A Model-Downscaling Method for Fine-Resolution LAI Estimation

The leaf area index (LAI) is a critical parameter for characterizing the structure and function of vegetation in ecosystems. Currently, operational LAI products always have coarse spatial resolution, and fine-resolution LAI maps are urgently needed for ecological environment assessment and the preci...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-12, Vol.12 (24), p.4147
Main Authors: Zhang, Jingyu, Wang, Jindi, Sun, Rui, Zhou, Hongmin, Zhang, Helin
Format: Article
Language:English
Subjects:
Accuracy
Agricultural land
Algorithms
downscaling modeling
Environmental assessment
Environmental monitoring
Evaluation
fine resolution
Fractals
Landsat
Landsat satellites
Leaf area
Leaf area index
Mathematical models
model parameters
Normalized difference vegetative index
Parameters
Remote sensing
Scaling
Spatial discrimination
Spatial resolution
Structure-function relationships
Vegetation
Vegetation index
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Summary:The leaf area index (LAI) is a critical parameter for characterizing the structure and function of vegetation in ecosystems. Currently, operational LAI products always have coarse spatial resolution, and fine-resolution LAI maps are urgently needed for ecological environment assessment and the precise monitoring of cropland growth. LAI downscaling methods are efficient at improving the spatial resolution of LAI products but often ignore the scaling effect of the model. In this study, a novel model-downscaling method is proposed for fine-resolution LAI estimation. It uses scaling equations of model parameters (SEMPs) to describe the scaling relations of models at different spatial resolutions and construct a downscaled model from a coarse-resolution model. Landsat Normalized Difference Vegetation Index (NDVI) at 30 m and Global LAnd Surface Satellite (GLASS) LAI at 1 km spatial resolutions are used because they are readily available. The downscaled model is evaluated by a fine-resolution model directly constructed with fine-resolution data. The fine-resolution LAI values estimated by this model-downscaling method are evaluated with field LAI measurements. The validation results show that the proposed method can generate highly accurate LAIs, with an RMSE of 0.821 at the Pshenichne cropland site in Ukraine and an RMSE of 0.515 at the Camerons forest site in Australia when compared with field LAI measurements. The results are also better than those of Ovakoglou’s downscaling method. These results demonstrate that the model-downscaling method for fine-resolution LAI estimation is viable and referable for related studies.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12244147