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Nondestructive estimation of canopy chlorophyll content using Hyperion and Landsat/TM images

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Abstract:

Remote sensing offers a nondestructive tool for the quick and precise estimation of canopy chlorophyll content that serves as an important indicator of the plant ecosystem. In this study, the canopy chlorophyll content of 26 samples in 2007 and 40 samples in 2008 of maize were nondestructively estimated by a set of vegetation indices (VIs; Normalized Difference Vegetation Index, NDVI; Green Chlorophyll Index, CIgreen; modified soil adjust vegetation index, MSAVI; and Enhanced Vegetation Index, EVI) derived from the hyperspectral Hyperion and Thematic Mapper (TM) images. The PROSPECT model was used for sensitivity analysis among the indices and results indicated that CIgreen had a large linear correlation with chlorophyll content ranging from 100-1000 mg m-2. EVI showed a moderate ability in avoiding saturation and reached a saturation of chlorophyll content above 600 mg m-2. Both of the other two indices, MSAVI and NDVI, showed a clear saturation at chlorophyll content of 400 mg m-2, which demonstrated they may be inappropriate for chlorophyll interpretation at high values. A validation study was also conducted with satellite observations (Hyperion and TM) and in-situ measurements of chlorophyll content in maize. Results indicated that canopy chlorophyll content can be remotely evaluated by VIs with r2 ranging from the lowest of 0.73 for NDVI to the highest of 0.86 for CIgreen. EVI had a greater precision (r2=0.81) than MASVI (r2=0.75) in canopy chlorophyll content estimation. The results agreed well with the sensitivity study and will be helpful in developing future models for canopy chlorophyll evaluation.

Document Type: Research Article

DOI: https://doi.org/10.1080/01431161003614382

Affiliations: 1: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China,Graduate University of Chinese Academy of Science, Beijing, China 2: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China

Publication date: 2010-03-01

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