Towards routine mapping of crop emergence within the season using the Harmonized Landsat and Sentinel-2 dataset

Authors: Gao, Feng; Anderson, Martha; JOHNSON, D. - National Agricultural Statistical Service (NASS, USDA); SEFFRIN, R. - National Agricultural Statistical Service (NASS, USDA); WARDLOW, B. - University Of Nebraska; SUYKER, A. - Nebraska Department Of Natural Resources; DIAO, C. - University Of Illinois; Browning, Dawn

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/9/2021
Publication Date: 12/21/2021
Citation: Gao, F.N., Anderson, M.C., Johnson, D., Seffrin, R., Wardlow, B., Suyker, A., Diao, C., Browning, D.M. 2021. Towards routine mapping of crop emergence within the season using the Harmonized Landsat and Sentinel-2 dataset. Remote Sensing. 13(24):5074 https://doi.org/10.3390/rs13245074.
DOI: https://doi.org/10.3390/rs13245074

Interpretive Summary: Crop emergence is the first indicator of crop success. Mapping crop emergence at the field scale during the growing season provides critical information for crop growth modeling, crop condition monitoring, biomass accumulation estimation, and yield prediction. Remotely sensed data have been used to map vegetation green-up dates, which relate to crop emergence. However, remotely sensed green-up maps are usually produced after the growing season. In a previous study, ARS scientists developed a within-season emergence (WISE) mapping algorithm in the Beltsville Agricultural Research Center (BARC) experimental fields using imagery from a small experimental satellite. This paper refines and significantly extends the WISE algorithm to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) using the routine harmonized Landsat and Sentinel-2 (HLS) data. Results from 2017 to 2020 were validated and assessed using ground observations, daily PhenoCam time series, and crop progress reports from the National Agricultural Statistics Service. Results show that routine mapping of crop emergence within the growing season at the field scale over a large region using operational HLS data is feasible. The green-up map produced at 30-m resolution provides spatial and temporal variability of crop emergence for crop monitoring and agricultural statistics.

Technical Abstract: Crop emergence is a critical growth stage for crop development modeling, crop condition monitoring, and biomass accumulation estimation. Green-up dates (or the start of the season) detected from remote sensing time series are related to, but generally lag, crop emergence dates. In this paper we refine the within-season emergence (WISE) algorithm and extend application to five Corn Belt states (Iowa, Illinois,Indiana, Minnesota, and Nebraska) using the routine harmonized Landsat and Sentinel-2 (HLS) data from 2018 to 2020. Green-up dates detected from the HLS time series were assessed using field observations and near-surface measurements from PhenoCams. Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. Comparing to observed crop emergence dates, green-up dates from HLS using WISE were ~3 days later at the field scale (30-m). The mean absolute difference (MAD) was ~7 days and the root mean square error (RMSE) was ~9 days. At the state level, the mean differences between median HLS green-up date and median crop emergence date were within 2 days for 2018-2020. At this scale, MAD was within 4 days and RMSE was less than 5 days for both corn and soybeans. The R-squares were 0.73 and 0.87 for corn and soybean, respectively. The 2019 late emergences in Corn Belt states (1-4 weeks) were captured by HLS green-up dates. This study demonstrates that routine mapping of green-up (crop emergence) within the growing season at the field scale over a large region using operational satellite data is practicable. The green-up map derived from HLS during the growing season provides spatial and temporal variability of crop emergence for crop monitoring and agricultural statistics.