Method for inverting LAI (leaf area index) from HJ-1 satellite data

Abstract

The invention discloses a method for inverting LAI (leaf area index) from HJ-1 satellite data, which comprises the following four steps: 1, carrying out filtering on a time series MODIS (moderate resolution imaging spectroradiometer) LAI product subjected to projection transformation by using a filtering algorithm so as to generate space-time continued 1km LAI data, then, building a dynamic vegetation growth equation according to the LAI data subjected to filtering; 2, simulating a reflectivity generation lookup table of a canopy by using a canopy reflection model, and through combining the spectral features of the existing HJ-1 reflectivity data, carrying out adjustment on the lookup table, training the lookup table subjected to adjustment so as to obtain a conditional probability distribution from LAI to reflectivity; 3, based on a filter reasoning method of a dynamic Bayesian network, through combining the dynamic vegetation growth equation, the conditional probability distributionand the current-moment HJ-1 reflectivity data, calculating a current-moment HJ-1 posterior probability distribution so as to obtain a posterior probability / density distribution of LAI on the time series; and 4, according to the existing LAI posterior probability distribution, obtaining a time series LAI inversion result. The method disclosed by the invention is novel in conception, and has a broad application prospect in the technical field of satellite remote sensing.

Description

technical field [0001] The invention relates to a method for retrieving LAI from HJ-1 satellite data, which is a practical technical method for retrieving high-resolution vegetation parameter LAI using data obtained from domestic HJ-1 satellite, and can be applied in agriculture and environmental monitoring and other fields, which belong to the field of satellite remote sensing technology. Background technique [0002] LAI is one of the most basic parameters to characterize the vegetation canopy structure, defined as the sum of the single surface area of ​​all leaves per unit ground area. Satellite remote sensing provides an effective way to study LAI in a large area. At present, global LAI products with multiple sensors have been released, which meet the needs of many researches to a certain extent, but at the same time, there are still some deficiencies that need to be further improved and perfected. [0003] 1. High-resolution LAI products have not yet been released ...

AI Technical Summary

Problems solved by technology

[0005] 2. The inversion algorithm needs to be improved

They have their own advantages and disadvantages. The empirical formula method has a weak physical foundation and does not have universality and scalability. The latter has a certain physical foundation and the algorithm is efficient, so it is applied in the generation of global LAI products based on remote sensing data.

However, since remote sensing data are easily affected by weather conditions, the LAI products obtained based on these inversion methods have certain spatiotemporal discontinuity, which restricts their further application.

Images