Hot dark spot index method based on multi-angular remote sensing for leaf area index retrieval View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-04-25

AUTHORS

Qingyan Meng, Chunmei Wang, Xingfa Gu, Yunxiao Sun, Ying Zhang, Rumiana Vatseva, Tamas Jancso

ABSTRACT

Leaf area index (LAI) is an important parameter of vegetation ecosystems for crop monitoring and yield estimations. To resolve the ‘saturation phenomenon’ and develop an ideal LAI retrieval model for Chinese satellite HJ-1 CCD data, a hot dark spot (HDS) index method based on multi-angular remote sensing was investigated in this study. Experiments were conducted to obtain in situ measured spectral reflectance and LAI data. An effective vegetation index, HJVI, was put forward according to the unique characteristics of HJ-1 CCD bands. This index alleviated the vegetation index saturation phenomenon based on the ratio of the red bands to near-infrared bands. The canopy HDSs of winter wheat were simulated for different growth stages using the PROSAIL model and the HDS indices were calculated for different bands. The HDS_HJVI was then developed using an HDS of 865 nm, which was the most sensitive to LAI retrieval (R2 = 0.9953). HDS_HJVI was shown to be more sensitive to LAI than NDVI, HDS_NDVI, and HJVI. Thus, the LAI was retrieved using the HDS_HJVI index model and validated with the measured data (R2 = 0.8622 and 0.8512, respectively). Overall, these results indicate that the HDS index method based on multi-angular remote sensing is effective and can serve as a reference for other relative quantitative retrieval research. More... »

PAGES

732

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12665-016-5549-x

DOI

http://dx.doi.org/10.1007/s12665-016-5549-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029013235


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