DOI: 10.1016/j.isprsjprs.2020.01.012
论文题名: Development and evaluation of a new algorithm for detecting 30 m land surface phenology from VIIRS and HLS time series
作者: Zhang X. ; Wang J. ; Henebry G.M. ; Gao F.
刊名: ISPRS Journal of Photogrammetry and Remote Sensing
ISSN: 9242716
出版年: 2020
卷: 161 语种: 英语
英文关键词: 30 m land surface phenology
; Evaluation and validation
; HLS time series
; VIIRS time series
Scopus关键词: Biodiversity
; Budget control
; Climate change
; Forestry
; Land use
; Petroleum reservoir evaluation
; Pixels
; Radiometers
; Surface measurement
; Thermography (imaging)
; Vegetation
; Enhanced vegetation index
; Environmental Monitoring
; Evaluation and validation
; Land surface phenology
; Mean absolute differences
; Moderate resolution imaging spectroradiometer
; Surface energy budget
; Visible infrared imaging radiometer suites
; Time series
; algorithm
; biodiversity
; carbon budget
; climate change
; detection method
; energy budget
; growth rate
; land cover
; land surface
; land use change
; model validation
; MODIS
; phenology
; pixel
; satellite data
; Sentinel
; time series analysis
; VIIRS
; United States
英文摘要: Land surface phenology (LSP) provides critical information for investigating vegetation growth and development, studying ecosystem biodiversity, modeling terrestrial carbon and surface energy budgets, detecting land cover and land use change, and monitoring climate change. Although operational 500 m LSP products have been produced from coarse resolution data observed from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), an LSP product is also needed at the Landsat scale (30 m) to enhance the environmental monitoring and modeling. However, temporal frequency of 30 m satellite data is always inadequate for reliable LSP detection, despite enrichment by the operational harmonized Landsat and Sentinel-2 (HLS) product. In this study, we propose a new algorithm of LSP detection for the generation of a 30 m LSP product using routinely produced HLS and VIIRS surface reflectance products. Specifically, the new algorithm compares a HLS EVI2 (two-band enhanced vegetation index) time series at a given 30 m pixel with the set of 500 m VIIRS EVI2 time series neighboring the HLS pixel and selects the most similar temporal shape of VIIRS time series even though the amplitude and/or phase between HLS and VIIRS EVI2 time series may be mismatched. The shape of the selected VIIRS EVI2 time series is then used to match to the given HLS EVI2 time series to generate a synthetic HLS-VIIRS time series. The HLS-VIIRS time series is subsequently processed using the hybrid piecewise logistic model to detect the phenological transition dates and to quantify the confidence of LSP detection. This new algorithm is evaluated by implementing 30 m LSP detection in eight HLS tiles in the northeastern (forests), central (croplands), and western (shrublands) United States. Evaluation finds that the new-algorithm-detected greenup onset (1) agrees well with the standard VIIRS LSP product without bias, (2) closely correlates to PhenoCam observations with a slope close to one, and (3) compares well with both PhenoCam and field species-specific observations with a mean absolute difference of 8 days and a difference less than 10 days in more than 70% of the validation samples. This implementation suggests that the new algorithm could be implemented for regional and global LSP product generation at a 30 m resolution. © 2020 The Author(s) Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158978
Appears in Collections: 气候变化与战略
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作者单位: Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, United States; Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD 57007, United States; Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI 48824, United States; Center for Global Change and Earth Observations (CGCEO), Michigan State University, East Lansing, MI 48823, United States; Agricultural Research Service, U.S. Department of Agriculture, BeltsvilleMD 20705, United States
Recommended Citation:
Zhang X.,Wang J.,Henebry G.M.,et al. Development and evaluation of a new algorithm for detecting 30 m land surface phenology from VIIRS and HLS time series[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2020-01-01,161