Mapping MODIS LST NDVI Imagery for Drought Monitoring in Punjab Pakistan

Khan, Jahangir; Wang, Pengxin; Xie, Yi; Li, Li

doi:10.1109/access.2018.2821717

Cited by 32 publications

(11 citation statements)

References 38 publications

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“…The multiyear Sentinel-3 VTCIs over 10-day intervals exhibit a significant correlation with the cumulative precipitation over the past 20 days (R 2 is 0.731, P-value is less than 0.001), in which the data before mid-April are mainly distributed in the lower left part of the figures and the data in early May are mainly distributed in the upper right part of the figures. Similar results can be found in the studies by Lin et al [21] and Khan et al [12]. Heyang is one of the counties with rainfed croplands in the Guanzhong Plain, and it is located in the northeast of the plain as shown in Fig.…”

Section: F Precipitation and Multiyear Sentinel-3 Vtcisupporting

confidence: 90%

Drought Monitoring Using the Sentinel-3-Based Multiyear Vegetation Temperature Condition Index in the Guanzhong Plain, China

Zhou

Wang

Tansey

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The vegetation temperature condition index (VTCI) has been shown to perform well for drought monitoring using multiyear advanced very high-resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) data. Compared to single-year VTCI, the VTCI calculated by multiyear data can quantitatively reflect the degree of drought and precipitation in a region. Sentinel-3 is a recently launched remote sensing satellite with high temporal resolution and is similar to the satellites carrying AVHRR and MODIS sensors. One year of Sentinel-3 data is available for calculating the VTCI, and given the need for developing quantitatively drought monitoring capabilities, the aim of this study is to investigate the methods used to calculate the potential multiyear Sentinel-3 VTCI for quantitative drought monitoring. This is based on a comparison with multiyear Terra MODIS VTCI and a correlation analysis with cumulative precipitation data. The analysis results indicate that the potential multiyear Sentinel-3 VTCI can be accurately calculated from the single-year Sentinel-3 VTCI based on the linear correlation between the single-year VTCI and multiyear VTCI derived from Terra MODIS, which do not exhibit obvious systematic deviations from the multiyear Terra MODIS VTCI (the absolute value of the average bias <0.04). The multiyear Sentinel-3 VTCIs can quantitatively reflect the drought levels, which are significantly correlated with the recent cumulative precipitation throughout the study period (R 2 = 0.731, P < 0.001). Therefore, it is proposed that Sentinel-3 can successfully inherit the VTCI-based drought monitoring tasks from MODIS.

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Section: F Precipitation and Multiyear Sentinel-3 Vtcisupporting

confidence: 90%

Drought Monitoring Using the Sentinel-3-Based Multiyear Vegetation Temperature Condition Index in the Guanzhong Plain, China

Zhou

Wang

Tansey

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In this case, the atmospheric conditions are likely to be also relatively stable. In the research of Liu et al (2017), Cao et al (2017), and Khan et al (2018), they also used MVC to composite 8-day Ts and 16-day NDVI images into monthly or yearly images and it obtained dry and wet edges from time series MDOIS data [2,52,53]. The main hypothesis of T s /NDVI approaches is that atmospheric conditions (including elevation effects) are relatively constant or uniform over the whole image [50,51].…”

Section: Scatter Plots Of Bi-parabolic and Triangular Ndvi-t S Spacementioning

confidence: 99%

“…Liu et al (2017),Cao et al (2017), andKhan et al (2018), they also used MVC to composite 8-day T s and 16-day NDVI images into monthly or yearly images and it obtained dry and wet edges from time series MDOIS data[2,52,53].…”

mentioning

confidence: 99%

The Temperature Vegetation Dryness Index (TVDI) Based on Bi-Parabolic NDVI-Ts Space and Gradient-Based Structural Similarity (GSSIM) for Long-Term Drought Assessment Across Shaanxi Province, China (2000–2016)

Liu

Yue

2018

Remote Sensing

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Traditional NDVI-T s space is triangular or trapezoidal, but Liu et al. (2015) discovered that the NDVI-T s space was bi-parabolic when the study area was covered with low biomass vegetation. Moreover, the numerical value of the indicator was considered in most of the study when the drought conditions in the space domain were evaluated. In addition, quantitatively assessing the spatial-temporal changes of the drought was not enough. In this study, first, we used MODIS NDVI and T s data to reexamine if the NDVI-T s space with "time" and a single pixel domain is bi-parabolic in the Shaanxi province of China, which is vegetated with low biomass to high biomass. This is compared with the triangular NDVI-T s space and one of the well-known drought indexes called the temperature-vegetation index (TVX). The results demonstrated that dry and wet edges exhibited a parabolic shape again in scatter plots of T s and NDVI in the Shaanxi province, which was linear in the triangular NDVI-T s space. The Temperature Vegetation Dryness Index (TVDI c) was obtained from bi-parabolic NDVI-T s andTVDI t was obtained from the triangular NDVI-T s space and TVX were compared with 10-cm depth relative soil moisture. By estimating the 10-cm depth soil moisture, TVDI c was better than TVDI t , which were all apparently better than TVX. Second, combined with MODIS data, the drought conditions of the study area were assessed by TVDI c between 2000 to 2016. Spatially, the drought in the Shaanxi Province between 2000 to 2016 were mainly distributed in the northwest, North Shaanxi, and the North and East Guanzhong plain. The drought area of the Shaanxi province accounted for 31.95% in 2000 and 27.65% in 2016, respectively. Third, we quantitatively evaluated the variation of the drought status by using Gradient-based Structural Similarity (GSSIM) methods. The area of the drought conditions significantly changed and moderately changed at 5.34% and 40.22%, respectively, between 2000 and 2016. Finally, the possible reasons for drought change were discussed. The change of precipitation, temperature, irrigation, destruction or betterment of vegetation, and the enlargement of opening mining, etc., can lead to the variations of drought.

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“…Reflectance related to SOM content can be estimated with validation set by using the model mentioned in (4). RMSEPs between estimated and measured reflectance were computed wavelength-by-wavelength in the range of 450-2500 nm.…”

Section: A Testing Of Som Radiative Transfer Modelmentioning

confidence: 99%

Wavelength Selection for Estimating Soil Organic Matter Contents Through the Radiative Transfer Model

Yuan

Wang

et al. 2020

IEEE Access

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Considering that large quantities of soil hyperspectral data include the redundancy and overlap of spectral information, the technology of selecting feature wavelength can effectively solve these problems, and improve the accuracy and stability of the soil organic matter (SOM) content retrieval model. Traditional methods of wavelength selection mainly attempt to establish the empirical relationship between reflectance and SOM contents, and the performance is directly related to the quality and representativeness of the "training data". This study first distinguished the sensitive wavelength interval of SOM through the sensitivity analysis (SA) of the SOM to soil reflectance in radiative transfer model. Then sensitive wavelength points of SOM were ascertained using the successive projection algorithm (SPA): 468, 476, 496, 599, 775 and 900nm. Results show that SOM content can be estimated with high accuracy (root-meansquare error of prediction (RMSEP) < 0.234%, coefficient of determination (R 2)> 82.9%) by adopting the selected six wavelengths. Especially at 599nm, the accuracy of SOM content estimation is the highest (RMSEP: 0.176%, R 2 : 90.4%). Compared with traditional empirical wavelength selection methods, the wavelength selection based on the SA-SPA with the SOM radiative transfer model improves the generalizability and accuracy of the result. The research provides theoretical basis and technical support for the remote sensing retrieval of SOM, the development of rapid spectral instruments, and the bands setting of sensor instrument.

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Mapping MODIS LST NDVI Imagery for Drought Monitoring in Punjab Pakistan

Cited by 32 publications

References 38 publications

Drought Monitoring Using the Sentinel-3-Based Multiyear Vegetation Temperature Condition Index in the Guanzhong Plain, China

Drought Monitoring Using the Sentinel-3-Based Multiyear Vegetation Temperature Condition Index in the Guanzhong Plain, China

The Temperature Vegetation Dryness Index (TVDI) Based on Bi-Parabolic NDVI-Ts Space and Gradient-Based Structural Similarity (GSSIM) for Long-Term Drought Assessment Across Shaanxi Province, China (2000–2016)

Wavelength Selection for Estimating Soil Organic Matter Contents Through the Radiative Transfer Model

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