Scaling Effect of Area-Averaged NDVI: Monotonicity along the Spatial Resolution

Abstract

Changes in the spatial distributions of vegetation across the globe are routinely monitored by satellite remote sensing, in which the reflectance spectra over land surface areas are measured with spatial and temporal resolutions that depend on the satellite instrumentation. The use of multiple synchronized satellite sensors permits long-term monitoring with high spatial and temporal resolutions. However, differences in the spatial resolution of images collected by different sensors can introduce systematic biases, called scaling effects, into the biophysical retrievals. This study investigates the mechanism by which the scaling effects distort normalized difference vegetation index (NDVI). This study focused on the monotonicity of the area-averaged NDVI as a function of the spatial resolution. A monotonic relationship was proved analytically by using the resolution transform model proposed in this study in combination with a two-endmember linear mixture model. The monotonicity allowed the inherent uncertainties introduced by the scaling effects (error bounds) to be explicitly determined by averaging the retrievals at the extrema of the resolutions. Error bounds could not be estimated, on the other hand, for non-monotonic relationships. Numerical simulations were conducted to demonstrate the monotonicity of the averaged NDVI along spatial resolution. This study provides a theoretical basis for the scaling effects and develops techniques for rectifying the scaling effects in biophysical Remote Sens. 2012, 4 161 retrievals to facilitate cross-sensor calibration for the long-term monitoring of vegetation dynamics.

Biophysical Properties of Cultivated Pastures in the Brazilian Savanna Biome: An Analysis in the Spatial-Temporal Domains Based on Ground and Satellite Data

“…Our results indicate that simple metrics, based on the combined use of moderate and high spatial resolution images, can depict major spatial and temporal biophysical variations at the landscape scale. Nevertheless, consistent biophysical retrieval and monitoring of pasture dynamics needs to take into account the effects of scaling, which varies according to the degree of spatial averaging involved and also depends on the nature, distribution patterns, and size of neighboring end-member targets [43]. The accuracy and precision of ground to image extrapolation is also proportional to the amount, area distribution and frequency of the samples, based on which, the utilized translation equations were defined and constrained [44].…”

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

“…Following [36,50], in Equation (14) the near-infrared and red reflectances for bare soil were set at 0.11 and 0.08, respectively, and for full vegetation cover at 0.5 and 0.05. However, these values generally change with many factors such as soil types, vegetation types and atmospheric conditions, as well as scaling [52]. It becomes very difficult to endorse this approach, especially for satellite remote sensing which is characterized by atmospheric effects and multi-temporal changes.…”

Spatial Up-Scaling Correction for Leaf Area Index Based on the Fractal Theory

“…The effects of the LAI retrieval model type on LAI scale transformation modeling with fractal theory should be considered in our future work. Besides, the nonlinearity relationship between NDVI and the reflectance of red and near-infrared bands (scaling effect of NDVI) also contributes to the scaling effect of LAI products [31,[44][45][46]. Our future work will also further investigate this issue.…”