Design of Soil Moisture Network based on Temporal and Spatial Variability

doi:10.11873/j.issn.1004-0323.2022.6.1437

Remote Sensing Technology and Application

2022, Vol. 37

Issue (6): 1437-1446 DOI: 10.11873/j.issn.1004-0323.2022.6.1437

Design of Soil Moisture Network based on Temporal and Spatial Variability

Xueqin Wang¹(

),Xiang Zhang^1,²(

),Nengcheng Chen^1,²,Hongliang Ma¹

^1.State Key Laboratory of Information Engineering in Surveying，Mapping and Remote Sensing，Wuhan University，Wuhan 430079，China
^2.National Engineering Research Center of Geographic Information System，China University of Geosciences （Wuhan），Wuhan 430074，China

Download:

HTML

PDF (5218KB)
Export: BibTeX | EndNote (RIS)

Abstract

Reasonable and effective soil moisture observation network can better monitor regional soil moisture based on in-situ data and provide high-precision soil moisture information. Based on the study of spatial and temporal variability of soil moisture in the region from year 2010 to 2019， and superimposed with different types of auxiliary data， the study area was divided twice， and an optimal layout method of soil moisture observation network was designed. On the basis of the existing 24 stations， 79 new stations were added to the designed observation network， which reduced the monitoring area of the existing single point to 381—792 km²， and the monitoring efficiency increased by 71.57%. This method follows the idea of "partition before laying out"， first utilizing the relative continuous satellite remote sensing data to acquire regional soil moisture geography law， and then deduce the layout plan of the ground station network， which can provide a new reference for the optimization of the layout of the related station network.

Key words: TVDI Soil moisture Spatio-temporal variability Zoning optimization Design of observation network

Received: 20 September 2021 Published: 15 February 2023

ZTFLH:

TP79

Corresponding Authors: Xiang Zhang E-mail: wangxueqin@whu.edu.cn;zhangxiang76@cug.edu.cn

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Xueqin Wang
	Xiang Zhang
	Nengcheng Chen
	Hongliang Ma

Cite this article:

Xueqin Wang,Xiang Zhang,Nengcheng Chen,Hongliang Ma. Design of Soil Moisture Network based on Temporal and Spatial Variability. Remote Sensing Technology and Application, 2022, 37(6): 1437-1446.

URL:

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.6.1437 OR http://www.rsta.ac.cn/EN/Y2022/V37/I6/1437

Fig.1 Location of the study area and spatial distribution of the existing stations

Fig.2 Technique flow chart

Fig.3 TVDI calculation and mapping

Fig.4 tRMSE distribution of soil moisture in the study area from 2010 to 2019

Fig.5 sRMSE distribution of soil moisture in the study area from 2010 to 2019

Fig.6 Spatial distribution map of

Fig.7 Clustering results of study area

Fig.8 Layout of regional soil moisture network

Table1 Statistics on the layout of stations in each city

1	Liu Qinhuo， Xin Jingfeng， Xin Xiaozhou， et al. Remote sensing monitoring of agricultural drought based on land surface temperature and vegetation index［J］. Science & Technology Review， 2007， 25（6）： 12-18.
1	柳钦火，辛景峰，辛晓洲，等. 基于地表温度和植被指数的农业干旱遥感监测方法［J］. 科技导报， 2007， 25（6）：12-18.
2	Chen Shaodan， Zhang Liping， Shan Lijie， et al. Remote sensing retrieval of soil moisture in the middle and lower reaches of the Yangtze River and analysis of its influencing factors［J］. Journal of Applied Basic and Engineering Science， 2017， 25（4）： 657-669.
2	陈少丹，张利平，闪丽洁，等. 长江中下游流域土壤湿度遥感反演研究及其影响因素分析［J］. 应用基础与工程科学学报， 2017， 25（4）：657-669.
3	Huang Yan， Zheng Wei. Design of forest ecosystem observation experiment platform based on wireless sensor networks［J］. Remote Sensing Technology and Application， 2021， 36（3）： 502-510.
3	黄艳，郑玮. 基于无线传感器网络的森林生态系统观测试验平台构建［J］. 遥感技术与应用， 2021， 36（3）： 502-510.
4	Zeng J Y， Li Z， Chen Q， et al. Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations［J］. Remote Sensing of Environment，2015，163. DOI： . doi: 10.1016/ j.rse.2015. 03.008
5	Ma H L， Zeng J Y， Chen N C， et al. Satellite surface soil moisture from SMAP，SMOS，AMSR2 and ESA CCI：A comprehensive assessment using global ground-based observations ［J］. Remote Sensing of Environment， 2019， 231. DOI： . doi: 10.1016/ j.rse.2019.111215
6	Fan Yue， Qiu Jianxiu， Dong Jianzhi， et al. Error characteristics of microwave soil moisture products based on Triple Collocation and its spatial-temporal pattern［J］. Remote Sensing Technology and Application， 2020， 35（1）： 85-96.
6	范悦，邱建秀，董建志，等. 基于Triple Collocation方法的微波土壤水分产品不确定性分析与时空变化规律研究［J］. 遥感技术与应用， 2020， 35（1）： 85-96.
7	Ma H L， Zeng J Y， Zhang X， et al. Evaluation of six satellite- and model-based surface soil temperature datasets using global ground-based observations［J］. Remote Sensing of Environment，2021，264. DOI： . doi: 10.1016/J.RSE. 2021.112605
8	Xu L， Chen N C， Chen Z Q， et al. Spatiotemporal forecasting in earth system science： Methods， uncertainties， predictability and future directions［J］. Earth-Science Reviews， 2021： 103828. DOI: . doi: 10.1016/j.earscirev.2021.103828
9	Dorigo W， Himmelbauer I， Aberer D， et al. The International soil moisture network： Serving earth system science for over a decade［J］. Hydrology and Earth System Sciences Discussions， 2021： 1-83. DOI: . doi: 10.5194/hess-25-5749-2021
10	Zhang X， Chen N C， Li J Z， et al. Multi-sensor integrated framework and index for agricultural drought monitoring［J］. Remote Sensing of Environment， 2017， 188： 141-163.
11	Chen D， Chen N C， Zhang X， et al. Next-Generation soil moisture sensor web： High density in-situ observation over NB-IoT［J］. IEEE Internet of Things Journal， 2021. DOI： . doi: 10.1109/JIOT.2021. 3065077
12	Robinson D A， Binley A， Crook N， et al. Advancing process-based watershed hydrological research using near-surface geophysics： A vision for， and review of， electrical and magnetic geophysical methods［J］. Hydrological Process， 2008， 22（18）： 3604–3635.
13	Zhang X， Chen N C， Chen Z Q， et al. Geospatial sensor web： A cyber-physical infrastructure for geoscience research and application［J］. Earth-Science Reviews，2018，185：684-703.
14	Kang G， Li X， Jin R， et al. Hybrid optimal design of the eco-hydrological wireless sensor network in the middle reach of the Heihe River Basin， China［J］. Sensors， 2014， 14（10）：19095-19114.
15	Wang K， Chen N C， Tong D Q， et al. Optimizing the configuration of streamflow stations based on coverage maximization： A case study of the Jinsha River Basin［J］. Journal of Hydrology， 2015， 527： 172-183.
16	Wang G， Jin Heuvelink， Wang L. Sampling design optimization of a wireless sensor network for monitoring ecohydrological processes in the Babao River Basin， China［J］. International Journal of Geographical Information Science， 2015， 29（1）： 92-110.
17	Xu Xinliang. Spatial distribution data set of China Monthly Vegetation Index (NDVI)[DB/OL]. 徐新良. 中国月度植被指数（NDVI）空间分布数据集［DB/OL］. 中国科学院资源环境科学数据中心数据注册与出版系统（http：∥www.resdc.cn/DOI），2018.DOI： . doi: 10.12078/201806 0602
18	Sandholt T I， Rasmussen K， Andeesen J. A simple interpretation of the surface temperature/ vegetation index space for assessment of surface moisture status［J］. Remote Sensing of Environment， 2002， 79（2）： 213-224．
19	Wang C Y， Qi S H， Niu Z， et al. Evaluating soil moisture status in China using the Temperature–Vegetation Dryness Index （TVDI）［J］. Canadian Journal of Remote Sensing， 2014， 30（5）：671-679.
20	Li Chao， Li Xuemei， Tian Yalin， et al. Time and space fusion model comparison of temperature vegetation drought index［J］. Remote Sensing Technology and Application， 2020， 35（4）： 832-844.
20	李超，李雪梅，田亚林，等. 温度植被干旱指数时空融合模型对比［J］. 遥感技术与应用， 2020， 35（4）： 832-844.
21	Lawrence H， Wigneron J P， Demontoux F， et al. Evaluating the semi-empirical H～Q model， used to calculate the emissivity of a rough bare soil， with a numerical modeling approach［J］. IEEE Transactions on Geoscience and Remote Sensing， 2013， 51（7）： 4075-4084．
22	Ran Qiong， Zhang Zengxiang， Zhang Guoping， et al. Correction of DEM for soil moisture retrieval by temperature vegetation drought index［J］. Science of Soil and Water Conservation， 2005， 3（2）： 32-36.
22	冉琼，张增祥，张国平，等．温度植被干旱指数反演全国土壤湿度的 DEM 订正［J］．中国水土保持科学，2005，3（2）：32-36.
23	Vachaud G， Desilans A P， Balabanis P， et al. Temporal stability of spatially measured soil-water probability density-function［J］. Soil Science Society of America Journal， 1985， 49（4）： 822-846.
24	Li Boxiang， Chen Xiaoyong， Xu Wenting， Spatio-temporal variation of soil moisture based on SMOS downscaling data［J］. China Rural Water and Hydropower， 2019（8）：5-11.
24	李伯祥，陈晓勇，徐雯婷. 基于SMOS降尺度数据的土壤水分时空变化分析研究［J］. 中国农村水利水电，2019（8）：5-11.
25	Vachaud G， Desilans A P， Balabanis P，et al.Temporal stability of spatially measured soil-water probability density-function［J］. Soil Science Social of America Journal，1985，49（4）：822-828.
26	Jacobs J M， Mohanty B P， Hsu E C，et al. SMEX02： field scale variability， time stability and similarity of soil moisture［J］. Remote Sensing of Environment，2004，92（4）：436-446.
27	Sha Sha， Guo Ni， Li Yaohui， et al. Application status and problems of temperature vegetation drought index TVDI in China［J］. Arid Meteorology， 2014， 32（1）：128-134.
27	沙莎，郭铌，李耀辉，等.我国温度植被旱情指数TVDI的应用现状及问题简述［J］. 干旱气象， 2014， 32（1）：128-134.
28	Specifications for soil moisture monitoring [S]. Ministry of Water Resources of the People's Republic of China.
28	土壤墒情监测规范［S］.中华人民共和国水利部，SL 364-2015．

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Shared

Discussed