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Brown and green LAI mapping through spectral indices
Institution:1. CREAF, Cerdanyola del Vallès 08193, Catalonia, Spain;2. CSIC, Global Ecology Unit, Cerdanyola del Vallès 08193, Catalonia, Spain;3. INRA UMR114 EMMAH, Domaine Saint-Paul, 84914 Avignon, France;1. School of Geodesy and Geomatics, Jiangsu Normal University, Xuzhou, Jiangsu 221116, China;2. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu 210008, China;3. Center for Spatial Information Science and Systems, George Mason University, VA 22030, USA;4. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Abstract:When crops senescence, leaves remain until they fall off or are harvested. Hence, leaf area index (LAI) stays high even when chlorophyll content degrades to zero. Current LAI approaches from remote sensing techniques are not optimized for estimating LAI of senescent vegetation. In this paper a two-step approach has been proposed to realize simultaneous LAI mapping over green and senescent croplands. The first step separates green from brown LAI by means of a newly proposed index, ‘Green Brown Vegetation Index (GBVI)’. This index exploits two shortwave infrared (SWIR) spectral bands centred at 2100 and 2000 nm, which fall right in the dry matter absorption regions, thereby providing positive values for senescent vegetation and negative for green vegetation. The second step involves applying linear regression functions based on optimized vegetation indices to estimate green and brown LAI estimation respectively. While the green LAI index uses a band in the red and a band in the red-edge, the brown LAI index uses bands located in the same spectral region as GBVI, i.e. an absorption band located in the region of maximum absorption of cellulose and lignin at 2154 nm, and a reference band at 1635 nm where the absorption of both water and dry matter is low. The two-step approach was applied to a HyMap image acquired over an agroecosystem at the agricultural site Barrax, Spain.
Keywords:Vegetation indices  Hyperspectral  Agroecosystem  Senescent vegetation  Sentinel-2
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