Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset
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...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/24/5074 |
| _version_ | 1797500967458963456 |
|---|---|
| author | Feng Gao Martha C. Anderson David M. Johnson Robert Seffrin Brian Wardlow Andy Suyker Chunyuan Diao Dawn M. Browning |
| author_facet | Feng Gao Martha C. Anderson David M. Johnson Robert Seffrin Brian Wardlow Andy Suyker Chunyuan Diao Dawn M. Browning |
| author_sort | Feng Gao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:11:30Z |
| format | Article |
| id | doaj.art-4fb2a38988dd42dd887258e4501a2e61 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:11:30Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4fb2a38988dd42dd887258e4501a2e612023-11-23T10:24:32ZengMDPI AGRemote Sensing2072-42922021-12-011324507410.3390/rs13245074Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 DatasetFeng Gao0Martha C. Anderson1David M. Johnson2Robert Seffrin3Brian Wardlow4Andy Suyker5Chunyuan Diao6Dawn M. Browning7Hydrology and Remote Sensing Laboratory, U.S. Department of Agriculture, Agricultural Research Service, 10300 Baltimore Avenue, Beltsville, MD 20705, USAHydrology and Remote Sensing Laboratory, U.S. Department of Agriculture, Agricultural Research Service, 10300 Baltimore Avenue, Beltsville, MD 20705, USAU.S. Department of Agriculture, National Agricultural Statistics Service, 1400 Independence Ave., SW., Washington, DC 20250, USAU.S. Department of Agriculture, National Agricultural Statistics Service, 1400 Independence Ave., SW., Washington, DC 20250, USACenter for Advanced Land Management Information Technologies, University of Nebraska-Lincoln, 3310 Holdrege St, Lincoln, NE 68583, USASchool of Natural Resources, University of Nebraska-Lincoln, 3310 Holdrege St, Lincoln, NE 68583, USADepartment of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USAU.S. Department of Agriculture, Agricultural Research Service, Jornada Experimental Range, Las Cruces, NM 88003, USACrop 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.https://www.mdpi.com/2072-4292/13/24/5074crop growth stagesstart of the seasongreen-upcrop progresscrop conditionland surface phenology |
| spellingShingle | Feng Gao Martha C. Anderson David M. Johnson Robert Seffrin Brian Wardlow Andy Suyker Chunyuan Diao Dawn M. Browning Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset Remote Sensing crop growth stages start of the season green-up crop progress crop condition land surface phenology |
| title | Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset |
| title_full | Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset |
| title_fullStr | Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset |
| title_full_unstemmed | Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset |
| title_short | Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset |
| title_sort | towards routine mapping of crop emergence within the season using the harmonized landsat and sentinel 2 dataset |
| topic | crop growth stages start of the season green-up crop progress crop condition land surface phenology |
| url | https://www.mdpi.com/2072-4292/13/24/5074 |
| work_keys_str_mv | AT fenggao towardsroutinemappingofcropemergencewithintheseasonusingtheharmonizedlandsatandsentinel2dataset AT marthacanderson towardsroutinemappingofcropemergencewithintheseasonusingtheharmonizedlandsatandsentinel2dataset AT davidmjohnson towardsroutinemappingofcropemergencewithintheseasonusingtheharmonizedlandsatandsentinel2dataset AT robertseffrin towardsroutinemappingofcropemergencewithintheseasonusingtheharmonizedlandsatandsentinel2dataset AT brianwardlow towardsroutinemappingofcropemergencewithintheseasonusingtheharmonizedlandsatandsentinel2dataset AT andysuyker towardsroutinemappingofcropemergencewithintheseasonusingtheharmonizedlandsatandsentinel2dataset AT chunyuandiao towardsroutinemappingofcropemergencewithintheseasonusingtheharmonizedlandsatandsentinel2dataset AT dawnmbrowning towardsroutinemappingofcropemergencewithintheseasonusingtheharmonizedlandsatandsentinel2dataset |