Abstract
Crop emergence is a critical 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 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. Compared 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 emergence of crops in Corn Belt states (1–4 weeks to five-year average) was captured by HLS green-up date retrievals. This study demonstrates that routine within-season mapping of crop emergence/green-up at the field scale is practicable over large regions using operational satellite data. The green-up map derived from HLS during the growing season provides valuable information on spatial and temporal variability in crop emergence that can be used for crop monitoring and refining agricultural statistics used in broad-scale modeling efforts.
Highlights
Crop emergence is the first indicator of crop success
The within-season emergence (WISE) algorithm has been demonstrated effective in detecting green-up dates ususing the VENμS (2-day) and Harmonized Landsat and Sentinel-2 (HLS) (3–4-day) time series
As with all phenology algorithms, the green-up detection depends on the frequency of remote sensing observations and the green-up detection depends on the frequency of remote sensing observations and cloud conditions related to season and location
Summary
Crop emergence is the first indicator of crop success. Crop emergence depends on crop planting date, soil moisture, soil temperature, seed variety, and other factors [1,2].Under warm soil conditions, crops may emerge within a few days after planting. Crop emergence is the first indicator of crop success. Crop emergence depends on crop planting date, soil moisture, soil temperature, seed variety, and other factors [1,2]. Crops may emerge within a few days after planting. 2021, 13, 5074 emergence can take as long as a few weeks under cold soil conditions [3]. At the level of individual fields, crops may fail to emerge and need replanting. Crop emergence dates vary significantly by field and year. 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 [4,5,6]
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