融合多非线性因素的MODIS PWV神经网络差分校正模型

doi:10.11947/j.AGCS.2024.20230233

测绘学报 ›› 2024, Vol. 53 ›› Issue (11): 2138-2148.doi: 10.11947/j.AGCS.2024.20230233

• 大地测量学与导航 • 上一篇

融合多非线性因素的MODIS PWV神经网络差分校正模型

王梦瑶¹(), 张书毕¹^,², 张文渊¹^,²(), 刘洋¹

^1.中国矿业大学环境与测绘学院，江苏　徐州　221116
^2.中国矿业大学自然资源部国土环境与灾害监测重点实验室，江苏　徐州　221116

收稿日期:2023-06-15 发布日期:2024-12-13
通讯作者: 张文渊 E-mail:mywang@cumt.edu.cn;zhangwy@cumt.edu.cn
作者简介:王梦瑶（1998—），女，硕士，研究方向为GNSS气象学。　E-mail：mywang@cumt.edu.cn
基金资助:
国家自然科学基金(42271460);江苏省自然科学基金(BK20241669);中央高校基本科研业务费专项资金(2024QN11077)

MODIS PWV neural network differential correction model integrating multiple nonlinear factors

Mengyao WANG¹(), Shubi ZHANG¹^,², Wenyuan ZHANG¹^,²(), Yang LIU¹

^1.School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
^2.MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China

Received:2023-06-15 Published:2024-12-13
Contact: Wenyuan ZHANG E-mail:mywang@cumt.edu.cn;zhangwy@cumt.edu.cn
About author:WANG Mengyao (1998—), female, master, majors in GNSS meteorology.　E-mail: mywang@cumt.edu.cn
Supported by:
The National Natural Science Foundation of China(42271460);The Natural Science Foundation of Jiangsu Province(BK20241669);The Fundamental Research Funds for the Central Universities(2024QN11077)

摘要/Abstract

摘要：

MODIS水汽产品凭借其高空间分辨率的优势成为重要的大气水汽产品，但由于降水、云层、地表反射光谱不确定等因素的影响，其反演精度有限。为有效提高MODIS水汽产品质量，本文通过分析云、地表类型、像元姿态、时间及位置等非线性因素，构建了一个融合多类型非线性因素的MODIS PWV神经网络差分校正模型。首先，分析MODIS PWV与同址高精度GNSS PWV观测值的相关性，并将两者的差值PWV_diff作为神经网络模型的目标值，以MODIS产品的云掩膜置信度、地表覆盖类型、周期项年积日、周期项日积时、传感器天顶角、太阳天顶角、传感器方位角和太阳方位角等19个非线性因素作为模型的输入信息。与传统线性校正模型对比，校正后MODIS PWV的均方根误差（RMSE）由3.271 3 mm降低为2.360 2 mm，精度提高了27.85%。以高时空ERA5 PWV数据为参考值评估模型性能，试验结果表明，本文模型校正后MODIS PWV的均方根误差为2.037 4 mm，较未校正MODIS PWV（RMSE为4.850 3 mm）精度提高了57.99%；进一步地，针对云掩膜产品提供的4种置信度下的MODIS PWV产品分别构建神经网络差分校正模型，结果表明大于99%、大于95%、大于66%和小于66% 4种云置信度的MODIS PWV产品较未校正MODIS PWV的RMSE分别提高了60.03%、61.21%、55.72%和54.57%。说明本文模型针对各种云覆盖下的MODIS PWV产品精度提高均具有较高的普适性，有望为气候变化和降雨预报研究提供高精度水汽信息。

关键词: MODIS PWV, GNSS PWV, PWV差分校正, BP神经网络, ERA5 PWV

Abstract:

MODIS water vapor products have become important atmospheric water vapor productsdue to their advantages of high spatial resolution, however, due to uncertain factors such as precipitation, cloud mask, and surface reflection spectrum, the inversion accuracy is limited. To effectively improve the quality of MODIS water vapor products, this article analyzes nonlinear factors such as cloud, land cover type, pixel attitude, time, and position, and a MODIS PWV neural network differential correction model integrating multiple nonlinear factors is constructed for the first time. Firstly, the correlation between MODIS PWV and GNSS PWVat the same site is analyzed, PWV_diff (the difference between the two) is taken as the target value of the neural network model, and the input information for the model is 19 nonlinear factors such as cloud mask confidence, land cover type, periodic day of year, periodic hour of day, sensor zenith angle, solar zenith angle, sensor azimuth angle, and solar azimuth angle. Compared with traditional linear correction model, the root mean square error (RMSE) of corrected MODIS PWV is reduced from 3.271 3 mm to 2.360 2 mm, and the accuracy is improved by 27.85%. The performance of the model isalso evaluated by using high spatiotemporal ERA5 PWV data as a reference value. The experimental results show that the corrected RMSE of the corrected MODIS PWV using the proposed model in this paper is 2.037 4 mm, which improves the accuracy by 57.99% compared to the uncorrected MODIS PWV (RMSE=4.850 3 mm); furthermore, neural network differential correction models are constructed for MODIS PWV products under four different confidence levels provided by cloud mask products, the results show that the RMSEs of MODIS PWV products with cloud confidence levels >99%, >95%, >66%, and <66% are increased by 60.03%, 61.21%, 55.72%, and 54.57% compared to uncorrected MODIS PWV. This indicates that the model constructed in this article has high universality in improving the accuracy of MODIS PWV products under various cloud covers, and is expected to provide high-precision water vapor information for climate change and rainfall forecasting research.

Key words: MODIS PWV, GNSS PWV, PWV differential correction, BP neural network, ERA5 PWV

中图分类号:

P228

王梦瑶, 张书毕, 张文渊, 刘洋. 融合多非线性因素的MODIS PWV神经网络差分校正模型[J]. 测绘学报, 2024, 53(11): 2138-2148.

Mengyao WANG, Shubi ZHANG, Wenyuan ZHANG, Yang LIU. MODIS PWV neural network differential correction model integrating multiple nonlinear factors[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(11): 2138-2148.

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对比数据	均值/mm	平均绝对误差/mm	均方根误差/mm	相关系数
探空站PWV	0.212 1	0.892 8	1.274 5	0.988 2
GNSS PWV	0.065 3	1.471 6	2.113 9	0.947 3

模型	均值	平均绝对误差	均方根误差
未校正	-0.013 1	3.610 6	4.474 3
传统线性校正模型	-0.006 6	2.633 5	3.271 3
神经网络差分校正模型	-0.002 4	1.745 5	2.360 2

方案	均值	平均绝对误差	均方根误差
方案Ⅰ	-0.005 6	1.866 2	2.517 2
方案Ⅱ	-0.001 8	1.855 1	2.478 6
方案Ⅲ	-0.007 0	1.858 1	2.494 5
方案Ⅳ	-0.003 0	2.297 9	2.956 4

云类型	模型信息	均值	平均绝对误差	均方根误差
确信晴空（99%）	未校正	-0.60 22	2.824 5	3.690 2
确信晴空（99%）	神经网络差分校正模型	0.053 7	1.067 1	1.475 0
可能晴空（95%）	未校正	-0.971 0	2.847 7	3.874 0
可能晴空（95%）	神经网络差分校正模型	0.091 9	1.197 3	1.685 5
不确定（66%）	未校正	-1.562 3	2.808 9	3.947 4
不确定（66%）	神经网络差分校正模型	0.046 1	1.239 5	1.747 9
云（cloud）	未校正	2.065 0	3.624 2	4.719 3
云（cloud）	神经网络差分校正模型	-0.002 9	1.651 1	2.144 2

融合多非线性因素的MODIS PWV神经网络差分校正模型

MODIS PWV neural network differential correction model integrating multiple nonlinear factors

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模型	均值	平均绝对误差	均方根误差
未校正	-0.681 9	3.748 2	4.850 3
神经网络差分校正模型	-0.007 7	1.566 6	2.037 4

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