Recipient Organization
DAGAN, INC.
15 NEWMARKET RD
DURHAM,NH 038242815
Performing Department
(N/A)
Non Technical Summary
Decisions regarding the implementation of conservation and soil health practices in agricultural areas can have a significant effect on productivity and environmental outcomes, including soil erosion, water quality, and carbon sequestration. In addition, the effects of management can vary due to soil type and topographic conditions. While Dagan's OpTIS system produces wide area maps of the presence of cover crops, there is currently no systematic and cost-effective method for documenting winter cover crop quality, or the resulting effects of these cover crops, over a large region. During Phase I activities, we demonstrated the feasibility of using multi-sensor satellite observations and biogeochemical modeling for operational cover crop monitoring that will systematically provide detailed information about the spatial and temporal dynamics of cover crop adoption and vigor. In Phase II, we propose to operationalize these algorithms to facilitate deployment of the system over wide areas, back through time. This monitoring system will be tied to our existing Operational Tillage Information System (OpTIS) to provide the enhanced, detailed information that our data service clients require for sustainability reporting, ecosystem service market participation, and scenario planning.Under Phase II activities our goal is to bring the research demonstrated under Phase I activities into a fully functional and largely automated prototype system and evaluate the products in four demonstration regions. This goal will be accomplished through five technical objectives.This project targets five primary decision-makers as users of a cover crop information system: ag non-profit organizations (e.g. The Nature Conservancy), the developing ecosystem service markets (e.g. ESMC), watershed- and state-level water quality programs, the US Dept. of Agriculture NRCS and National Agriculture Statistics Service [NASS], and private corporations (e.g. General Mills and Bayer). Each of these organizations need accurate, timely, and spatially comprehensive information about the dynamic state of cover crops across large regions.
Animal Health Component
45%
Research Effort Categories
Basic
10%
Applied
45%
Developmental
45%
Goals / Objectives
Primary Goal: Bring the research demonstrated under Phase I activities into a fully functional and largely automated prototype system, and we will evaluate the products for four demonstration regions.This goal will be accomplished through five technical objectives.Algorithm refinement objectivesTechnical Objective1: Refine and implement the automated data acquisition, pre-processing, and sensor integration framework outlined during Phase I activities.Technical Objective 2: Refine and implement the automated optimization of the fAPAR CSDM model that provides the foundation for estimating biomass, fractional cover, and crop calendar for the four Phase II implementation sites (a. Chesapeake Bay watershed, b. Minnesota, c. Northeastern US, and d. 90 sites across the South).Technical Objective 3: Refine and implement the automated integration of remote sensing-based estimates of biomass to constrain and improve the modeling of soil health and greenhouse gas emissions in the DNDC biogeochemical model.Product delivery objectiveTechnical Objective 4: Refine and implement the API delivery system outlined during Phase I activities.End-user integration and expanded marketing objectiveTechnical Objective 5: Work hand-in-hand with end-users at the Ecosystem Service Market Consortium, The Nature Conservancy, and General Mills to tailor our products and assist in integrating these products into their existing systems.
Project Methods
We will use our Geospatial Image Processing System (GIPS; open source software available at https://github.com/applied-geosolutions/gips) to acquire and pre-process multi-temporal Landsat, Sentinel-2A and 2B, and MODIS data for our testing locations. As noted in Phase I activities, modeling of canopy structure dynamics can be improved through the fusion of Landsat, Sentinel 2, and MODIS to create a seamless, gap-filled timeseries of NDVI. While our existing fusion module in GIPS is functional and was improved in Phase I, additional improvements to algorithms will be implemented in Phase II. The fusion process continues to produce sub-optimal results when the temporal gap between Landsat or Sentinel 2 extends beyond 40 days, particularly in the dynamic spring and fall seasons. These results can be improved by adjusting the way the weighting scheme is currently implemented in the algorithm.The Phase I implementation of the Canopy Structure Dynamic Model follows similar implementation of Liao et al., 2019 and Liu et al., 2010. This approach integrates patterns of daily temperature into the model. In Phase II, we will look to improve the CSDM approach by including the consideration of cumulative precipitation data as input into the modelling process (e.g. Sun and Schulz, 2017). In addition to temperature, precipitation is a primary controlling factor in the development of the crop canopy and integrating precipitation is expected to improve the fit between modeled and remote-sensing observed fAPAR.In Phase I, our team outlined two preferred approaches to using remote sensing observations for improved DNDC modeling of SOC, N2O, CH4, and other critical greenhouse gas emissions and soil health metrics: (1) to identify fields with similar growth patterns to be calibrated as a group, and (2) to directly calibrate seasonal growth at the field-scale. In Phase II, we will implement, demonstrate, and test these two approaches and report metrics on improvement and functionality. Our work on these two approaches will include both science research and implementation via software engineering and development.During Phase II, the team will build an API to receive data requests from customers and return detailed cover crop information back to the customers. These APIs will be tested with our partners and will be considered complete after our partners confirm successful use and implementation into their reporting systems.Communication between (a) the team's technical, scientific, and marketing staff and (b) our end-users and customers will result in improved products and more effective marketing. The world of soil health, ag conservation, and ecosystem service markets continues to grow and evolve rapidly. As end-user needs are refined, we will work to ensure the content and format of our data services, as well as our messaging, are well delivered to our clients. To ensure we understand our end-user needs, we will establish quarterly web meetings with representatives from our partner customers. These quarterly meetings will include updates on project progress provided by the project team, and updates from each organization regarding related projects and updates to their needs.