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High-Spatial-Resolution NDVI Reconstruction with GA-ANN.

Yanhong Zhao¹, Peng Hou², Jinbao Jiang¹

¹School of Earth Science and Mapping Engineering, China University of Mining and Technology, Beijing 100083, China.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

This study introduces a novel method using a genetic algorithm-artificial neural network (GA-ANN) to reconstruct the Normalized Differential Vegetation Index (NDVI) from Landsat data, effectively filling gaps caused by clouds and improving time-series continuity.

Keywords:

GA-ANN Landsat MODIS NDVI high spatial resolution reconstruction algorithm

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Area of Science:

Remote Sensing
Geospatial Analysis
Artificial Intelligence in Earth Observation

Background:

Normalized Differential Vegetation Index (NDVI) derived from Landsat exhibits temporal discontinuities due to long revisit times and cloud cover.
Existing methods struggle to accurately reconstruct NDVI data, particularly in areas with complex land use.
Filling these gaps is crucial for reliable vegetation monitoring and analysis.

Purpose of the Study:

To develop and validate a novel method for reconstructing Landsat NDVI data affected by clouds, cloud shadows, and uncovered areas.
To leverage Moderate Resolution Imaging Spectroradiometer (MODIS) data for wide-area coverage in the reconstruction process.
To compare the performance of the proposed method against existing algorithms like ESTARFM and FSDAF.

Main Methods:

Proposed a reconstruction method based on the genetic algorithm-artificial neural network (GA-ANN) algorithm.
Utilized MODIS data characteristics to fill and reconstruct discontinuous Landsat NDVI time series.
Validated the reconstructed NDVI data using self-testing (RMSE, MAE, R) and comparison with Sentinel NDVI data.

Main Results:

The GA-ANN model achieved high accuracy with RMSE of 0.0508, MAE of 0.0557, and R of 0.8971 in self-validation.
The GA-ANN method outperformed ESTARFM and FSDAF in reconstructing NDVI for complex land use types.
Validation with Sentinel NDVI data showed a strong correlation (R > 0.97) for cropland, forest, and grassland, confirming the method's effectiveness.

Conclusions:

The GA-ANN based reconstruction model effectively fills gaps in Landsat NDVI data caused by clouds and shadows.
This method produces high-resolution, long-term NDVI series data suitable for various applications.
The approach offers superior performance, especially for complex land cover types, enhancing vegetation monitoring capabilities.