Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data

Son, N. T.; Chen, C. F.; Chang, Le; Minh, Võ Quang

doi:10.1016/j.jag.2012.03.014

Cited by 229 publications

(161 citation statements)

References 48 publications

(55 reference statements)

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“…Soil moisture and precipitation data were used to validate the calculation results of the TVDI [30]. The 10-day soil moisture data were obtained from the Dataset of the Growth and Development of Crops and the 10-day Soil Moisture of Farmland in China from the China Meteorological Data Sharing Service System.…”

Section: Soil Moisture and Precipitation Datamentioning

confidence: 99%

“…The TVDI for woodland was found to have been underestimated, and those of the grassland and farmland were overestimated when the dry and wet edges in Table 1 were inserted into Equation (2). To assess the efficacy of the two calculation methods, as an example, the soil moisture data of study area, which were collected in May 2010, were used to validate the results [30]. The soil moisture sampling points are shown as black triangular spots in Figure 1.…”

Section: Calculating Tvdi On the Basis Of Different Land Cover Typesmentioning

confidence: 99%

“…The correlations are shown in Figure 5; there is a significant correlation between the calculation results and the soil moisture, with a correlation coefficient (R 2 ) that ranges from 0.531 to 0.674. To assess the efficacy of the two calculation methods, as an example, the soil moisture data of study area, which were collected in May 2010, were used to validate the results [30]. The soil moisture sampling points are shown as black triangular spots in Figure 1.…”

Section: Calculating Tvdi On the Basis Of Different Land Cover Typesmentioning

confidence: 99%

See 2 more Smart Citations

The Spatiotemporal Variation of Drought in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) Based on the Modified TVDI

et al. 2016

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This study proposes a modified vegetation-dependent temperature-vegetation dryness index (TVDI) model for analyzing regional drought disasters in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) of China. First, MODIS monthly normalized difference vegetation index (NDVI), land surface temperature (LST) data and land use/cover data (Land cover type2) were pre-processed as a consistent big dataset. The land use/cover data were modified and integrated into six primary types. Then, these land types were used as the base data layer to calculate the TVDI by parameterizing the relationship between the MODIS NDVI and LST data. By emphasizing different types of land uses, this study was able to compare and analyze the differences of the TVDI indices between the entire study area (no consideration of the land types) and the six classified land uses. The soil moisture data were used to validate the modified TVDI values based on different land uses, which confirmed that the modified model more effectively reflected drought conditions. Finally, the aforementioned model was used to analyze the temporal and spatial variation of drought experienced by vegetation cover from 2000 to 2014. The results of the modified model were validated with the synchronized soil moisture and precipitation data. The case study clearly demonstrated that the modified TVDI model, which is based on different vegetation indexes, could better reflect the drought conditions of the study area.

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Section: Soil Moisture and Precipitation Datamentioning

confidence: 99%

Section: Calculating Tvdi On the Basis Of Different Land Cover Typesmentioning

confidence: 99%

Section: Calculating Tvdi On the Basis Of Different Land Cover Typesmentioning

confidence: 99%

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The Spatiotemporal Variation of Drought in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) Based on the Modified TVDI

et al. 2016

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“…Earth observations (EO) have served as an important information resource in DRM and has been applied to earthquakes [13][14][15], tsunami arrival predictions [16], volcanic hazards [17,18], floods [19][20][21][22][23], landslides [24,25], forest fires [26], and drought monitoring and prediction [27][28][29][30][31]. Earth observations can help to simultaneously capture the land condition of a broad area and are less expensive than field surveys.…”

Section: Earth Observationmentioning

confidence: 99%

“…For the preparedness phases, establishing an early warning system is the most recognized application of EO. Applications for early flood and drought warnings were proposed [20,21,[27][28][29] and have been introduced as pilot operations [45].…”

Section: Earth Observationmentioning

confidence: 99%

Reviews of Geospatial Information Technology and Collaborative Data Delivery for Disaster Risk Management

Miyazaki

Nagai

Shibasaki

2015

IJGI

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Due to the fact that geospatial information technology is considered necessary for disaster risk management (DRM), the need for more effective collaborations between providers and end users in data delivery is increasing. This paper reviews the following: (i) schemes of disaster risk management and collaborative data operation in DRM; (ii) geospatial information technology in terms of applications to the schemes reviewed; and (iii) ongoing practices of collaborative data delivery with the schemes reviewed. This paper concludes by discussing the future of collaborative data delivery and the progress of the technologies.

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Remote sensing of drought: Progress, challenges and opportunities

AghaKouchak

Farahmand

Melton

et al. 2015

Reviews of Geophysics

719

400

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This review surveys current and emerging drought monitoring approaches using satellite remote sensing observations from climatological and ecosystem perspectives. We argue that satellite observations not currently used for operational drought monitoring, such as near-surface air relative humidity data from the Atmospheric Infrared Sounder mission, provide opportunities to improve early drought warning. Current and future satellite missions offer opportunities to develop composite and multi-indicator drought models. While there are immense opportunities, there are major challenges including data continuity, unquantified uncertainty, sensor changes, and community acceptability. One of the major limitations of many of the currently available satellite observations is their short length of record. A number of relevant satellite missions and sensors (e.g., the Gravity Recovery and Climate Experiment) provide only a decade of data, which may not be sufficient to study droughts from a climate perspective. However, they still provide valuable information about relevant hydrologic and ecological processes linked to this natural hazard. Therefore, there is a need for models and algorithms that combine multiple data sets and/or assimilate satellite observations into model simulations to generate long-term climate data records. Finally, the study identifies a major gap in indicators for describing drought impacts on the carbon and nitrogen cycle, which are fundamental to assessing drought impacts on ecosystems.

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Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data

Cited by 229 publications

References 48 publications

The Spatiotemporal Variation of Drought in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) Based on the Modified TVDI

The Spatiotemporal Variation of Drought in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) Based on the Modified TVDI

Reviews of Geospatial Information Technology and Collaborative Data Delivery for Disaster Risk Management

Remote sensing of drought: Progress, challenges and opportunities

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