浏览全部资源
扫码关注微信
Received：09 August 2019，
Revised：2019-8-31，
Accepted：07 September 2019，
Published：16 April 2020
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.
Badrinarayanan V, Kendall A and Cipolla R. 2017. SegNet:a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12):2481-2495[DOI:10.1109/TPAMI.2016.2644615]
Chen L C, Papandreou G, Kokkinos I, Murphy K and Yuille A L. 2018. DeepLab:semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4):834-848[DOI:10.1109/TPAMI.2017.2699184]
Chen S B, Chen J Y, Zhang Z T, Bian J, Wang Y F and Shi S L. 2018. Retrieving soil water content of winter wheat during heading period by multi-spectral remote sensing of Unmanned Aerial Vehicle (UAV). Water Saving Irrigation, (5):39-43
陈硕博, 陈俊英, 张智韬, 边江, 王禹枫, 石树兰. 2018.无人机多光谱遥感反演抽穗期冬小麦土壤含水率研究.节水灌溉, (5):39-43 [DOI:10.3969/j.issn.1007-4929.2018.05.009]
Chen W Y, Xiao Q G and Sheng Y W. 1994. Application of the anomaly vegetation index to monitoring heavy drought in 1992. Remote Sensing of Environment China, 9(2):106-112
陈维英, 肖乾广, 盛永伟. 1994.距平植被指数在1992年特大干旱监测中的应用.环境遥感, 9(2):106-112
Dobson M C and Ulaby F T. 1986. Active microwave soil moisture research. IEEE Transactions on Geoscience and Remote Sensing, GE-24(1):23-36[DOI:10.1109/TGRS.1986.289585]
England A W, Galantowicz J F and Schretter M S. 1992. The radiobrightness thermal inertia measure of soil moisture. IEEE Transactions on Geoscience and Remote Sensing, 30(1):132-139[DOI:10.1109/36.124223]
Fung A K, Li Z and Chen K S. 1992. Backscattering from a randomly rough dielectric surface. IEEE Transactions on Geoscience and Remote Sensing, 30(2):356-369[DOI:10.1109/36.134085]
Ghulam A, Qin Q M and Zhan Z M. 2007. Designing of the perpendicular drought index. Environmental Geology, 52(6):1045-1052[DOI:10.1007/s00254-006-0544-2]
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE: 770-778[ DOI:10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]
Idso S B, Jackson R D, Pinter P J Jr, Reginato R J and Hatfield J L. 1981. Normalizing the stress-degree-day parameter for environmental variability. Agricultural Meteorology, 24:45-55[DOI:10.1016/0002-1571(81)90032-7]
Kogan F N. 1990. Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11(8):1405-1419[DOI:10.1080/01431169008955102]
Krizhevsky A, Sutskever I and Hinton G E. 2017. ImageNet classification with deep convolutional neural networks.Communications of the ACM, 60(6):84-90[DOI:10.1145/3065386]
Li X M, Liu A L, Zhang S Y and Wang Z. 2005. Use of thermal inertia approach in the monitoring of drought by remote wensing. Agricultural Research in the Arid Areas, 23(1):54-59
李星敏, 刘安麟, 张树誉, 王钊. 2005.热惯量法在干旱遥感监测中的应用研究.干旱地区农业研究, 23(1):54-59 [DOI:10.3321/j.issn:1000-7601.2005.01.010]
Lin G S, Milan A, Shen C H and Reid I. 2016. RefineNet: multi-path refinement networks for high-resolution semantic segmentation.[EB/OL].[2019-07-12] . https://arxiv.org/pdf/1611.06612.pdf https://arxiv.org/pdf/1611.06612.pdf
Liu H, Liu R G and Liu S Y. 2012. Review of drought monitoring by remote sensing. Journal of Geo-Information Science, 14(2):232-239
刘欢, 刘荣高, 刘世阳. 2012.干旱遥感监测方法及其应用发展.地球信息科学学报, 14(2):232-239 [DOI:10.3724/SP.J.1047.2012.00232]
Liu P J, Zhang L, Kurban A, Chang P, Li L X, Pularti and Zhao B K. 1997. A method for monitoring soil water contents using satellite remote sensing. Journal of Remote Sensing, 1(2):135-138
刘培君, 张琳, 艾里西尔·库尔班, 常萍, 李良序, 镨拉提, 赵兵科. 1997.卫星遥感估测土壤水分的一种方法.遥感学报, 1(2):135-138 [DOI:10.11834/jrs.19970210]
Mao K B, Tang H J, Zhou Q B and Chen Y Q. 2007. A survey of soil moisture retrieval by passive microwave remote sensing. Remote Sensing Technology and Application, 22(3):466-470
毛克彪, 唐华俊, 周清波, 陈佑启. 2007.被动微波遥感土壤水分反演研究综述.遥感技术与应用, 22(3):466-470 [DOI:10.3969/j.issn.1004-0323.2007.03.028]
Njoku E G and Li L. 1999. Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz. IEEE Transactions on Geoscience and Remote Sensing, 37(1):79-93[DOI:10.1109/36.739125]
Price J C. 1977. Thermal inertia mapping:a new view of the Earth. Journal of Geophysical Research, 82(18):2582-2590[DOI:10.1029/jc082i018p02582]
Price J C. 1985. On the analysis of thermal infrared imagery:the limited utility of apparent thermal inertia. Remote Sensing of Environment, 18(1):59-73[DOI:10.1016/0034-4257(85)90038-0]
Qi S H, Zhang Y P, Niu Z, Wang C Y and Zheng L. 2005. Application of water deficit index in drought monitoring in China with remote sensing. Acta Pedologica Sinica, 42(3):367-372
齐述华, 张源沛, 牛铮, 王长耀, 郑林. 2005.水分亏缺指数在全国干旱遥感监测中的应用研究.土壤学报, 42(3):367-372 [DOI:10.11766/trxb200406070303]
Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer: 234-241[ DOI:10.1007/978-3-319-24574-4_28 http://dx.doi.org/10.1007/978-3-319-24574-4_28 ]
Sandholt I, Rasmussen K and Andersen J. 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79(2/3):213-224[DOI:10.1016/s0034-4257(01)00274-7]
Shelhamer E, Long J and Darrell T. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4):640-651[DOI:10.1109/TPAMI.2016.2572683]
Shi Y, Gong H R, Zhang X and Li C. 2015. Soil moisture retrieval with temperature-vegetation dryness index of FY-3/MERSI data. Science of Surveying and Mapping, 40(11):63-66, 84
石玉, 宫恒瑞, 张旭, 李聪. 2015.风云三号温度植被指数反演土壤湿度研究.测绘科学, 40(11):63-66, 84 [DOI:10.16251/j.cnki.1009-2307.2015.11.013]
Simonyan K and Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition[EB/OL].[2019-07-12] . https://arxiv.org/pdf/1409.1556.pdf https://arxiv.org/pdf/1409.1556.pdf
Sun F J, Ju W M, Fang M H and Fan W L. 2018. Retrieval of forest above ground biomass using 4-scale geometrical optical model and remote sensing data. Remote Sensing Technology and Application, 33(6):1046-1055
孙凤娟, 居为民, 方美红, 范渭亮. 2018.基于四尺度几何光学模型的森林地上生物量遥感估算.遥感技术与应用, 33(6):1046-1055 [DOI:10.11873/j.issn.1004-0323.2018.6.1046]
Sun J Y, Li M Z, Tang N and Zheng L H. 2007. Spectral characteristics and their correlation with soil parameters of black soil in Northeast China. Spectroscopy and Spectral Analysis, 27(8):1502-1505
孙建英, 李民赞, 唐宁, 郑立华. 2007.东北黑土的光谱特性及其与土壤参数的相关性分析.光谱学与光谱分析, 27(8):1502-1505 [DOI:1000-0593(2007)08-1502-04]
Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A. 2015. Going deeper with convolutions[EB/OL].[2019-07-12] . https://arxiv./pdf/1409.4842.pdf https://arxiv./pdf/1409.4842.pdf
Ulaby F T, McDonald K, Sarabandi K and Dobson M C. 1988. Michigan microwave canopy scattering models (MIMICS)//Proceedings of the Moving Toward the 21st Century International Geoscience and Remote Sensing Symposium. Edinburgh: IEEE: 1009-1009[ DOI:10.1109/IGARSS.1988.570506 http://dx.doi.org/10.1109/IGARSS.1988.570506 ]
Wang J X, Pan Y Z, Zhu X F and Sun Z L. 2019. A review of researches on inversion of eigenvariance of soil water. Acta Pedologica Sinica, 56(1):23-35
王俊霞, 潘耀忠, 朱秀芳, 孙章丽. 2019.土壤水分反演特征变量研究综述.土壤学报, 56(1):23-35 [DOI:10.11766/trxb201803090579]
Wang Z X, Liu C and Huete A. 2003. From AVHRR-NDVI to MODIS-EVI:Advances in vegetation index research. Ac-ta Ecolog ica Sinica, 23(5):979-987
王正兴, 刘闯, Alfredo H. 2003.植被指数研究进展:从AVHRR-NDVI到MODIS-EVI.生态学报, 23(5):979-987 [DOI:10.3321/j.issn:1000-0933.2003.05.020]
Wei S P, Wang L and Wu F Q. 2008. Research and application of SVAT models. Science of Soil and Water Conservation, 6(2):113-120
卫三平, 王力, 吴发启. 2008. SVAT模型的研究与应用.中国水土保持科学, 6(2):113-120 [DOI:10.16843/j.sswc.2008.02.020]
Wigneron J P, Calvet J C, Pellarin T, van de Griend A A, Berger M and Ferrazzoli P. 2003. Retrieving near-surface soil moisture from microwave radiometric observations:current status and future plans. Remote Sensing of Environment, 85(4):489-506[DOI:10.1016/s0034-4257(03)00051-8]
Wigneron J P, Waldteufel P, Chanzy A, Calvet J C and Kerr Y. 2000. Two-dimensional microwave interferometer retrieval capabilities over land surfaces (SMOS Mission). Remote Sensing of Environment, 73(3):270-282[DOI:10.1016/s0034-4257(00)00103-6]
Wu J, Zhang H and Liu P F. 2017. Inversion algorithms of medium high-resolution surface albedo. Journal of Tianjin Normal University(Natural Science Edition, 37(2):45-49
毋杰, 张虎, 刘朋飞. 2017.中高分辨率地表反照率反演算法.天津师范大学学报(自然科学版), 37(2):45-49 [DOI:10.3969/j.issn.1671-1114.2017.02.010]
Xu L N, Niu R Q and Shang X Z. 2011. Estimating soil moisture in Three Gorges area based on temperature vegetation dryness index. Computer Engineering and Applications, 47(25):235-238, 244
许丽娜, 牛瑞卿, 尚秀枝. 2011.利用温度植被干旱指数反演三峡库区土壤水分.计算机工程与应用, 47(25):235-238, 244 [DOI:10.3778/j.issn.1002-8331.2011.25.062]
Yan H B and Zhou G Q. 2017. Surface soil moisture retrieval from optical remote sensing:current status and perspectives. Journal of Subtropical Resources and Environment, 12(2):82-89, 95
晏红波, 周国清. 2017.地表土壤湿度光学遥感反演方法研究进展.亚热带资源与环境学报, 12(2):82-89, 95 [DOI:10.19687/j.cnki.1673-7105.2017.02.011]
Yang T, Gong H L, Li X J, Zhao W J and Meng D. 2010. Progress of soil moisture monitoring by remote sensing. Acta Ecologica Sinica, 30(22):6264-6277
杨涛, 宫辉力, 李小娟, 赵文吉, 孟丹. 2010.土壤水分遥感监测研究进展.生态学报, 30(22):6264-6277
Yu F and Zhao Y S. 2011. A new semi-empirical model for soil moisture content retrieval by ASAR and TM data in vegetation-covered areas. Science China Earth Sciences, 41(4):532-540
余凡, 赵英时. 2011. ASAR和TM数据协同反演植被覆盖地表土壤水分的新方法.中国科学:地球科学, 41(4):532-540 [DOI:10.1007/s11430-011-4204-3]
Yu F, Zhao Y S and Li H T. 2012. Soil moisture retrieval based on GA-BP neural networks algorithm. Journal of Infrared and Millimeter Waves, 31(3):283-288
余凡, 赵英时, 李海涛. 2012.基于遗传BP神经网络的主被动遥感协同反演土壤水分.红外与毫米波学报, 31(3):283-288 [DOI:10.3724/SP.J.1010.2012.00283]
Zhang J, Xie S Y, Liu J, Chen C and Zhao L. 2018. Research on the temperature compensation model of the FDR soil moisture sensor. Journal of Agricultural Mechanization Research, 40(4):177-182, 189
张健, 谢守勇, 刘军, 陈翀, 赵镭. 2018. FDR土壤湿度传感器的温度补偿模型研究.农机化研究, 40(4):177-182, 189 [DOI:10.3969/j.issn.1003-188X.2018.04.034]
Zhang L and Sun H. 2009. A research on the relationships between soil moisture and reflectance spectrum in Loess Plateau Area. Chinese Journal of Soil Science, 40(6):1255-1258
张莉, 孙虎. 2009.黄土高原地区土壤含水量与反射光谱关系研究.土壤通报, 40(6):1255-1258 [DOI:10.19336/j.cnki.trtb.2009.06.005]
Zhang Y L, Yang P, Yang Y X, Zeng W G, Huang Z B, Li Q and Zheng Y C. 2017. Design and research on the soil temperature and humidity data acquisition system of citrus' root zone. Techniques of Automation and Applications, 36(4):17-20
张永磊, 杨鹏, 杨彦鑫, 曾文果, 黄兆波, 李琦, 郑永春. 2017.大田柑橘根区土壤温湿度数据采集系统设计与研究.自动化技术与应用, 36(4):17-20 [DOI:10.3969/j.issn.1003-7241.2017.04.004]
相关文章
相关作者
相关机构
京公网安备11010802024621
