基于机器学习的遥感反演：不确定性因素分析

汪静平; 吴小丹; 马杜娟; 闻建光; 肖青

doi:10.11834/jrs.20221172

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基于机器学习的遥感反演：不确定性因素分析
Remote sensing retrieval based on machine learning algorithm： Uncertainty analysis
2023年27卷第3期页码：790-801
纸质出版日期： 2023-03-07 ，
DOI： 10.11834/jrs.20221172

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汪静平，吴小丹，马杜娟，闻建光，肖青.2023.基于机器学习的遥感反演：不确定性因素分析.遥感学报，27（3）： 790-801

Wang J P，Wu X D，Ma D J，Wen J G and Xiao Q. 2023. Remote sensing retrieval based on machine learning algorithm： Uncertainty analysis. National Remote Sensing Bulletin， 27（3）：790-801
汪静平，吴小丹，马杜娟，闻建光，肖青.2023.基于机器学习的遥感反演：不确定性因素分析.遥感学报，27（3）： 790-801 DOI： 10.11834/jrs.20221172.

Wang J P，Wu X D，Ma D J，Wen J G and Xiao Q. 2023. Remote sensing retrieval based on machine learning algorithm： Uncertainty analysis. National Remote Sensing Bulletin， 27（3）：790-801 DOI： 10.11834/jrs.20221172.

摘要

遥感反演是定量遥感的核心内容之一，只有实现高精度的参数反演，才能提高遥感数据的利用效率，从而进一步推进定量遥感的发展。基于机器学习的遥感参数反演充分利用了卫星大数据的优势，能够避免因物理模型带来的复杂处理和计算过程，同时减小由遥感数据预处理包括几何校正、辐射校正、大气校正等引起的不确定性，因此被应用于遥感反演中。然而，机器学习在遥感反演中的效用和能力需要客观看待。本文重点对机器学习在遥感反演中的应用进行论述。首先在文献检索的基础上总结了目前机器学习在遥感反演方面的应用现状。然后总结了几种典型的机器学习算法的原理及特点。最后，重点论述了基于机器学习的遥感反演不确定性产生的几种主要因素，包括机器学习算法的选择、辅助变量的选择、变量数据集的来源及准确性、训练样本的选择和模型的跨尺度、跨区域应用等。在机器学习算法的实际应用中，应综合考虑这些因素可能会引起的不确定性，从而选择最为合适的算法和样本，提高结果的准确度和可信度。

Abstract

Remote sensing retrieval is one of the core issues of quantitative remote sensing. High-precision retrieval can improve the utilization efficiency of satellite data and promote the development of quantitative remote sensing. Machine learning algorithms have been increasingly used in remote sensing domains due to the outstanding advantages in dealing with complex and nonlinear problems. They can avoid the complicated processing and calculation in physical models

and minimize the uncertainty resulting from data preprocessing such as geometric correction

radiometric correction

and atmospheric correction. However

the utility of machine learning algorithms in retrieval needs to be viewed objectively. For instance

which machine learning algorithm is the most appropriate or which parameter configuration is optimal in order to obtain more accurate retrieval results. What we are concerned about most is which factors might influence the accuracy of retrievals. Hence

this paper systematically combs the current situation and principles of the application of machine learning algorithms in remote sensing retrieval with the focus on the main uncertainty factors in machine learning-based retrieval.

It was found that the number of relevant articles surged especially after 2018. Several mainstream algorithms including random forests

support vector machine

and artificial neural network have been widely used in remote sensing retrieval. And the retrievals of vegetation index

leaf area index

soil moisture

chlorophyll content

and biomass are the main research hotspots at present. MODIS and Sentinel datasets are the most widely used data. The process of machine learning-based retrieval can be summarized as the acquisition of training samples as well as the construction and application of the training model. The main factors causing the uncertainty of retrievals including the selection of machine learning models

the selection of auxiliary variables

the source and accuracy of the variable datasets

the selection of training samples

and the cross-regional and cross-area application of models were discussed in this paper. The findings are helpful in deepening the consciousness and understanding of the uncertainties of the retrieval results based on machine learning algorithms

which is necessary to select the most suitable algorithm and samples for improving the accuracy and reliability of the results.

Moreover

it is important to note that there is a trade-off between the model accuracy and complexity for machine learning algorithms. Therefore

they may be not feasible in solving different remote sensing problems. Further retrieval based on machine learning models may be developed from the following four aspects: 1) Selecting more detailed information to fully capture the spatial heterogeneity of land surface and the spatiotemporal variation characteristics of parameters; 2) The training samples should be more representative to make the models more universal; 3) The scale mismatch between the

in situ

measurements and auxiliary variables extracted from satellite products should be fully taken into consideration to reduce the uncertainty caused by the scale effect; 4) The combination of physical models and machine learning algorithms can improve the representativeness of training samples

providing a new way for the construction of training samples.

关键词

机器学习反演不确定性定量遥感深度学习文献分析

Keywords

machine learningretrievaluncertaintyquantitative remote sensingdeep learningliterature analysis

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