The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields

被引:32
作者
Fontanet, Mireia [1 ,2 ,3 ]
Fernandez-Garcia, Daniel [2 ,3 ]
Ferrer, Francesc [1 ]
机构
[1] LabFerrer, Cervera 25200, Spain
[2] Univ Politecn Cataluna, Dept Civil & Environm Engn, ES-08034 Barcelona, Spain
[3] UPC CSIC, Associated Unit, Hydrogeol Grp, Barcelona, Spain
基金
欧盟地平线“2020”;
关键词
HIGH-RESOLUTION; SURFACE-TEMPERATURE; WATER CONTENT; IN-SITU; SMOS; VALIDATION; SCALE; RETRIEVAL; SENSOR; EVAPOTRANSPIRATION;
D O I
10.5194/hess-22-5889-2018
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Soil moisture measurements are needed in a large number of applications such as hydro-climate approaches, watershed water balance management and irrigation scheduling. Nowadays, different kinds of methodologies exist for measuring soil moisture. Direct methods based on gravimetric sampling or time domain reflectometry (TDR) techniques measure soil moisture in a small volume of soil at few particular locations. This typically gives a poor description of the spatial distribution of soil moisture in relatively large agriculture fields. Remote sensing of soil moisture provides widespread coverage and can overcome this problem but suffers from other problems stemming from its low spatial resolution. In this context, the DISaggregation based on Physical And Theoretical scale CHange (DISPATCH) algorithm has been proposed in the literature to downscale soil moisture satellite data from 40 to 1 km resolution by combining the low-resolution Soil Moisture Ocean Salinity (SMOS) satellite soil moisture data with the high-resolution Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) datasets obtained from a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in an agricultural field during two different hydrologic scenarios: wet conditions driven by rainfall events and wet conditions driven by local sprinkler irrigation. Results show that the DISPATCH algorithm provides appropriate soil moisture estimates during general rainfall events but not when sprinkler irrigation generates occasional heterogeneity. In order to explain these differences, we have examined the spatial variability scales of NDVI and LST data, which are the input variables involved in the downscaling process. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average soil moisture at the site, and this could be a reason why the DISPATCH algorithm does not work properly in this field site.
引用
收藏
页码:5889 / 5900
页数:12
相关论文
共 50 条
  • [1] STUDY ON A SINGLE INTERPOLATION FUSION ALGORITHM FOR MULTISOURCE REMOTE SENSING DATA OF SOIL MOISTURE
    Yu, J. S.
    Chen, J. P.
    Li, X. J.
    Liu, Y. M.
    Yao, X. L.
    APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH, 2019, 17 (05): : 11605 - 11617
  • [2] Field-scale evaluation of remote sensing soil moisture retrievals using a multi-satellite approach
    Naseri, Saeed
    Bansouleh, Bahman Farhadi
    Hassanpour, Bahareh
    Azari, Arash
    JOURNAL OF SPATIAL SCIENCE, 2024, 69 (01) : 181 - 202
  • [3] The Indian COSMOS Network (ICON): Validating L-Band Remote Sensing and Modelled Soil Moisture Data Products
    Upadhyaya, Deepti B.
    Evans, Jonathan
    Muddu, Sekhar
    Tomer, Sat Kumar
    Al Bitar, Ahmad
    Yeggina, Subash
    Thiyaku, S.
    Morrison, Ross
    Fry, Matthew
    Tripathi, Sachchida Nand
    Mujumdar, Milind
    Goswami, Mangesh
    Ganeshi, Naresh
    Nema, Manish K.
    Jain, Sharad K.
    Angadi, S. S.
    Yenagi, B. S.
    REMOTE SENSING, 2021, 13 (03)
  • [4] Downscaling of Satellite Remote Sensing Soil Moisture Products Over the Tibetan Plateau Based on the Random Forest Algorithm: Preliminary Results
    Chen, Qingqing
    Miao, Fang
    Wang, Hao
    Xu, Zi-Xin
    Tang, Zhiya
    Yang, Ling
    Qi, Shengxiu
    EARTH AND SPACE SCIENCE, 2020, 7 (06)
  • [5] Inverse Dynamic Parameter Identification for Remote Sensing of Soil Moisture From SMAP Satellite Observations
    Zhang, Runze
    Watts, Adam
    Alipour, Mohamad
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 16592 - 16607
  • [6] Remote sensing of vegetation and soil moisture content in Atlantic humid mountains with Sentinel-1 and 2 satellite sensor data
    Monteiro, Antonio T.
    Arenas-Castro, Salvador
    Punalekar, Suvarna M.
    Cunha, Mario
    Mendes, Ines
    Giamberini, Mariasilvia
    da Costa, Eduarda Marques
    Fava, Francesco
    Lucas, Richard
    ECOLOGICAL INDICATORS, 2024, 163
  • [7] Global soil moisture trend analysis using microwave remote sensing data and an automated polynomial-based algorithm
    Mohseni, Farzane
    Jamali, Sadegh
    Ghorbanian, Arsalan
    Mokhtarzade, Mehdi
    GLOBAL AND PLANETARY CHANGE, 2023, 231
  • [8] An inter-comparison of soil moisture data products from satellite remote sensing and a land surface model
    Fang, Li
    Hain, Christopher R.
    Zhan, Xiwu
    Anderson, Martha C.
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2016, 48 : 37 - 50
  • [9] The Relevance of Soil Moisture by Remote Sensing and Hydrological Modelling
    Zhuo, Lu
    Han, Dawei
    12TH INTERNATIONAL CONFERENCE ON HYDROINFORMATICS (HIC 2016) - SMART WATER FOR THE FUTURE, 2016, 154 : 1368 - 1375
  • [10] Soil Moisture Remote Sensing: State-of-the-Science
    Mohanty, Binayak P.
    Cosh, Michael H.
    Lakshmi, Venkat
    Montzka, Carsten
    VADOSE ZONE JOURNAL, 2017, 16 (01)