Evaluation of MODIS NDVI product in a heterogeneous urban environment using five upscaling methods and Landsat 8 product

In order to accurately assess the quality of low-resolution biogeophysical parameter products, accurate scale transformations are essential. However, different scaling models often lead to inconsistent transformation results. More worryingly, many biogeophysical parameters are not scale-invariant, such as the Normalized Difference Vegetation Index (NDVI), which makes the quality assessment of low-resolution products even more challenging. Therefore, we propose an integrated approach that utilizes multiple upscaling methods and high-quality, moderate-resolution surface reflectance products to evaluate the quality of low-resolution MODIS NDVI products, eliminating the need for extensive in-situ observation data. In this study, the full-scale transformation of Landsat 8 OLI NDVI in heterogeneous urban environments is realized using five upscaling methods, including two reflectance-level Taylor series expansion (TSE) models, the simple averaging method, the Chen NDVI model, and the point spread function (PSF) method. Finally, the overall quality of the MOD13Q1 product in the study area was evaluated based on the upscaled NDVI images. Our study provides quantitative insights into the underlying causes of scale effects in NDVI, including the spatial heterogeneity of the surface and the nonlinearity of the NDVI model. Furthermore, the TSE method, which integrates land cover types, and the PSF method were first practically applied to the study of upscaling NDVI. The integration of land cover types in the TSE method and the consideration of specific weights for “small pixels” in the PSF method offer nuanced insights. Our findings affirm the overall high quality of the MOD13Q1 product and the overall bias between the MOD13Q1 images and the corresponding upscaled NDVI images for the entire study area, Xiamen city, which ranged from 0.0176 to 0.0225 in absolute value (mean difference) and from 0 to 0.0071 in absolute value (standard deviation difference). This study advances our understanding of NDVI scale effects and demonstrates that the proposed method serves as an efficient and effective way to evaluate the overall quality of low-resolution constructed biogeophysical parameters that lack scale-invariant characteristics in expansive areas with insufficient in-situ observation data.