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Intercomparison and validation of five existing leaf chlorophyll content products over China
- Source :
- International Journal of Applied Earth Observations and Geoinformation, Vol 130, Iss , Pp 103930- (2024)
- Publication Year :
- 2024
- Publisher :
- Elsevier, 2024.
-
Abstract
- Leaf chlorophyll content (LCC) is crucial in plant physiology and ecological research. Although several LCC products have recently been generated at a regional or global scale, understanding their accuracy is still a concern in the scientific community. We intercompared and analyzed five existing LCC products (MuSyQ LCC, MODIS LCC, MERIS LCC, GLCC, and GLOBMAP MERIS LCC) over China in terms of spatial continuity and spatiotemporal consistency over seven plant functional types. The products of 2011 and 2019 over China were used in this study. Research findings indicate (1) the 30 m-resolution MuSyQ LCC has the highest accuracy compared to field LCC of cropland and grassland types, with an RMSE of 19.5 μg/cm2, while MODIS LCC product demonstrates a more robust fit to the measured LCC, with an R2 of 0.341. (2) Interpolation of products with lower spatial resolution e.g. MODIS LCC, MERIS LCC, and GLOBMAP MERIS LCC, generally improves spatial continuity. The non-interpolated 30 m MuSyQ LCC exhibits good regional continuity due to its high spatial resolution. The lowest spatial continuity is found over shrubs for all products. (3) MODIS LCC and MuSyQ LCC of 2019 demonstrate high overall spatial consistency and exhibit the highest correlation over cropland sites. MODIS LCC and GLOBMAP MERIS LCC of 2011 demonstrate high temporal consistency over deciduous forests, evergreen forests, grasslands, and shrubs sites. The most robust overall temporal consistency is exhibited among all products in the deciduous needleleaved forest, followed by evergreen needleleaved forest and grassland. The findings of this research are essential for improving leaf chlorophyll content inversion algorithms and for understanding and better use of LCC products in land surface models.
Details
- Language :
- English
- ISSN :
- 15698432
- Volume :
- 130
- Issue :
- 103930-
- Database :
- Directory of Open Access Journals
- Journal :
- International Journal of Applied Earth Observations and Geoinformation
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.8bbdacb5a1384f8d859170fb01225765
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.jag.2024.103930