Remote Sensing Technology and Application, Volume. 39, Issue 5, 1237(2024)

Soil Moisture Estimation and Drought Monitoring in Grape Growing Areas based on Time Series Data of MODIS

Yi GAO... Yibo WANG, Xia ZHANG and Shiyu TAO |Show fewer author(s)
Author Affiliations
  • Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100101, China
  • show less
    Figures & Tables(13)
    Location of Jingyang in Shanxi Province and the distribution of concentrated grape planting areas and sample points
    Flowchart of the research
    Remote sensing based drought indices and SM temporal changes of grape in Jingyang
    Spatial distribution of Remote sensing based drought indices and SM in major grape growing areas of Jingyang County from March to October in 2016
    Correlation matrix between remote sensing drought index and SM at different growth stages from 2009 to 2016
    Scatterplots of measured versus estimated SM at mid-ripening stage (July)
    Accuracy of multi-temporal comprehensive estimation model for SM
    Drought distribution map of grape growing area in northern Jingyang in 2011 based on Model-RSM and SPEI
    Distribution map of drought frequency in northern Jingyang during key growth periods for grapes from 2009 to 2016
    • Table 1. Remote sensing drought index indices

      View table
      View in Article

      Table 1. Remote sensing drought index indices

      遥感干旱指标计算公式

      MODIS

      产品名称

      来源和参考文献
      NDWINDWI=ρ2-ρ7ρ2+ρ 7MOD09A1Chen(2005) [21]
      TVDITVDI=Ts-TsminTsmax-Tsmin

      MOD09A1

      MOD11A2

      Sandholt(2002)[22]
      CWSICWSI=1-ETPETMOD16A2Jackson(1981)[23]
      VSWIVSWI=NDVILST×100

      MOD09A1

      MOD11A2

      McVicar(2001)[24]
      VCIVCI=NDVIi-NDVIminNDVImax-NDVIminMOD09A1Kogan(1995)[25]
      TCITCI=LSTmax-LSTiLSTmax-LSTminMOD11A2Kogan(1995)[25]
    • Table 2. Similarity of temporal changes between remote sensing drought monitoring indicators and SM

      View table
      View in Article

      Table 2. Similarity of temporal changes between remote sensing drought monitoring indicators and SM

      遥感干旱指标Person相关系数余弦相似性欧氏距离
      CWSI0.257 0*0.786 87.151 1
      NDWI0.654 9**0.937 22.801 9
      TVDI-0.009 80.916 09.690 3
      VSWI-0.081 80.916 49.735 5
      VCI0.099 50.797 57.116 8
      TCI0.275 0**0.824 94.989 6
    • Table 3. Correlation between VSWI, CWSI, NDWI and SM at different time lags

      View table
      View in Article

      Table 3. Correlation between VSWI, CWSI, NDWI and SM at different time lags

      遥感干旱指标滞后时相2009年2010年2011年2012年2013年2014年2015年2016年
      VSWI0-0.200.450.210.530.570.290.090.28
      1-0.100.580.360.590.540.470.300.35
      2-0.190.510.230.340.500.440.460.10
      3-0.320.47-0.050.140.400.190.20-0.01
      CWSI0-0.29-0.09-0.47-0.18-0.01-0.42-0.34-0.26
      1-0.12-0.06-0.44-0.070.02-0.23-0.16-0.19
      2-0.24-0.05-0.28-0.080.07-0.13-0.01-0.13
      3-0.10-0.06-0.35-0.080.08-0.120.13-0.14
      NDWI00.640.690.590.620.600.750.270.46
      10.490.560.620.520.580.620.240.22
      20.340.540.400.330.490.380.410.17
      30.130.360.250.150.360.240.210.10
    • Table 4. The drought classification of Model-RSM

      View table
      View in Article

      Table 4. The drought classification of Model-RSM

      等级类型Model-RSM / %
      4~6月7~9月
      0无旱>60>60
      1轻旱55~6050~60
      2中旱40~5530~50
      3重旱30~4020~30
      4特旱<30<20
    Tools

    Get Citation

    Copy Citation Text

    Yi GAO, Yibo WANG, Xia ZHANG, Shiyu TAO. Soil Moisture Estimation and Drought Monitoring in Grape Growing Areas based on Time Series Data of MODIS[J]. Remote Sensing Technology and Application, 2024, 39(5): 1237

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Feb. 23, 2023

    Accepted: --

    Published Online: Jan. 7, 2025

    The Author Email:

    DOI:10.11873/j.issn.1004-0323.2024.5.1237

    Topics