Evaluation of multi-sensor satellite data for monitoring different drought impacts

Park, Seo-Yeon; Sur, Chanyang; Kim, Jong‐Suk; Lee, Joo-Heon

doi:10.1007/s00477-018-1537-x

Cited by 24 publications

(18 citation statements)

References 25 publications

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“…The results related to the decrease in crop yield in North Korea were published from FAO/GIEWS [58,60] as well as RDA. In 2014, the yield of rice decreased by about −5.7% and the yield of maize increased by about 16.4% compared to 2013 [60]. In 2015, the yields of rice and maize decreased by about 16.5% and 7.5% in 2015 compared to 2014 [58].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, a negative value of SPI represents dry conditions. The drought severity of SPI is generally classified as shown in Table 2 [11,16,41]. Table 2.…”

Section: Meteorological Drought Indexmentioning

confidence: 99%

“…Table 2. Drought classes of SPI, VHI, TCI, and VCI [11,16,41]. In addition, SPI can be produced as multiple time scales such as 1-, 3-, 6-, and 12 months.…”

Section: Meteorological Drought Indexmentioning

confidence: 99%

“…According the previous studies, the value of α is 0.5 [16,50,51]. The drought severity of VHI, TCI, and VCI are classified in Table 2.…”

Section: Agricultural Drought Indexmentioning

confidence: 99%

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Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

et al. 2019

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Satellite-based remote sensing techniques have been widely used to monitor droughts spanning large areas. Various agricultural drought indices have been developed to assess the intensity of agricultural drought and to detect damaged crop areas. However, to better understand the responses of agricultural drought to meteorological drought, agricultural management practices should be taken into consideration. This study aims to evaluate the responses to drought under different forms of agricultural management for the extreme drought that occurred on the Korean Peninsula in 2014 and 2015. The 3-month standardized precipitation index (SPI3) and the 3-month vegetation health index (VHI3) were selected as a meteorological drought index and an agricultural drought index, respectively. VHI3, which comprises the 3-month temperature condition index (TCI3) and the 3-month vegetation condition index (VCI3), differed significantly in the study area during the extreme drought. VCI3 had a different response to the lack of precipitation in South and North Korea because it was affected by irrigation. However, the time series of TCI3 were similar in South and North Korea. These results meant that each drought index has different characteristics and should be utilized with caution. Our results are expected to help comprehend the responses of the agricultural drought index on meteorological drought depending on agricultural management.

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Section: Discussionmentioning

confidence: 99%

“…Thus, a negative value of SPI represents dry conditions. The drought severity of SPI is generally classified as shown in Table 2 [11,16,41]. Table 2.…”

Section: Meteorological Drought Indexmentioning

confidence: 99%

“…Table 2. Drought classes of SPI, VHI, TCI, and VCI [11,16,41]. In addition, SPI can be produced as multiple time scales such as 1-, 3-, 6-, and 12 months.…”

Section: Meteorological Drought Indexmentioning

confidence: 99%

“…According the previous studies, the value of α is 0.5 [16,50,51]. The drought severity of VHI, TCI, and VCI are classified in Table 2.…”

Section: Agricultural Drought Indexmentioning

confidence: 99%

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Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

et al. 2019

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“…Satellite imagery provides a powerful and effective means for describing and monitoring a great variety of information related to urbanization, pollution, deforestation, climate change, sea level, and many others agricultural or environmental applications (see for example Xu et al, 2015;Van de Kassteele et al, 2006;Park et al, 2018). Long-term changes of many derived variables and vegetation indexes are now easier to analyse because of the free availability of satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

Filling missing data and smoothing altered data in satellite imagery with a spatial functional procedure

Militino

Ugarte

Montesino

2019

Stoch Environ Res Risk Assess

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Outliers and missing data are commonly found in satellite imagery. These are usually caused by atmospheric or electronic failures, hampering the correct monitoring of remote-sensing data. To avoid distorted data, we propose a procedure called "spatial functional prediction" (SFP). The SFP procedure consists of the following: 1) aggregating remote-sensing data for reducing the number of missing data and/or outliers; 2) additively decomposing the time series of images into a trend, a seasonal, and an error component; 3) defining the spatial functional data and predicting the trend component using an ordinary kriging; and 4) adding back the seasonal and error components to the predicted trend. The benefits of the SFP procedure are illustrated in the following scenarios: introducing random outliers, random missing data, mixtures of both, and artificial clouds in an extensive simulation study of composite images, and using daily images with real clouds. The following two derived variables are considered: land surface temperature (LST day) and normalized vegetation index (NDVI), which are obtained as remote-sensing data in a region in northern Spain during 2003-2016. The performance of SFP was checked using the root mean square error (RMSE). A comparison with a procedure based on predicting with thin-plate splines (TpsP) is also made. We conclude that SFP is simpler and faster than TpsP, and provides smaller values of RMSE.

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Drought risk assessment for future climate projections in the Nakdong River Basin, Korea

Kim

Park

Hong

et al. 2020

Intl Journal of Climatology

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To prepare for the potential of extreme drought caused by climate change, both physical and socioeconomic factors need to be considered. In this study, three general circulation models were selected: IPSL‐CM5A‐LR, HadGEM2‐AO, and CanESM2 as representative scenarios for severe, moderate, and relatively weak droughts to assess the risk of droughts for future climate projections. Drought hazard was estimated using the meteorological drought index to determine drought frequency, severity, and probability. Population density, agricultural land area, and municipal, agricultural, and industrial water demand were used as drought vulnerability indicators. The Analytical Hierarchy Process technique was applied to determine the weight of each indicator, and the integrated drought risk was then estimated. HadGEM2‐AO and CanESM2 projected that drought risk would be reduced in the far future. IPSL‐CM5A‐LR projected an increased drought risk in the near and far future in the downstream area of the Nakdong River Basin. This study presented a quantitative approach to identify high drought‐risk areas that should be the focus of future extreme drought assessments and mid‐ to long‐term drought mitigation strategies.

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Evaluation of multi-sensor satellite data for monitoring different drought impacts

Cited by 24 publications

References 25 publications

Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

Filling missing data and smoothing altered data in satellite imagery with a spatial functional procedure

Drought risk assessment for future climate projections in the Nakdong River Basin, Korea

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