Drought monitoring using an Integrated Drought Condition Index (IDCI) derived from multi-sensor remote sensing data

2015 ◽  
Vol 80 (2) ◽  
pp. 1135-1152 ◽  
Author(s):  
Lingkui Meng ◽  
Ting Dong ◽  
Wen Zhang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Condition Index ◽  
Drought Monitoring ◽  
Drought Condition ◽  
Sensing Data

scholarly journals Construction of Comprehensive Drought Monitoring Model in Jing-Jin-Ji Region Based on Multisource Remote Sensing Data

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1077 ◽  
Author(s):  
Haozhe Yu ◽  
Lijuan Li ◽  
Yang Liu ◽  
Jiuyi Li
Keyword(s):  
Remote Sensing ◽  
Correlation Coefficient ◽  
Remote Sensing Data ◽  
Condition Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Sensing Data ◽  
Monitoring Model ◽  
Crop Area ◽  
Unit Yield

Drought is a complex hazard that has more adverse effects on agricultural production and economic development. Studying drought monitoring techniques and assessment methods can improve our ability to respond to natural disasters. Numerous drought indices deriving from meteorological or remote sensing data are focused mainly on monitoring single drought response factors such as soil or vegetation, and the ability to reflect comprehensive information on drought was poor. This study constructed a comprehensive drought-monitoring model considering the drought factors including precipitation, vegetation growth status, and soil moisture balance during the drought process for the Jing-Jin-Ji region, China. The comprehensive drought index of remote sensing (CDIR), a drought indicator deduced by the model, was composed of the vegetation condition index (VCI), the temperature condition index (TCI), and the precipitation condition index (PCI). The PCI was obtained from the Tropical Rainfall Measuring Mission (TRMM) satellite. The VCI and TCI were obtained from a moderate-resolution imaging spectroradiometer (MODIS). In this study, a heavy drought process was accurately explored using the CDIR in the Jing-Jin-Ji region in 2016. Finally, a three-month scales standardized precipitation index (SPI-3), drought affected crop area, and standardized unit yield of wheat were used as validation to evaluate the accuracy of this model. The results showed that the CDIR is closely related to the SPI-3, as well as variations in the drought-affected crop area and standardized unit yield of crop. The correlation coefficient of the CDIR with SPI-3 was between 0.45 and 0.85. The correlation coefficient between the CDIR and drought affected crop was between −0.81 and −0.86. Moreover, the CDIR was positively correlated with the standardized unit yield of crop. It showed that the CDIR index is a decent indicator that can be used for integrated drought monitoring and that it can synthetically reflect meteorological and agricultural drought information.

Drought monitoring in North China using HJ-1 satellite remote sensing data

2009 ◽  
Author(s):  
Bingfeng Yang ◽  
Qiao Wang ◽  
Changzuo Wang ◽  
Huawei Wan ◽  
Yipeng Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
North China ◽  
Remote Sensing Data ◽  
Drought Monitoring ◽  
Sensing Data

scholarly journals STUDY ON REMOTE SENSING MONITORING MODEL OF AGRICULTURAL DROUGHT BASED ON RANDOM FOREST DEVIATION CORRECTION

2021 ◽  
pp. 413-422
Author(s):  
Shao Li ◽  
Xia Xu
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Data Sources ◽  
Drought Monitoring ◽  
Monitoring Methods ◽  
Large Area ◽  
Sensing Data ◽  
Monitoring Model

Using remote sensing data to monitor large area drought is one of the important methods of drought monitoring at present. However, the traditional remote sensing drought monitoring methods mainly focus on monitoring single drought response factors such as soil moisture or vegetation status, and the research on comprehensive multi-factor drought monitoring is limited. In order to improve the ability to resist drought events, this paper takes Henan Province of China as an example, takes multi-source remote sensing data as data sources, considers various disaster-causing factors, adopts random forest method to model, and explores the method of regional remote sensing comprehensive drought monitoring using various remote sensing data sources. Compared with neural network, classification regression tree and linear regression, the performance of random forest is more stable and tolerant to noise and outliers. In order to provide a new method for comprehensive assessment of regional drought, a comprehensive drought monitoring model was established based on multi-source remote sensing data, which comprehensively considered the drought factors such as soil water stress, vegetation growth status and meteorological precipitation profit and loss in the process of drought occurrence and development.

scholarly journals Monitoring Droughts in the Greater Changbai Mountains Using Multiple Remote Sensing-Based Drought Indices

2020 ◽  
Vol 12 (3) ◽  
pp. 530 ◽  
Author(s):  
Yang Han ◽  
Ziying Li ◽  
Chang Huang ◽  
Yuyu Zhou ◽  
Shengwei Zong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Lag ◽  
Remote Sensing Data ◽  
Meteorological Drought ◽  
Condition Index ◽  
Temporal Variations ◽  
Changbai Mountains ◽  
Drought Indices ◽  
Drought Condition ◽  
Meteorological Observations

Various drought indices have been developed to monitor drought conditions. Each index has typical characteristics that make it applicable to a specific environment. In this study, six popular drought indices, namely, precipitation condition index (PCI), temperature condition index (TCI), vegetation condition index (VCI), vegetation health index (VHI), scaled drought condition index (SDCI), and temperature–vegetation dryness index (TVDI), have been used to monitor droughts in the Greater Changbai Mountains(GCM) in recent years. The spatial pattern and temporal trend of droughts in this area in the period 2001–2018 were explored by calculating these indices from multi-source remote sensing data. Significant spatial–temporal variations were identified. The results of a slope analysis along with the F-statistic test showed that up to 20% of the study area showed a significant increasing or decreasing trend in drought. It was found that some drought indices cannot be explained by meteorological observations because of the time lag between meteorological drought and vegetation response. The drought condition and its changing pattern differ from various land cover types and indices, but the relative drought situation of different landforms is consistent among all indices. This work provides a basic reference for reasonably choosing drought indices for monitoring drought in the GCM to gain a better understanding of the ecosystem conditions and environment.

Study on Agricultural Drought Monitoring Based on Multi-Sources Remote Sensing Data

2014 ◽  
Vol 12 (3) ◽  
pp. 659-666
Author(s):  
He Huang ◽  
Yida Fan ◽  
Siquan Yang ◽  
Wenbo Li ◽  
Haixia He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Sensing Data
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