气候变化领域集成服务门户(CCPortal): A within-season approach for detecting early growth stages in corn and soybean using high temporal and spatial resolution imagery

CCPortal

DOI	10.1016/j.rse.2020.111752
	A within-season approach for detecting early growth stages in corn and soybean using high temporal and spatial resolution imagery
	Gao F.; Anderson M.; Daughtry C.; Karnieli A.; Hively D.; Kustas W.
发表日期	2020
ISSN	00344257
卷号	242
英文摘要	Crop emergence date is a critical input to models of crop development and biomass accumulation. The ability to robustly detect and map emergence date using remote sensing would greatly benefit operational yield estimation and crop monitoring efforts; however, this has proven to be challenging. Previous remote-sensing phenology algorithms showed that crop stages can typically be detected starting only around the V3-V4 (3 to 4 established leaves) vegetative stage. Furthermore, traditional approaches have a strong assumption regarding the temporal evolution of plant growth and normally require a complete growth period of observations to define seasonal changes. Most approaches were not designed for within-season operational mapping, particularly in the early growing season. In the current paper, we describe a new within-season emergence (WISE) approach to mapping crop green-up date using satellite observations available during early growth stages. The approach was first optimized using high spatiotemporal resolution (10 m, 2-day revisit) imagery from the Vegetation and Environment monitoring New MicroSatellite (VENμS) research mission, and assessed using ground observations of early crop growth stages (emergence VE and one leaf V1 stages for corn, and emergence VE and unifoliolate VC stages for soybeans) collected over the Beltsville Agricultural Research Center (BARC) experimental fields in Beltsville, MD during the 2019 growing season. Results show that early crop growth stages can be reliably detected at sub-field scale about two weeks after crop emergence. The remote-sensing green-up dates were about 4–5 days after crop emergence on average. Coefficients of determination (R2) between green-up dates and the mid-point dates of the early growth stages were above 0.90. The mean absolute differences, standard deviations, and root mean square errors comparing to the early growth stage mid-point dates were within six days. The maximum differences were within ±10 days across all fields. The WISE approach was assessed using operational Sentinel-2 data (10 m, 5-day revisit) over BARC. The detected green-up dates from Sentinel-2 were consistent with those from VENμS. Some fields were not detected due to the lack of observations around the emergence dates. For independent evaluation, the WISE approach was applied over an agricultural watershed on the Maryland Eastern Shore using both VENμS and the Harmonized Landsat and Sentinel-2 (HLS) data (30 m, 3–4-day revisit). The detected green-up dates were compared with emergence dates in crop progress reports from the National Agricultural Statistics Service (NASS) at the state-level. The WISE-detected green-up dates at the regional scale are within VE stage ranges but slightly earlier than NASS crop progress reports at the state-level. The WISE approach uses remote-sensing observations during the early crop growth stages and has potential for operational application within the season using Sentinel-2 and HLS data. © 2020
英文关键词	Crop emergence; Crop growth stages; Crop progress; Harmonized Landsat and Sentinel-2; Landsat; Remote-sensing phenology; Sentinel-2; Time-series analysis; VENμS
语种	英语
scopus关键词	Crops; Mapping; Mean square error; Plants (botany); Scheduling; Time series analysis; Crop emergences; Crop growth; LANDSAT; Mean absolute differences; National agricultural statistics services; Operational applications; Sentinel-2; Spatio-temporal resolution; Remote sensing; biomonitoring; growth rate; image resolution; Landsat; maize; phenology; remote sensing; seasonal variation; Sentinel; soybean; spatial resolution; temporal variation; Beltsville; Maryland; United States; Glycine max; Zea mays
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179348
作者单位	United States Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, 10300 Baltimore Avenue, Beltsville, MD 20705, United States; Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Israel; U.S. Geological Survey, Lower Mississippi-Gulf Water Science Center, Beltsville, MD 20705, United States
推荐引用方式 GB/T 7714	Gao F.,Anderson M.,Daughtry C.,et al. A within-season approach for detecting early growth stages in corn and soybean using high temporal and spatial resolution imagery[J],2020,242.
APA	Gao F.,Anderson M.,Daughtry C.,Karnieli A.,Hively D.,&Kustas W..(2020).A within-season approach for detecting early growth stages in corn and soybean using high temporal and spatial resolution imagery.Remote Sensing of Environment,242.
MLA	Gao F.,et al."A within-season approach for detecting early growth stages in corn and soybean using high temporal and spatial resolution imagery".Remote Sensing of Environment 242(2020).