Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data

Rhee, Jinyoung; Im, Jungho; Carbone, Gregory J.

doi:10.1016/j.rse.2010.07.005

Cited by 554 publications

(288 citation statements)

References 28 publications

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“…ω1, ω2 and 1 − ω1 -ω2 are the weights of the scaled precipitation, soil moisture, and CWC, respectively. We adopted the three sets of weights used in the work of Rhee and conducted experiments to identify the weights that have the best performance and selected them as the final weights [5,70]. Table 1 shows the details of the weights distribution, and GDI-1, GDI-2, and GDI-3 represents the GDI calculated by different weights.…”

Section: The Normalization and Integration Of Precipitation Sm And Cmentioning

confidence: 99%

“…However, the network of meteorological stations often lacks continuous spatial coverage, especially in mountainous regions [4]. Thus, spatial interpolation is a common method to obtain a continuous spatial distribution of drought conditions based on data from meteorological stations, but there may be considerable uncertainties involved in such an approach because many factors affect the interpolation process [5].…”

Section: Introductionmentioning

confidence: 99%

“…One of the most widely used indices was developed by Palmer in 1965. To better monitor drought in terms of its intensity, duration, and spatial extent, Palmer's original index was extended to create the monitoring in both arid and humid regions by integrating the data of precipitation, NDVI and LST [5]. Abbas used the Vegetation Supply Water Index (NVSWI), calculated as the ratio of the NDVI to the LST, to characterize the development of drought conditions in central Yunnan Province, China [38].…”

Section: Introductionmentioning

confidence: 99%

“…Abbas used the Vegetation Supply Water Index (NVSWI), calculated as the ratio of the NDVI to the LST, to characterize the development of drought conditions in central Yunnan Province, China [38]. Combining remote sensing data with in situ data from a relatively dense network, such as the Vegetation Drought Response Index (VegDRI) has proven extremely useful [5,28]. By including six key physical indicators and many supplementary indicators, United States Drought Monitor (USDM) maps have been published online as weekly product for drought monitoring in the US [2].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Global Grassland Drought Index (GDI) Product: Algorithm and Validation

Liao

Quan

et al. 2015

Remote Sensing

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Existing drought indices have been widely used to monitor meteorological drought and agricultural drought; however, few of them are focus on drought monitoring for grassland regions. This study presented a new drought index, the Grassland Drought Index (GDI), for monitoring drought conditions in global grassland regions. These regions are vital for the environment and human society but susceptible to drought. The GDI was constructed based on three measures of water content: precipitation, soil moisture (SM), and canopy water content (CWC). The precipitation information was extracted from the available precipitation datasets, and SM was estimated by downscaling exiting soil moisture data to a 1 km resolution, and CWC was retrieved based on the PROSAIL (PROSPECT + SAIL) model. Each variable was scaled from 0 to 1 for each pixel based on absolute minimum and maximum values over time, and these scaled variables were combined with the selected weights to construct the GDI. According to validation at the regional scale, the GDI was correlated with the Standardized Precipitation Index (SPI) to some extent, and captured most of the drought area identified by the United States Drought Monitor (USDM) maps. In addition, the global GDI product at a 1 km spatial resolution substantially agreed with the global Standardized Precipitation Evapotranspiration Index (SPEI) product throughout the period 2005-2010, and it provided detailed and accurate information about the location and the duration of drought based on the evaluation using the known drought events. OPEN ACCESSRemote Sens. 2015, 7 12705

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Section: The Normalization and Integration Of Precipitation Sm And Cmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Global Grassland Drought Index (GDI) Product: Algorithm and Validation

Liao

Quan

et al. 2015

Remote Sensing

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“…Remotely sensed measures of vegetation health, such as the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI), are widely used to monitor agricultural responses to drought (Peters et al, 2002;Rhee, Im, & Carbone, 2010). Providing managers and farmers with accurate information about vegetation health increases systemwide capacity to prepare for and adapt to water scarcity (Dessai, 2009;Ziervoge et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to the Prediction of Vegetation Health

Burchfield¹,

Nay²,

Gilligan³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern.

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Remote sensing of drought: Progress, challenges and opportunities

AghaKouchak

Farahmand

Melton

et al. 2015

Reviews of Geophysics

746

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 for arid and humid regions using multi-sensor remote sensing data

Cited by 554 publications

References 28 publications

A Global Grassland Drought Index (GDI) Product: Algorithm and Validation

A Global Grassland Drought Index (GDI) Product: Algorithm and Validation

Application of Machine Learning to the Prediction of Vegetation Health

Remote sensing of drought: Progress, challenges and opportunities

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