Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 15, Issue 11, Pages 1662-1666Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2018.2856765
Keywords
Crop; inverted difference vegetation index (IDVI); leaf area index (LAI); normalized difference vegetation index (NDVI); sensitivity analysis
Categories
Funding
- Natural Science Foundation of China [41771371, 41771369]
- NSFC
- STFC of the U.K. Joint Program Project [61661136006002]
- China National Major Project of High-Resolution Earth Observation [11-Y20A05-9001-15/16]
- STFC [ST/N006798/1] Funding Source: UKRI
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Leaf area index (LAI), an important parameter describing a crop canopy structure and its growth status, can he estimated from remote sensing data by statistical methods involving vegetation indices (VIs). This letter reports the development of a new VI, the inverted difference vegetation index (IDVI), for crop LAI retrieval. The IDVI can overcome the saturation issue of the normalized difference vegetation index (NDVI) at high LAI values and exhibits robust insensitivity to crop leaf water and chlorophyll content. By combining the IDVI and NDVI with a scaling factor, we constructed a novel statistical regression model with parameters that can he calibrated to a specific region to estimate the LAI. Validations on simulated data and in situ observations show that the proposed retrieval method with the IDVI is stable for low and high LAIs and obtains better results than the empirical method involving the NDVI at the regional scale. Findings in this letter will benefit future agricultural applications.
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