas back through time. This monitoringsystem will be tied to our existing Operational Tillage Information System (OpTIS) toprovide the enhanced detailed information that our data service clients require forsustainability reporting ecosystem service market participation and scenario planning.Under Phase II activities our goal is to bring the research demonstrated under Phase I activitiesinto a fully functional and largely automated prototype system and evaluate the products in fourdemonstration regions. This goal will be accomplished through five technical objectives:"¢ Technical Objective1: Refine and implement the automated data acquisition pre-