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收稿：2019-08-09，
修回：2019-8-31，
录用：2019-9-7，
纸质出版：2020-04-16
移动端阅览
目的
2
时空分辨率较高的土壤湿度数据对于生产实践和科学研究具有重要意义。以国产的风云气象卫星为数据源，利用卷积神经网络自主学习输入变量间深层关联的优势，获取高质量土壤湿度数据，为科学研究和生产实践服务。
方法
2
首先构建了一个土壤湿度提取卷积神经网络（soil moisture convolutional neural network，SMCNN），SMCNN由温度子网络和土壤湿度子网络构成，每个子网络均包含特征提取器和编码器。特征提取器用于为每个像素生成一个特征向量，其中温度子网络的特征提取器由11个卷积层组成，湿度子网络的特征提取器由9个卷积层组成，卷积层均使用1×1的卷积核。编码器用于将提取到的特征拟合为目标变量。两个子网络均使用平均方差作为损失函数。使用随机梯度下降算法对模型进行训练，最后利用训练好的模型提取区域土壤湿度数据。
结果
2
选择宁夏回族自治区为实验区，利用获取的2016-2019年风云3D影像和相应地面站点数据作为实验数据，选择线性回归模型、BP（back propagation）神经网络模型作为对比模型开展数据实验，选择均方根误差作为评价指标。实验结果表明，SMCNN的均方根误差为0.006 7，优于对比模型，SMCNN模型在从风云影像中提取土壤湿度方面具有优势。
结论
2
本文利用卷积神经网络分别构建用于反演地表温度和土壤湿度的子网络，再组成一个完整的土壤湿度反演网络结构，从风云3D数据中获取数值精度、时空分辨率均较高的土壤湿度数据，满足了科学研究和生产实践对大范围高精度土壤湿度数据的需求。
Objective
2
Obtaining soil moisture data with high temporal and spatial resolution is important for agricultural management and scientific research. The goal of this study is to use the Fengyun meteorological satellite as the data source
and utilize the advantage of convolutional neural network (CNN) that can independently learn the deep correlation between input variables to obtain high-quality soil moisture data. Fengyun 3 meteorological satellite is China's second-generation meteorological satellite. The goal of Fengyun 3 is to acquire all-weather
multi-spectral and 3D observations of global atmospheric and geophysical elements
providing satellite observation data to medium-term numerical weather prediction; monitoring ecological environment and large-scale natural disasters; providing satellite meteorological information for global environmental change
global climate change research
and others. The medium-resolution spectral imager Ⅱ (MERSI-Ⅱ) is one of the main loads of Fengyun 3D (FY-3D) and is equipped with 25 channels
including 16 visible-near-infrared channels
3 short-wave infrared channels
and 6 medium-long infrared channels. Among the 25 channels
6 channels with 250 m ground resolution and 19 channels with 1 000 m ground resolution are obtained. The research used FY-3D to obtain high-precision soil moisture data and constructed a soil moisture monitoring technology system that can greatly reduce the dependence on foreign data
operating cost of large-scale monitoring systems
and improve system stability
safety
and monitoring timeliness. Improving the ability of meteorological services and level of domestic satellite applications is important. To obtain high spatial and temporal resolution soil moisture data using Fengyun satellite imagery
this study proposes a method of extracting soil moisture data using convolutional neural network(CNN).
Method
2
CNN is a new machine learning technology that was newly developed in recent years and has attracted research attention because of its powerful autonomous learning ability. This technology has achieved great success in image classification
image segmentation
and other fields. This study constructed a soil moisture convolutional neural network (SMCNN) to achieve the goal of obtaining large-scale high-precision soil moisture monitoring using FY-3D remote sensing image. The SMCNN model includes seven parts
namely
input
temperature subnetwork
normalized difference vegetation index (NDVI) extraction module
enhanced vegetation index (EVI) extraction module
surface albedo extraction module
soil moisture subnetwork
and output. The temperature and soil moisture subnetworks contain a feature extractor and an encoder. The feature extractor is used to generate a feature vector for each pixel
where the feature extractor of the temperature subnetwork has 11 convolutional layers
and the feature extractor of the humidity subnetwork consists of 9 convolutional layers
and the convolutional layer uses a 1×1 type convolution kernel. The encoder is used to fit the extracted features to the target variable. Both subnetworks use the average variance as a loss function. In the model training stage
the preprocessed FY-3D image and corresponding observation point data are used as inputs
and in the model test phase
only the preprocessed FY-3D image is used as the input. The temperature subnetwork is used to obtain the ground temperature from the FY-3D image
the NDVI extraction module is used to extract the NDVI from the FY-3D image
the EVI extraction module is used to extract the EVI from the FY-3D image
and the surface albedo extraction module is used to obtain surface albedo. The extraction results of the aforementioned four parts are used as input to the soil moisture subnetwork. The soil moisture subnetwork uses the extracted ground temperature
NDVI
EVI
and ground albedo to retrieve soil moisture. The output of the model is the pixel-by-pixel soil moisture value. The model is trained using a stochastic gradient descent algorithm
and finally the trained model is used to extract regional soil moisture data.
Result
2
Ningxia was selected as the experimental area. The FY-3D used in this study is all from the satellite ground receiving station of Ningxia Meteorological Bureau
including 161 images in 2018 and 92 images in 2019
with a total of 253. After the images were stitched together
a total of 92 images covering the entire territory of Ningxia were formed. The ground observation data used in this study came from the automatic weather station deployed by the Ningxia Meteorological Bureau. The time range was from January 1
2016 to June 30
2019. A total of 36 ground temperature stations and 37 soil moisture stations were observed. To verify the validity and rationality of this proposed method
we selected the linear regression and back propagation(BP) neural network models as the contrast models to conduct the data experiment. The mean square error was selected as the evaluation index. The comparison experimental results show that the RMSE of the SMCNN model is 0.006 7
which is higher than the comparison model. The experimental results show that the SMCNN model has advantages in extracting soil moisture from wind cloud images.
Conclusion
2
The SMCNN model proposed in this paper fully utilizes deep learning technology to learn independently and improves the accuracy of obtaining soil moisture. The main contributions of this study are as follows:1) Based on the analysis of the characteristics of FY-3D data
a step-by-step inversion strategy is established for the inversion of soil moisture requirements
and each step inversion uses a more relevant variable. The proposed strategy is an important reference for inverting other variables. 2) CNNs are used to construct network structures for inversion of surface temperature and soil moisture
and organized into a complete soil moisture inversion network structure. This structure enables direct access to soil moisture data from FY-3D data. 3) The feature value extracted by the 1×1 type convolution kernel used can be regarded as a spectral index and has a physical meaning. The main disadvantage of this study is that in the late stage of crop growth
the effect of vegetation index becomes invalid due to the saturation problem
which influences the inversion effect. This study aims to find other suitable supplementary parameters to introduce into the model to solve the effect of vegetation index saturation.
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