基于MODIS數(shù)據(jù)中國近地表氣溫反演

doi:10.12306/2024jms.0009

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基于MODIS數(shù)據(jù)中國近地表氣溫反演
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                        10.12306/2024jms.0009
                    
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中圖分類號:P407
基金項目:廣東鄉(xiāng)村地域系統(tǒng)野外科學(xué)觀測研究站(2021B212050026)

Inversion of near-surface air temperature in China based on MODIS data

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本文根據(jù)中國植被類型將全國劃歸為5個區(qū):熱帶闊葉雨林區(qū)、溫帶針葉闊葉混交林區(qū)、溫帶荒漠區(qū)、溫帶草原區(qū)以及高原高寒植被區(qū)。在每個區(qū)域內(nèi),基于1 km空間分辨率的MODIS數(shù)據(jù),利用遺傳算法(Genetic Algorithm,GA)優(yōu)化反向傳播神經(jīng)網(wǎng)絡(luò)(Backpropagation Neural Network,BPNN)算法構(gòu)造環(huán)境指標(biāo)和氣候的關(guān)系模型,實現(xiàn)對全國近地表氣溫的反演。結(jié)果表明:(1)相比多元線性回歸模型(Multivariable Linear Regression Model,MLR),GA-BPNN模型在氣溫估算中具有一定的優(yōu)越性,其決定性系數(shù)(Coefficient of Determination,R²)為0.90~0.94,均方根誤差(Root Mean Square Error,RMSE)為0.44~0.71℃;(2)在不同的時空分布格局中,GA-BPNN模型在植被豐富(如熱帶闊葉雨林區(qū)、溫帶針葉闊葉混交林區(qū))、地形復(fù)雜(如高原高寒植被區(qū))的地區(qū),其擬合精度遠(yuǎn)高于MLR模型;(3)在氣溫空間分布上,GA-BPNN模型所獲取的氣溫分布圖呈現(xiàn)出明顯的時空細(xì)節(jié)信息,氣溫整體呈現(xiàn)東南及西北干旱區(qū)高、東北及青藏高原低、平原高、山區(qū)低的格局,這與實際情況相符合。

Abstract:

This paper categorizes the entire country into five zones based on Chinese vegetation types: tropical broad-leaved rainforest, temperate coniferous and broad-leaved mixed forest, temperate desert, temperate grassland, and plateau alpine vegetation. In each zone,utilizing MODIS data with a 1km spatial resolution, the relationship between environmental indicators and climate was established by using the Genetic Algorithm (GA) and optimized Back Propagation Neural Network (BPNN) algorithms, which enables the modeling of the national near-surface temperature and its inversion. Results reveal that:(1) compared to the Multiple Linear Regression (MLR) model, the GA-BPNN model exhibits superior temperature estimation, with a coefficient of determination (R²) ranging from 0.90 to 0.94 and a Root Mean Square Error (RMSE) between 0.44-0.71 ℃.(2) In various spatial and temporal distribution patterns, the fitting accuracy of the GA-BPNN model significantly surpasses that of the MLR model in regions characterized by abundant vegetation (e.g., tropical broadleaf rainforest area, temperate coniferous broadleaf mixed forest area) and intricate topography (e.g., plateau alpine vegetation area).(3) Regarding the spatial distribution of air temperatures, the maps derived from the GA-BPNN model provide clear spatiotemporal details. (4) In terms of the spatial distribution of temperature, the temperature distribution map obtained by the GA-BPNN model presents obvious information on spatial and temporal details, and the temperature as a whole shows a pattern of high in the Southeast and Northwest arid zones, low in the Northeast and Tibetan Plateau regions, high in the plains, and low in the mountainous regions, which is in line with the actual situation.

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盧文婷,謝淑娟,文雅,林陳捷,劉振華.基于MODIS數(shù)據(jù)中國近地表氣溫反演.氣象科學(xué),2025,45(2):264-275 LU Wenting, XIE Shujuan, WEN Ya, LIN Chenjie, LIU Zhenhua. Inversion of near-surface air temperature in China based on MODIS data. Journal of the Meteorological Sciences,2025,45(2):264-275

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收稿日期:2023-08-07
最后修改日期:2024-01-17
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在線發(fā)布日期: 2025-07-03
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