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遥感技术与应用  2022, Vol. 37 Issue (2): 474-487    DOI: 10.11873/j.issn.1004-0323.2022.2.0474
模型与反演     
基于机器学习算法的AMSR-2地表温度降尺度研究
李永康1,2(),王新军1,2(),马燕飞3,陈蓓1,2,闫立男1,2,张冠宏1,2
1.新疆农业大学草业与环境科学学院,新疆 乌鲁木齐 830052
2.新疆土壤与植物生态过程实验室,新疆 乌鲁木齐 830052
3.邯郸学院地理系,河北 邯郸 056005
Downscaling Land Surface Temperature through AMSR-2 Observations by Using Machine Learning Algorithms
Yongkang Li1,2(),Xinjun Wang1,2(),Yanfei Ma3,Bei Chen1,2,Linan Yan1,2,Guanhong Zhang1,2
1.College of Grassland and Environment Sciences,Xinjiang Agricultural University,Urumqi 830052,China
2.Xinjiang Key Laboratory of Soil and Plant Ecological Processes,Urumqi 830052,China
3.Department of Geography,Handan University,Handan 056005,China
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摘要:

MODIS日尺度的地表温度受到天气影响,有效像元信息严重缺失, 这对数据稀缺区域尤为重要。以古尔班通古特沙漠为研究区,探索了采用AMSR-2 的垂直极化亮度温度与植被指数对地表温度空间降尺度的方法,并用此方法填补了2018年MODIS的缺失像元。①通过十折交叉验证,对4种机器学习算法(Cubist、DBN、SVM、RF)、10个波段组合、2个空间尺度(5 km、10 km)下的训练模型进行了分析,表明RF算法精度明显高于其他3种算法,C09波段组合的验证精度高于其他波段组合。②构建了2个鲁棒性的随机森林算法地表温度降尺度模型(5 km|RF|09、10 km|RF|09),将AMSR-2亮度温度降尺度到1km分辨率,表明5 km|RF|09模型反演结果更为合理,MODIS与站点验证的R2分别为0.971、0.930,RMSE分别为3.38 K、4.71 K,MAE分别为2.51 K、3.84 K。③降尺度结果填补MODIS地表温度缺失像元,将其应用到古尔班通古特沙漠长时间序列的陆表温度分析,可为数据稀缺区域数据获取提供科学参考。

关键词: 陆表温度AMSR?2机器学习降尺度古尔班通古特沙漠    
Abstract:

MODIS dailyLand Surface Temperature (LST) products are seriously contaminated by weather effects and the effective pixel information missed. It is sincerely important in areas where data is sparse. An approach to downscaling LSTs from AMSR-2 vertical polarizations multi-brightness temperature and vegetation index observations was preliminarily investigated in the Gurbantunggut Desert, and then the downscaled LSTs were used to fill the gaps due to clouds in the MODIS of 2018.(1) In this study, four machine learning methods(Cubist、DBN、SVM、RF), two training spatial resolution(5 km、10 km ), ten band combinations, were applied to train the model. The 10-fold cross-validation results show that the RF model and C09 band combination have the best simulation effect. (2) Two robust downscaling methods of land surface temperature using Random Forest algorithm (5 km|RF|09/10 km|RF|09) were developed to retrieve a 1km-resolution land surface temperature product from Advanced Microwave Scanning Radiometer 2 (AMSR2) data. The validation results with MODIS and station LSTs show that 5 km|RF|09 downscaled LSTs has a better performance than 10 km| RF|09. Comparisons of the retrieval results with MODIS LSTs and ground measurement data from Fukang stations yielded that R2 respectively is 0.971、0.761, RMSE is 3.380 K、7.614 K and MAE is 2.509 K、6.695 K, which indicated that the accuracy of the 5 km|RF|09 LST retrieval model was high. (3) The downscaling results fill the gaps due to clouds in the MODIS, which can be applied to long-term LST sequence analysis in Gurbantunggut Desert. The method of LSTs downscaled provided scientific reference for data acquisition in data sparse area.

Key words: Land Surface Temperature    AMSR-2    Machine learning algorithm    Downscale    Gurbantunggut Desert
收稿日期: 2020-08-31 出版日期: 2022-06-17
ZTFLH:  P423  
基金资助: 国家自然科学基金项目“古尔班通古特沙漠稀疏固沙植被NPP对水热条件变化的响应”(41761085);国家自然科学基金项目“古尔班通古特沙漠南缘固沙植被斑块格局对降水脉动的响应研究”(41301205);国家自然科学基金项目(41701426);自治区研究生科研创新项目“基于深度学习的古尔班通古特沙漠地表温度降尺度研究”(XJ2020G152);邯郸学院校级重点项目(2016103)
通讯作者: 王新军     E-mail: yongkang_xau@163.com;wxj8112@163.com
作者简介: 李永康(1995-),男,河北张家口人,硕士研究生,主要从事陆面数据同化研究。E?mail: yongkang_xau@163.com
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引用本文:

李永康,王新军,马燕飞,陈蓓,闫立男,张冠宏. 基于机器学习算法的AMSR-2地表温度降尺度研究[J]. 遥感技术与应用, 2022, 37(2): 474-487.

Yongkang Li,Xinjun Wang,Yanfei Ma,Bei Chen,Linan Yan,Guanhong Zhang. Downscaling Land Surface Temperature through AMSR-2 Observations by Using Machine Learning Algorithms. Remote Sensing Technology and Application, 2022, 37(2): 474-487.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.2.0474        http://www.rsta.ac.cn/CN/Y2022/V37/I2/474

图1  研究区概况图审图号:GS(2020)4619
AMSR-2被动微波亮温数据MODIS数据
中心频率/GHz极化方式空间分辨率/km数据类型空间分辨率/km数据集
23.8V10

MYD

11A1

1LST_Day_1km QC_Day
36.5V10

MYD

13A2

1

1 km_16_days_EVI

1 km_16_days_NDVI

89V5
表1  AMSR-2与MODIS相关数据参数
图2  AMSR-2地表温度降尺度流程图

组合

序号

AMSR-2 被动微波亮温MYD13A2MYD11A1
36.5 Pol.V23.8 Pol.V36.5 Pol.V~23.8 Pol.V89 Pol.VNDVIEVILST
C01----
C02----
C03---
C04---
C05---
C06---
C07---
C08--
C09--
C10--
表2  10种特征和详情
图3  5 km尺度下10种特征组合的十折交叉验证结果
图4  10 km尺度下10种特征组合的十折交叉验证结果
图5  相关性r空间分布图
图6  基于5 km RF和10 km RF模型的反演的陆表温度与MODIS 陆表温度散点图
图7  时间序列上的5 km RF、10 km RF 模型的LST,5 km RF、10 km RF 模型的反演的LST与阜康站点 LST散点图
图8  2018年8月18日和19日的产品生成示意图
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