Method and system for improving the accuracy of satellite remote sensing inversion by integrating hyperspectral proximal sensing

A method for improving the accuracy of satellite remote sensing inversion by integrating hyperspectral proximal sensing, comprising the steps of:

• Step 1:
• acquiring a water body hyperspectral proximal sensing reflectance spectrum dataset, a satellite remote sensing surface reflectance dataset, and a water environment in-situ measurement dataset, and selecting corresponding data preprocessing methods to perform preprocessing;
• Step 2:
• determining an optimal matching window by using a temporal matching principle and constructing a synchronous dataset;
• wherein the synchronous dataset comprises:
• synchronous data of SRrs(λ) and water quality, and synchronous data of fnR(λ) and water quality;
• wherein the SRrs(λ) is preprocessed satellite remote sensing surface reflectance data, the fnR(λ) is preprocessed water body hyperspectral proximal sensing reflectance spectrum data, and λ is a band;
• wherein the steps for determining the optimal matching window comprise:
• defining a sum of coefficients of determination for all bands as S for screening out an optimal temporal matching window, with the calculation formula being:
• S
• =
• R
• 443
• 2
• +
• R
• 490
• 2
• +
• R
• 560
• 2
• +
• R
• 665
• 2
• +
• R
• 705
• 2
• +
• R
• 740
• 2
• +
• R
• 783
• 2
• ;
• wherein
• R
• 443
• 2
• ,
• R
• 490
• ,
• 2
• ,
• R
• 560
• 2
• ,
• R
• 665
• 2
• ,
• R
• 705
• 2
• ,
• R
• 740
• 2
• ,
• R
• 783
• 2
• are coefficients of determination for bands λ∈[443, 490, 560, 665, 705, 740, 783], respectively;
• and selecting a temporal matching window corresponding to a maximum value of S as the optimal matching window;
• Step 3:
• creating a method for simulating multispectral data from hyperspectral data, generating corresponding synchronous data of nGRrs(λ) and water quality based on the synchronous data of fnR(λ) and water quality, and obtaining synchronous data of SRrs(λ) and nGRrs(λ), wherein the nGRrs(λ) is simulation-derived satellite remote sensing surface reflectance;
• Step 4:
• performing normalization processing on the preprocessed satellite remote sensing surface reflectance data SRrs(λ) based on the synchronous data of SRrs(λ) and nGRrs(λ) to obtain new satellite remote sensing surface reflectance data nSRrs(λ), performing a comparative analysis between the new satellite remote sensing surface reflectance data nSRrs(λ) and the simulation-derived satellite remote sensing surface reflectance nGRrs(λ), and establishing a quantitative conversion relationship between nSRrs(λ) and nGRrs(λ) band-by-band to eliminate error;
• Step 5:
• combining the synchronous data of nGRrs(λ) and water quality and the synchronous data of nSRrs(λ) and water quality to generate a fused satellite-ground dataset;
• and using the synchronous data of SRrs(λ) and water quality and the fused satellite-ground dataset as input features, respectively, and using the water environment in-situ measurement dataset as an output feature, to construct and train a water quality parameter inversion model;
• and
• Step 6:
• repeating Step 3 to Step 5, updating the method for simulating multispectral data from hyperspectral data, a method for enhancing satellite spectral band precision, and the fused satellite-ground dataset in real time, continuously optimizing the water quality parameter inversion model, and displaying and downloading inversion results according to a query instruction until a query termination instruction from a user is received.