地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (4): 749-761.doi: 10.12082/dqxxkx.2021.200149
收稿日期:
2020-03-27
修回日期:
2020-06-21
出版日期:
2021-04-25
发布日期:
2021-06-25
通讯作者:
凌子燕
作者简介:
邓雅文(1999— ),女,湖南衡阳人,硕士生,研究方向为生态水文遥感。E-mail: 202021051199@mail.bnu.edu.cn
基金资助:
DENG Yawen1(), LING Ziyan2,*(
), SUN Na1, LV Jinxia1,3
Received:
2020-03-27
Revised:
2020-06-21
Online:
2021-04-25
Published:
2021-06-25
Contact:
LING Ziyan
Supported by:
摘要:
土壤湿度是地表水热交换过程和水文循环中的一个关键组成部分,获取高时空分辨率的土壤湿度数据一直是当前研究的热点。SMAP(Soil Moisture Passive and Active)主被动微波土壤湿度产品的精度高,但存在着空间分辨率低和时间分辨率缺失的问题,这限制了其在区域尺度上的应用,为解决这一问题得到更高时空分辨率的土壤湿度产品,本文利用广义回归神经网络模型(GRNN)模拟了MODIS地表温度、反射率、植被指数光学/热红外遥感数据以及高程、坡度、坡向、经纬度数据与SMAP土壤湿度的关系,从而将京津冀地区SMAP L2土壤湿度产品的时间分辨率由不连续(4~20 d)提升至1 d,空间分辨率由3 km提升至1 km,并扩展其在京津冀地区的空间覆盖范围。研究发现:① GRNN模型总体验证结果表明土壤湿度估算值与SMAP原始值的相关性较高(r=0.7392),均方根误差(RMSE)为0.0757 cm3/cm3;② 不同季节典型日期的GRNN模型估算结果精度相差较大,春季处的相关性相比其他季节最低,精度相对较高(r春=0.6152,RMSE春=0.0653cm3/cm3),秋季和夏季土壤湿度估算精度较为接近(r夏=0.6957,r秋=0.7053,RMSE夏=0.0754cm3/cm3,RMSE秋=0.0694cm3/cm3),冬季的估算精度最高(r冬=0.8214,RMSE冬=0.0367cm3/cm3);③ 2016年京津冀夏秋季节的土壤湿度较其他季节要显著提高,空间分布上坝上高原区域较低,而沿海地区的土壤湿度明显较高。本研究对京津冀地区的生态水文、气候预测以及干旱监测等应用领域具有重要价值。
邓雅文, 凌子燕, 孙娜, 吕金霞. 基于广义回归神经网络的京津冀地区土壤湿度遥感逐日估算研究[J]. 地球信息科学学报, 2021, 23(4): 749-761.DOI:10.12082/dqxxkx.2021.200149
DENG Yawen, LING Ziyan, SUN Na, LV Jinxia. Daily Estimation of Soil Moisture over Beijing-Tianjin-Hebei Region based on General Regression Neural Network Model[J]. Journal of Geo-information Science, 2021, 23(4): 749-761.DOI:10.12082/dqxxkx.2021.200149
表2
对模型输入变量进行主成分分析得到的特征根与方差贡献率"
主成分 | 特征值 | 方差贡献率 | 累计贡献率 |
---|---|---|---|
1 | 5.8544 | 0.488 | 0.488 |
2 | 1.8477 | 0.154 | 0.642 |
3 | 1.1322 | 0.094 | 0.736 |
4 | 0.9806 | 0.082 | 0.818 |
5 | 0.7982 | 0.067 | 0.884 |
6 | 0.5754 | 0.048 | 0.932 |
7 | 0.4060 | 0.034 | 0.966 |
8 | 0.2731 | 0.023 | 0.989 |
9 | 0.0982 | 0.008 | 0.997 |
10 | 0.0257 | 0.002 | 0.999 |
11 | 0.0079 | 0.001 | 1.000 |
12 | 0.0006 | 0.000 | 1.000 |
表3
GRNN模型输入层变量降维前后的表现对比"
r | RMSE/(cm³/cm³) | bias | ubRMSE | 样本量/个 | 训练时间/s | ||
---|---|---|---|---|---|---|---|
GRNN降维前扩散因子为0.09 | 训练精度 | 0.7566 | 0.0736 | -0.0011 | 0.0736 | 48000 | |
验证精度 | 0.7322 | 0.0766 | -0.0005 | 0.0765 | 12000 | 1007.75 | |
总体精度 | 0.7392 | 0.0757 | -0.0007 | 0.0757 | 60000 | ||
GRNNPCA降维后扩散因子为0.04 | 训练精度 | 0.7032 | 0.0777 | -0.0002 | 0.0777 | 48000 | |
验证精度 | 0.7140 | 0.0787 | 0.0012 | 0.0787 | 12000 | 768.02 | |
总体精度 | 0.7110 | 0.0784 | 0.0008 | 0.0784 | 60000 |
表4
不同季节GRNN 1 km土壤湿度的总体评价"
时间 | 指标 | 训练精度 | 验证精度 | 总体精度 | 时间 | 指标 | 训练精度 | 验证精度 | 总体精度 |
---|---|---|---|---|---|---|---|---|---|
春季 (2016-04-07) | r | 0.6300** | 0.5886** | 0.6152** | 秋季 (2016-09-28) | r | 0.7467** | 0.6900** | 0.7053** |
RMSE/(cm³/cm³) | 0.0636 | 0.0669 | 0.0653 | RMSE/(cm³/cm³) | 0.0658 | 0.0708 | 0.0694 | ||
bias | 0.0009 | 0.0003 | 0.0005 | bias | 0.0004 | 0.0008 | 0.0007 | ||
ubRMSE | 0.0636 | 0.0659 | 0.0653 | ubRMSE | 0.0658 | 0.0708 | 0.0694 | ||
样本量/个 | 32 000 | 8000 | 40 000 | 样本量/个 | 32 000 | 8000 | 40 000 | ||
夏季 (2016-07-05) | r | 0.7277** | 0.6843** | 0.6957** | 冬季 (2016-12-14) | r | 0.8064** | 0.8308** | 0.8214** |
RMSE/(cm³/cm³) | 0.0715 | 0.0769 | 0.0754 | RMSE/(cm³/cm³) | 0.0348 | 0.0374 | 0.0367 | ||
bias | 0.0018 | 0.0013 | 0.0014 | bias | -0.0019 | -0.0016 | -0.0017 | ||
ubRMSE | 0.0715 | 0.0769 | 0.0754 | ubRMSE | 0.0348 | 0.0374 | 0.0367 | ||
样本量/个 | 32 000 | 8000 | 40 000 | 样本量/个 | 16 000 | 4000 | 20 000 |
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