基于SCLSTM模型的MODIS地表温度产品重建方法

doi:10.3969/j.issn.0253-4967.2023.06.006

地震地质 ›› 2023, Vol. 45 ›› Issue (6): 1349-1369.DOI: 10.3969/j.issn.0253-4967.2023.06.006

基于SCLSTM模型的MODIS地表温度产品重建方法

宋冬梅¹^,²⁾(), 张曼玉¹⁾^,^*(), 单新建³⁾, 王斌¹⁾

¹⁾ 中国石油大学(华东)海洋与空间信息学院, 青岛 266580
²⁾ 海洋矿物资源实验室, 青岛海洋科学技术国家实验室, 青岛 266071
³⁾ 中国地震局地质研究所, 北京 100029

收稿日期:2022-10-08 修回日期:2022-11-28 出版日期:2023-12-20 发布日期:2024-01-16
通讯作者: *张曼玉, 女, 1997年生, 硕士, 主要研究方向为地表温度补值及地震前热异常检测, E-mail: zhangmanyu1203@163.com。
作者简介:
宋冬梅, 女, 1973年生, 2003年于中国科学院沈阳应用生态研究所获理学博士学位, 教授, 主要研究方向为地震热红外异常信息提取, E-mail: songdongmei@upc.edu.cn。
基金资助:
国家重点研发计划项目(2019YFC1509202); 国家自然科学基金(41772350); 国家自然科学基金(61371189); 国家自然科学基金(41701513)

MODIS LAND SURFACE TEMPERATURE DATA RECONSTRUCTION BASED ON THE SCLSTM MODEL

SONG Dong-mei¹^,²⁾(), ZHANG Man-yu¹⁾^,^*(), SHAN Xin-jian³⁾, WANG Bin¹⁾

¹⁾ College of Oceanography and spatial information, China University of Petroleum, Qingdao 266580, China
²⁾ The Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China
³⁾ Institute of Geology, China Earthquake Administration, Beijing 100029, China

Received:2022-10-08 Revised:2022-11-28 Online:2023-12-20 Published:2024-01-16

摘要/Abstract

摘要：

MODIS(Moderate-resolution Imaging Spectroradiometer, 中分辨率成像光谱仪)LST(Land Surface Temperature, 地表温度)产品在大气物质和能量交换、气候变化研究及地震前兆热异常探测等方面具有重要价值。然而, 由于云的遮挡导致MODIS LST数据产品中存在大量空值, 限制了其广泛应用。为此, 文中提出了一种基于混合模型的地表温度重建方法——SCLSTM(即SSA-CLSTM)。与传统方法相比, 该方法无需建立复杂的回归关系模型。此外, 由于CNN(Convolutional Neural Network, 卷积神经网络)能够充分提取一维时间序列数据的局部特征, 而LSTM(Long Short-Term Memory, 长短期记忆)能够充分学习数据的长时间序列特征, 因此将CNN和LSTM结合能够更加充分地学习数据的潜在特征。首先, 使用SSA(Singular Spectrum Analysis, 奇异谱分析)模型提取出地表温度时间序列中的趋势值用于填补缺值像元, 实现地表温度的初步重建。然后, 再利用SCLSTM(即1DCNN-3层堆叠LSTM)模型学习数据的局部时序特征和长期依赖关系, 并实现对缺失像元的地表温度进行迭代预测, 完成数据的精细重建。新疆和田地区和四川汶川地区的实验结果表明, 文中方法与现有其他2种基于混合模型的重建方法相比, 重建后的LST数据误差最小, 与原始数据的一致性最高。其中, 文中方法的RMSE可降至0.712K, AD为0.695K, 重建后的LST数据与原始数据的相关系数可达0.95以上。此外, 气象站的实测地表温度数据也进一步验证了该方法的可靠性。文中所提方法为基于深度学习的LST重建工作提供了一种新的技术手段和思路, 同时也为基于LST的地表过程和地震热异常研究提供了坚实的数据基础。

关键词: 地表温度, SSA, CNN, LSTM, MODIS

Abstract:

MODIS land surface temperature(LST)products are of great value in the exchange of atmospheric matter and energy, climate change research, and detection of thermal anomalies as earthquake precursors. However, due to the influence of the cloud, there are a large number of missing values in the MODIS LST data products, limiting its wide application. Therefore, in this study we propose a method of surface temperature reconstruction based on mixed model: SCLSTM(SSA-CLSTM). Compared with traditional methods, this method does not need to establish a complex regression relationship model. In addition, since CNN can fully extract local features of one-dimensional time series data, and LSTM can fully learn long-term time series features of data, the combination of CNN and LSTM is capable of fully learning potential features of data.

Firstly in this study, the trend value of LST time series is extracted by SSA model to fill the missing pixel, and the initial reconstruction of LST is realized. Then, CLSTM(that is, 1DCNN, three-layer stacked LSTM)model is used to learn the local temporal characteristics and long-term dependence of the data, and the iterative prediction of the surface temperature of the missing pixel is realized to complete the fine reconstruction of the data. Based on the experimental results in Hotan region of Xinjiang and Wenchuan region of Sichuan, it can be proved that compared with the other two existing reconstruction methods based on mixed models, the reconstructed return data error is minimum, and the consistency with the original data is the highest. The RMSE of this method can be reduced to 0.712K, the minimum is 0.695K, and the correlation between the reconstructed return data and the original data can reach more than 0.95. In addition, the reliability of the method is further verified by the measured surface temperature data of the meteorological station. In summary, the proposed method provides a new technical means and ideas for deep learn-based reconstruction work, and also provides a solid data foundation for the research of surface processes and seismic thermal anomalies.

Using MODIS MYD11A2 remote sensing data and based on the proposed new method, the reconstruction experiments were carried out in Hotan region of Xinjiang Uygur Autonomous Region and Wenchuan region of Sichuan Province with the strategy of “remove-construction-contrast”, and the results were compared with other two mixed model methods. In addition, the reliability of the reconstruction accuracy of the proposed method is verified based on the 0cm measured surface temperature data of the weather station. The main conclusions are as follows:

(1)Through the reconstruction experiment of LST in Hotan, Xinjiang, it is concluded that compared with the existing two hybrid model reconstruction methods, the new method can better capture the time series features of LST data, so that the reconstructed image can not only better maintain the texture features of the original image, but also improve the accuracy of data reconstruction. The reconstruction error is the smallest among the three methods, RMSE can be reduced to 0.712K, and the correlation with the original data can reach more than 0.95 after reconstruction.

(2)In order to prove the regional applicability of the new method, a reconstruction experiment was carried out in Wenchuan region of Sichuan Province, and the missing values were reconstructed using the proposed method. Through this reconstruction experiment, we found that the reconstruction method in this paper can achieve better data reconstruction effect even in areas with more cloud and fog coverage, poor weather conditions, and complex land cover types, which proves the reliability and regional universality of the method in this paper.

(3)To further verify the reliability of the new method, the accuracy of the new method was evaluated by using the measured 0cm surface temperature data of 6 meteorological stations in Hotan region of Xinjiang. Based on the temporal variation characteristics of the MODIS return data from 2015 to 2019, the return values of 2020 are reconstructed, and the reconstructed results are compared with the measured data. By comparing the 0cm measured data of the meteorological station and the data before and after reconstruction, it can be concluded that the correlation and average deviation of the returned data and measured data after reconstruction based on SCLSTM method are closer to the correlation and average deviation of the original data and measured data. Therefore, the reconstructed data based on the new method can maintain a good consistency with the original data.

(4)By reconstructing the missing value regions of Hotan region of Xinjiang in 2008 and Wenchuan region of Sichuan in 2020, we found that the texture of the images after the supplementary value is fine and natural, without obvious boundary effect. Therefore, it can be proved that the method in this paper can realize the data reconstruction of a large area with missing values.

In summary, the method proposed in this paper provides a new idea and technique for MODIS surface temperature reconstruction work based on deep learning, and also provides a solid data foundation for the ground process research and seismic thermal anomaly information extraction based on MODIS LST.

Key words: Land surface temperature, SSA, CNN, LSTM, MODIS

宋冬梅, 张曼玉, 单新建, 王斌. 基于SCLSTM模型的MODIS地表温度产品重建方法[J]. 地震地质, 2023, 45(6): 1349-1369.

SONG Dong-mei, ZHANG Man-yu, SHAN Xin-jian, WANG Bin. MODIS LAND SURFACE TEMPERATURE DATA RECONSTRUCTION BASED ON THE SCLSTM MODEL[J]. SEISMOLOGY AND GEOLOGY, 2023, 45(6): 1349-1369.

图/表 18

图1 研究区概况

Fig. 1 Overview of study area.

图2 重建方法的流程

Fig. 2 Method showing data reconstruction process in this study.

图3 基于SSA方法的LST初步重建过程

Fig. 3 Process of preliminary LST data reconstruction based on the SSA method.

图4 一维卷积层结构及卷积核滑动方式示意图

Fig. 4 Schematic diagram of the structure of one-dimensional convolutional layers and the mode of kernel slide.

图5 LSTM模型预测示意图

Fig. 5 Schematic diagram of the LSTM model.

图6 SCLSTM模型中不同LSTM层数的数据重建精度

Fig. 6 Data reconstruction accuracy of different LSTM layers in SCLSTM model.

表1 CNN与LSTM模型的参数信息

Table1 Parameter information of CNN and LSTM model

模型	参数	数值
CNN	卷积层	5
	卷积核大小	1×3
	空洞率	1/2/4/8/16
	通道数	32
LSTM	层数	3
	神经元数	128/64/32
	步长	1
	学习率	0.002

图7 卷积核权重与LST数据局部序列段之间的卷积运算

Fig. 7 Convolution operation between kernel weight and local segments of LST time series.

图8 3层堆叠LSTM精细化重建过程

Fig. 8 Process of refined LST data reconstruction with the three-layer superimposed LSTM.

图9 LST时序数据的迭代精细重建过程

Fig. 9 Process of refined reconstruction of LST time series data through iteration.

图10 CNN-LSTM与对比方法重建效果的对比为了清晰地展示重建结果的差异之处, 仅对抠值附近小范围的图像进行了展示, 经纬度范围为 (34.5°~35.0°N, 80.0°~80.5°E)。红色框部分表示抠值区域, 此图为新疆和田地区

Fig. 10 Comparison of reconstruction effects between CNN-LSTM and LSTM.

图11 新疆和田地区LST重建精度与效率分析 “2008-29”为2008年第29期数据,“2008-35”为2008年第35期数据

Fig. 11 Accuracy and efficiency analysis of LST reconstruction in Hotan region, Xinjiang.

图12 CNN-LSTM与对比方法重建效果的对比为了清晰地展示重建结果的差异之处, 仅对抠值附近小范围的图像进行了展示, 经纬度范围为 (31.5°~32.0°N, 100.0°~100.5°E)。红色框部分表示抠值区域, 此图为四川汶川地区

Fig. 12 Comparison of reconstruction effects between CNN-LSTM and LSTM.

图13 四川汶川地区LST重建精度与效率分析 “2020-8”为2020年第8期数据, “2020-44”为2020年第44期数据

Fig. 13 Accuracy and efficiency analysis of LST reconstruction in Wenchuan region, Sichuan.

表2 气象站的空间位置

Table2 The spatial locations of the meteorological stations

站点	北纬/(°)	东经/(°)
皮山	37.37	78.17
墨玉	37.1	79.38
和田	37.08	79.56
洛浦	37.05	80.10
策勒	37.01	80.48
于田	36.51	81.39

图14 基于气象站实测LST的可靠性分析

Fig. 14 Reliability analysis of measured LST based on meteorological station.

图15 新疆和田重建前后对比为了清晰地展示重建结果的差异之处, 仅对研究区内缺值严重的区域进行了展示, 经纬度范围为(31.5°~32.5°N, 82.0°~83.5°E)。“2008-1”为2008年第1期数据, “2008-2”为2008年第2期数据, “2008-45”为2008年第45期数据

Fig. 15 Comparison of the original and reconstructed LST images for the Hotan region in Xinjiang.

图16 四川汶川重建前后对比为了清晰地展示重建结果的差异之处, 仅对研究区内缺值严重的区域进行了展示, 经纬度范围为(29.0°~30.0°N, 102.0°~103.0°E)。“2020-14”为2020年第14期数据, “2020-31”为2020年第31期数据, “2020-32”为2020年第32期数据

Fig. 16 Comparison of the original and reconstructed LST images for the Wenchuan region in Sichuan.

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基于SCLSTM模型的MODIS地表温度产品重建方法

MODIS LAND SURFACE TEMPERATURE DATA RECONSTRUCTION BASED ON THE SCLSTM MODEL

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