气候变化领域集成服务门户(CCPortal): Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach

CCPortal

DOI	10.1016/j.rse.2020.112156
	Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach
	Sun L.; Gao F.; Xie D.; Anderson M.; Chen R.; Yang Y.; Yang Y.; Chen Z.
发表日期	2021
ISSN	00344257
卷号	253
英文摘要	Multi-sensor remote sensing data fusion technologies have been developed and widely applied in recent years, providing a feasible and economical solution to increase the availability of high spatial and temporal resolution data. These methods, however, have been challenging to apply in highly heterogeneous areas, especially in complex agricultural landscapes where there are rapid changes at small scales, while features at larger scales change more slowly. In this study, we developed a novel method to reconstruct daily 30 m Normalized Difference Vegetation Index (NDVI) using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat and Landsat-like platforms, and the Cropland Data Layer (CDL). This method utilizes a crop reference curve (CRC) approach, in which a set of NDVI time series are extracted from pure MODIS pixels (250 m resolution) identified using the CDL, and then used to fit Landsat-like observations (30 m). The CRC based method was applied over a complex agricultural landscape in the Choptank River watershed on the Eastern Shore of Maryland. Landsat data from 2013 and 2014 and Harmonized Landsat and Sentinel-2 (HLS) data from 2018 were used to reconstruct 30 m daily NDVI maps for major crop types. Results show that the relative error (RE) in reconstructed NDVI is around 6–8% during periods of rapid crop growth, and 3–5% during peak periods when growth is slow. The accuracy of the CRC method outperforms a standard image pair-based data fusion algorithm (Spatial and Temporal Adaptive Reflectance Fusion Model; STARFM), which yields RE of 4–9% in slow-growth periods and 10–16% in fast-growth periods when clear Landsat images are scarce. The CRC method was also compared with time-series data fusion methods, including a harmonic fitting model and the SaTellite dAta IntegRation (STAIR) model. The results show that CRC gives similar results when the Landsat-like image availability is high (around 27 images per year), but outperforms other methods when availability is limited (less than 15 images per year). The reconstructed NDVI time series for corn, soybean, winter wheat/soybean and forest at 30-m resolution show clear phenological patterns at the sub-field scale. The resulting 30-m NDVI timeseries data provide useful information for mapping crop phenology and monitoring crop condition in complex agricultural landscapes, especially for complex double-cropping areas. However, the input requirement of an accurate 30-m crop classification map constrains its application to areas and periods where classifications are available. © 2020 Elsevier Inc.
英文关键词	Crop phenology; Crop progress condition; Crop reference curve; High spatial and temporal resolution; Vegetation index time series
语种	英语
scopus关键词	Agricultural robots; Crops; Data integration; Radiometers; Remote sensing; Sensor data fusion; Time series; Agricultural landscapes; Crop classification; Data fusion algorithm; Image availability; Moderate resolution imaging spectroradiometer; Normalized difference vegetation index; Remote sensing data fusion; Spatial and temporal resolutions; Image processing; agricultural land; algorithm; Landsat; MODIS; NDVI; phenology; satellite imagery; Sentinel; time series analysis; watershed; Choptank River; Maryland; United States; Glycine max
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/179048
作者单位	Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, Beijing, 100081, China; U. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, United States; State Key Laboratory of Remote Sensing Science, Jointly Sponsored by, Beijing Normal University, Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
推荐引用方式 GB/T 7714	Sun L.,Gao F.,Xie D.,et al. Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach[J],2021,253.
APA	Sun L..,Gao F..,Xie D..,Anderson M..,Chen R..,...&Chen Z..(2021).Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach.Remote Sensing of Environment,253.
MLA	Sun L.,et al."Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach".Remote Sensing of Environment 253(2021).