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Global 250-meter resolution space-time continuous leaf area index satellite product generation method

A technology of leaf area index and resolution, used in neural learning methods, biological neural network models, instruments, etc.

Active Publication Date: 2021-09-10
WUHAN UNIV
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Problems solved by technology

Therefore, there are currently no LAI products that meet this requirement

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  • Global 250-meter resolution space-time continuous leaf area index satellite product generation method
  • Global 250-meter resolution space-time continuous leaf area index satellite product generation method
  • Global 250-meter resolution space-time continuous leaf area index satellite product generation method

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Embodiment Construction

[0031] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0032] like figure 1 As shown in a temporal resolution of 250 m LAI continuous global satellite product generation method includes the following steps:

[0033] Step 1: Cover Classification based on existing products and surface leaf area index, created on behalf of the world's major land cover types of training samples using cluster analysis and the minimum difference rule;

[0034] Adequate and representative sample of any training provided by Remote Sensing depth learning model. Our sampling strategy is to select the global sample, to ensure that they have enough time to change, but also on behalf of LAI ground truth. First, in order to reduce data redundancy, to ensure enough time to change LAI, by any one year (any year to 2000 a year, here take 2014) - time series GLASS (Global LAnd Surface Satellite, the global land table fe...

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Abstract

The invention relates to a global 250-meter resolution space-time continuous leaf area index satellite product generation method, which comprises the following steps of: step 1, creating a training sample capable of representing global main land coverage types by utilizing clustering analysis and a minimum difference rule based on an existing leaf area index and a land coverage classification product; step 2, determining an optimal leaf area index estimation model by training long and short term memory, a gating recursion unit and a bidirectional LSTM deep learning model; step 3, respectively applying the BiLSTM model to the surface reflectance of 500m and 250m of the MODIS to generate a leaf area index intermediate product with the resolution ratio of 500m and 250m; and step 4, combining two LAI intermediate products with the resolution of 250 meters and 500 meters by utilizing space-time weighted average post-processing so as to obtain a global leaf area index product with the resolution of 250 meters and space-time continuity. The invention can fill the blank of the current product in the high-latitude region, and is the only leaf area index product capable of meeting the requirement of simulating carbon cycle by the global climate observation system at present.

Description

Technical field [0001] The present invention belongs to the field of quantitative product generation satellite remote sensing, and more particularly to a global temporal resolution of 250 m LAI satellite continuous product generation. Background technique [0002] LAI (Leaf Area Index, LAI) is one of terrestrial essential climate variables Global Climate Observing System (GCOS) specified, widely used in terrestrial ecosystem model simulations, crop yield estimate and vegetation change monitoring and other scientific applications. Satellite observations provide the only reliable means to LAI time series of global mapping. The current global LAI products, there are some limitations. The most prominent problem is often subject to input albedo cloud or aerosol pollution and high concentrations, the resulting time series fluctuations in the product or missing data. For example in the region cloudy with snow, MODIS LAI missing data rate of up to 40%, although GLASS-LAI algorithm has be...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06K9/62G06N3/04G06N3/08G06F111/06
CPCG06F30/27G06N3/08G06F2111/06G06N3/044G06F18/23213
Inventor 马晗
Owner WUHAN UNIV
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