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Funded Research

Cropland Carbon Monitoring System (CCMS): A satellite-based system to estimate carbon fluxes on U.S. Croplands

Izaurralde, Roberto (Cesar): University of Maryland (Project Lead)
Bandaru, Varaprasad (Prasad): USDA ARS (Co-Investigator)
Gowda, Prasanna: USDA (Co-Investigator)
Hurtt, George: University of Maryland (Co-Investigator)
Sahajpal, Ritvik: University of Maryland (Co-Investigator)
Sedano, Fernando: University of Maryland (Co-Investigator)
Daughtry, Craig: USDA (Collaborator)
Justice, Christopher (Chris): University of Maryland (Collaborator)
Nemani, Ramakrishna (Rama): NASA ARC (Collaborator)
Jones, Curtis: University of Maryland (Participant)
Williams, Mona Lisa: University of Maryland (Participant)

Project Funding: 2016 - 2019

NRA: 2015 NASA: Carbon Monitoring System   

Funded by NASA

Abstract:
Croplands are considered to have large CO2 offset capacity. However, it is highly uncertain how much CO2 stabilization can be achieved through land management strategies as croplands are expected to meet increasing demands for food and bioenergy production. The impacts of land use and land management practices on carbon (C) cycling should be anticipated when developing recommended strategies and policies; otherwise, they may induce unintended loss of CO2 to the atmosphere and render croplands as C sources. Lack of a cropland C monitoring system that captures the complexity of cropland C cycling and provides fine-scale and accurate C flux estimates hinders the development of effective joint policies and integrated sustainable carbon management strategies targeting CO2 offset potentials. Current methods for cropland C monitoring yield unreasonable regional flux estimates as they lack spatially resolved crop parameters and management practices. Satellite remote sensing is a strong tool for estimating spatially distributed vegetative characteristics (e.g. crop phenology and LAI) and crop parameters (e.g. land cover and land use change, crop species, crop rotations) used in agroecosystem models. As part of the Global Agricultural Monitoring (GEO-GLAM) program, which is jointly funded by NASA and USDA, we have developed a remote-sensing version of the mechanistic agroecosystem model EPIC, herein referred to as RS-EPIC, which utilizes satellite remote sensing data to improve crop characterization and simulation of crop productivity, soil C storage and C fluxes. The overall scientific goal of this proposal is to develop a Cropland C Monitoring System (CCMS) prototype that improves upon cropland C storage and flux estimates developed under previous NASA CMS activities in terms of spatial and temporal scale and completeness. As a first objective of this goal, we will integrate satellite-derived crop specific characterization of vegetation and management, off-shelf ancillary spatial databases and the RS-EPIC model to estimate seasonal and annual C cycle components including net primary production (NPP), net ecosystem productivity (NEP), harvested C, lateral soil C fluxes and net ecosystem C balance (NECB). These estimates will be produced for corn, soybean, wheat, sorghum, cotton, alfalfa, barley, rice and peas crops grown in the conterminous US at a spatial resolution of 500 m for 2015-2016. Together, the nine major crops grown cover approximately 96% of US cropland area. Three additional objectives are: 1) estimate uncertainty of C storage and fluxes estimated by the CCMS prototype; 2) engage with national agencies to evaluate the CCMS consistency with existing C inventories; 3) conduct a scoping study to evaluate remote sensing methods for mapping soil tillage at large scales. Ultimately, the CCMS products developed under this project will provide the knowledge base at relevant spatial and temporal scales for understanding complex C cycling outcomes under various land use and land management practices and developing joint policies to meet multiple objectives (e.g. food and energy security) while contributing to stabilize atmospheric CO2. Other potential uses of the CCMS include: 1) use in economic models to determine incentive levels for C management options; 2) integration into hydrological models to assess impacts on aquatic ecosystems; 3) incorporation into regional integrated assessment models to understand contributions of regional management practices to global climate change; 4) use of NPP estimates to interpret the top-bottom CO2 estimates 5) enhancement of EPA reporting of CO2 offset potentials on croplands.

Publications:

Bandaru, V., Yaramasu, R., PNVR, K., He, J., Fernando, S., Sahajpal, R., Wardlow, B. D., Suyker, A., Justice, C. 2020. PhenoCrop: An integrated satellite-based framework to estimate physiological growth stages of corn and soybeans. International Journal of Applied Earth Observation and Geoinformation. 92, 102188. DOI: 10.1016/j.jag.2020.102188


More details may be found in the following project profile(s):