Abstract
Timely and accurate knowledge about the geospatial distribution of crops at regional to continental scales is crucial for forecasting crop production and estimating crop water use. The United States (US) is one of the leading food-producing countries, but lacks a nationwide high resolution crop-specific land cover map available publicly during the current growing season. The goal of this study was to map crops across the Continental US (CONUS) before the harvest, and to estimate the earliest date of classification by which crops can be mapped with sufficient accuracy (90% of full-season accuracy). The study employed a scalable cluster-then-label model that was trained on multiple years of MODIS NDVI using ground truth data in the form of US Department of Agriculture (USDA) Cropland Data Layer (CDL) products. The first step in the crop classification was to perform Multivariate Spatio-Temporal Clustering (MSTC) of annual MODIS-derived NDVI trajectories to create phenologically similar regions, or phenoregions. The second step was to assign crop labels to phenoregions based on spatial concordance between phenoregions and crop classes from CDL using Mapcurves. Assigning crop labels to phenoregions was performed within ecoregions to reduce classification errors due to spatial variability in phenology caused by variations in climate, agricultural practices, and growing conditions. The crop classifier was trained and validated on the years 2008–2014, then tested independently on 2015–2018. Ecoregion-level crop classification performed better than state-level and CONUS-level classification. Pixel-wise accuracy of classification for eight major crops by area was around 70% across the major corn-, soybeans- and winter wheat-producing areas, whereas regions characterized by high crop diversity had slightly lower accuracy. Classification accuracy for dominant crops like corn, soybeans, winter wheat, fallow/idle cropland and other hay/non alfalfa improved with time as they grew, reaching 90% of year-end accuracy by the end of August over each of the four unseen years in the test period. For corn and soybeans, the earliest dates of classification were found to be much earlier in the central regions of the Corn Belt (parts of Iowa, Illinois and Indiana) than in peripheral areas. The ability to map growing crops may permit near real-time monitoring of the health status and vigor of agricultural crops nationally.
| Original language | English |
|---|---|
| Article number | 112048 |
| Journal | Remote Sensing of Environment |
| Volume | 251 |
| DOIs | |
| State | Published - Dec 15 2020 |
Funding
This research was partially sponsored by the US Department of Agriculture, US Forest Service, Eastern Forest Environmental Threat Assessment Center . Additional support was provided by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation (RUBISCO) Science Focus Area (SFA), which is sponsored by the Regional and Global Model Analysis (RGMA) activity in the Earth and Environmental Systems Sciences Division (EESSD) of the Biological and Environmental Research (BER) office in the US Department of Energy Office of Science . This research used resources of the Oak Ridge Leadership Computing Facility , which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725 . This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). Funding for Auroop R. Ganguly was provided by National Science Foundation (NSF) through its BIGDATA award 1447587 , CyberSEES award 1442728 and CRISP Type 2 award 1735505 . This research was partially sponsored by the US Department of Agriculture, US Forest Service, Eastern Forest Environmental Threat Assessment Center. Additional support was provided by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation (RUBISCO) Science Focus Area (SFA), which is sponsored by the Regional and Global Model Analysis (RGMA) activity in the Earth and Environmental Systems Sciences Division (EESSD) of the Biological and Environmental Research (BER) office in the US Department of Energy Office of Science. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Funding for Auroop R. Ganguly was provided by National Science Foundation (NSF) through its BIGDATA award 1447587, CyberSEES award 1442728 and CRISP Type 2 award 1735505.
Keywords
- Cropland data layer
- MODIS
- Mapcurves
- Multivariate spatio-temporal clustering
- NDVI
- Near real-time crop mapping
- Phenoregions
