The MODIS normalized difference vegetation index (NDVI) product plays an important role in the eco-environmental monitoring of natural disasters. However, its validation has been a long standing and important scientific problem. The paper proposed a method to integrate accurate classification information for medium-high spatial resolution remote sensing images to improve the traditional Chen NDVI scale conversion model and perform MOD13 Q1 validation. The authors had verified the method in the research area of Xiamen, Fujian Province, China, and the experimental results proved its effectiveness. This paper focuses on the availability research of the model in different experimental areas. Taking Fuzhou City of Jiangxi Province, China, as the study area, the MOD13 Q1 validation experiment was implemented. The conclusions are obtained from the experimental results: the Chen NDVI scale transformation model is not robust, and in some experimental areas there is significant transformation error when the conversion factor is too large (such as eightfold from 30 m OLI NDVI to 240 m up-scaled NDVI). In these bad cases, other more robust scale transformation models should be elected for the validation of the low-resolution land surface parameter images.
Heterogeneous land surface causes the scale effect of remotely sensed land surface parameters. Addressing on quantitatively describing the influence of different ground objects on scale effect of the common surface parameter normalized difference vegetation index (NDVI), the paper proposed an improved NDVI scale transformation model. The model integrated accurate classification information from medium- or high- spatial resolution remote sensing images to improve the traditional Chen NDVI scale conversion model, and showed its superiority for NDVI scale effect description. Xiamen was taken as the experimental area for the study and the conclusions could be obtained from the experimental results. Compared with the traditional Chen NDVI model with rough information, the improved Chen NDVI model incorporating fine ground information provides a finer and more quantitative description of the influence of different land types on the NDVI scale effect. Furthermore, it is found that the presence of water is the key factor underlying the NDVI scale effect. The conclusions of this study have important implications for the scale effect research of other NDVI-like surface parameters such as ratio vegetation index (RVI), normalized difference built-up index (NDBI), normalized burn ratio (NBR).
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