Abstract
High-resolution satellite images can be used to some extent to mitigate the mixed-pixel problem caused by the lack of intensive production, farmland fragmentation, and the uneven growth of field crops in developing countries. Specifically, red-edge (RE) satellite images can be used in this context to reduce the influence of soil background at early stages as well as saturation due to crop leaf area index (LAI) at later stages. However, the availability of high-resolution RE satellite image products for research and application globally remains limited. This study uses the weight-and-unmixing algorithm as well as the SUPer-REsolution for multi-spectral Multi-resolution Estimation (Wu-SupReME) approach to combine the advantages of Sentinel-2 spectral and Planet spatial resolution and generate a high-resolution RE product. The resultant fused image is highly correlated (R2 > 0.98) with Sentinel-2 image and clearly illustrates the persistent advantages of such products. This fused image was significantly more accurate than the originals when used to predict heterogeneous wheat LAI and therefore clearly illustrated the persistence of Sentinel-2 spectral and Planet spatial advantage, which indirectly proved that the fusion methodology of generating high-resolution red-edge products from Planet and Sentinel-2 images is possible. This study provided method reference for multi-source data fusion and image product for accurate parameter inversion in quantitative remote sensing of vegetation.
Highlights
Low agriculture intensification in most countries, especially developing ones, results in a disperse distribution of fields and uneven crop growth [1]. This means that low- to medium-resolution satellite images tend to suffer from mixed-pixel problems, so high-resolution images are required for crop growth monitoring in such areas [2]
Leaf area index (LAI) is a key parameter for vegetation monitoring, because it is correlated with wheat canopy structure and is related to both canopy chlorophyll contents and photosynthesis rate [3,4]
The coarse resolution of RE bands influences LAI predictive accuracy when using RE-vegetation indices (VIs) during Sentinel-2 applications. In light of this result, Clevers et al [12] structured RE-VIs and none-RE-VIs using original Sentinel-2 images, and the outcomes of this study showed that red-edge chlorophyll index (CIred-edge) at 20 m resolution were less accurate than their 10 m resolution weighted difference vegetation index (WDVI) counterparts for predicting potato LAI
Summary
Low agriculture intensification in most countries, especially developing ones, results in a disperse distribution of fields and uneven crop growth [1]. This means that low- to medium-resolution satellite images tend to suffer from mixed-pixel problems, so high-resolution images are required for crop growth monitoring in such areas [2]. Leaf area index (LAI) is a key parameter for vegetation monitoring, because it is correlated with wheat canopy structure and is related to both canopy chlorophyll contents and photosynthesis rate [3,4]. Predicting LAI over large spatial scales has become possible due to the development of satellite technology and can be implemented via models that relate this variable to satellite reflectance or vegetation indices (VIs). RE-VIs have the capacity to reduce the influence of soil on LAI predictions at early stages and remove saturation due to high LAI at later stages [5,6,7,8]
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