Abstract
Remote sensing (RS)-derived vegetation indices (VIs) with medium and high spatial resolution have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring at local or global scales. Before they can be used as reliable inputs for other research, conducting in situ measurements for validation is very critical. However, the spatial heterogeneity due to the diversity of land cover and its spatial organization in the landscape increases the uncertainty of validation, so design of optimal sampling is an important basis for the reliability of the validation. In this paper, we propose an integrative stratified sampling strategy (INTEG-STRAT) based on normalized difference vegetation index (NDVI) data as prior knowledge. The basic idea is to realize a sampling optimization by determining the optimal combination of the spatial sampling method (e.g., simple random sampling (SRS), spatial system sampling (SYS), stratified sampling, generalized random tessellation stratified (GRTS), balanced acceptance sampling (BAS)) and spatial stratification scheme with an objective rule. The objective rule in this paper is to minimize the root mean square error (RMSE) of 10-fold cross validation between estimated values (sample are not included) and the corresponding values on prior knowledge. Relative precision, correlation coefficient, and RMSE are used to compare the effectiveness of the proposed sampling strategy with each sampling method without considering sampling optimization. After comparing, we find that the INTEG-STRAT requires fewer samples to become stable and has higher accuracy. At site 1, when the correlation coefficient between NDVI image and the simulated NDVI surface reached 80%, INTEG-STRAT needed only 70 sampling points while other methods require more sampling points. At the same time, INTEG-STRAT strategy has a smaller RMSE between the estimated values and the corresponding values on prior knowledge image. In general, INTEG-STRAT is an effective method in the selection of representative samples to support the validation of vegetation indices products with medium and high spatial resolution.
Highlights
There is a clear requirement for remote sensing vegetation indices (VIs) products with medium and high spatial resolution to be validated using reference data, such as in situ measurements, due to VIs’ significant role in validating coarse resolution products, together with modeling the ecological environment at local scale
Sampling strategy is one of the important factors to ensure the success of ground validation activities
We proposed an integrative stratified sampling strategy based on normalized difference vegetation index (NDVI) data as the prior knowledge
Summary
The spectral vegetation indices (VIs), based on remotely sensed reflectance in the near-infrared and visible bands, are deemed as the fundamental and most widely used indicators for many regional and global ecological and environmental applications [3,4]. Resolution Imaging Spectroradiometer (MODIS) VIs are still a good choice for regional or Remote Sens. Due to the relatively coarse spatial resolution from 250 to 1000 m, it can be difficult to detect vegetation characteristics in heterogeneous landscapes on a local scale. The global biophysical products derived from RS images with medium and high spatial resolution have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring [7]. The Global Climate Observing System (GCOS) has updated the more specific requirements for remote sensing at decametric resolution (
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