Drought Monitoring in Food-Insecure Areas of Ethiopia by Using Satellite Technologies

2011 ◽  
pp. 183-200
Author(s):  
Getachew Berhan ◽  
Tsegaye Tadesse ◽  
Solomon Atnafu ◽  
Shawndra Hill
Keyword(s):  
Drought Monitoring

Review of Drought Monitoring by Remote Sensing

2012 ◽  
Vol 14 (2) ◽  
pp. 232-239 ◽  
Author(s):  
Huan LIU ◽  
Ronggao LIU ◽  
Shiyang LIU
Keyword(s):  
Remote Sensing ◽  
Drought Monitoring

Drought Monitoring using High Spatial Resolution Soil Moisture Data over Australia in 2015-2019

2021 ◽  
pp. 125960
Author(s):  
Bin Fang ◽  
Prakrut Kansara ◽  
Chelsea Dandridge ◽  
Venkat Lakshmi
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Drought Monitoring ◽  
Soil Moisture Data

Drought monitoring in arid and semi-arid region based on multi-satellite datasets in northwest, China

2021 ◽  
Author(s):  
Wei Wei ◽  
Haoyan Zhang ◽  
Junju Zhou ◽  
Liang Zhou ◽  
Binbin Xie ◽  
...  
Keyword(s):  
Arid Region ◽  
Northwest China ◽  
Drought Monitoring ◽  
Semi Arid Region ◽  
Semi Arid

scholarly journals Assessment of the Dissimilarities of EDI and SPI Measures for Drought Determination in South Africa

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 82
Author(s):  
Omolola M. Adisa ◽  
Muthoni Masinde ◽  
Joel O. Botai
Keyword(s):  
South Africa ◽  
South African ◽  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Standardized Precipitation Index ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Drought Severity ◽  
Pearson Correlation Coefficient ◽  
Drought Duration

This study examines the (dis)similarity of two commonly used indices Standardized Precipitation Index (SPI) computed over accumulation periods 1-month, 3-month, 6-month, and 12-month (hereafter SPI-1, SPI-3, SPI-6, and SPI-12, respectively) and Effective Drought Index (EDI). The analysis is based on two drought monitoring indicators (derived from SPI and EDI), namely, the Drought Duration (DD) and Drought Severity (DS) across the 93 South African Weather Service’s delineated rainfall districts over South Africa from 1980 to 2019. In the study, the Pearson correlation coefficient dissimilarity and periodogram dissimilarity estimates were used. The results indicate a positive correlation for the Pearson correlation coefficient dissimilarity and a positive value for periodogram of dissimilarity in both the DD and DS. With the Pearson correlation coefficient dissimilarity, the study demonstrates that the values of the SPI-1/EDI pair and the SPI-3/EDI pair exhibit the highest similar values for DD, while the SPI-6/EDI pair shows the highest similar values for DS. Moreover, dissimilarities are more obvious in SPI-12/EDI pair for DD and DS. When a periodogram of dissimilarity is used, the values of the SPI-1/EDI pair and SPI-6/EDI pair exhibit the highest similar values for DD, while SPI-1/EDI displayed the highest similar values for DS. Overall, the two measures show that the highest similarity is obtained in the SPI-1/EDI pair for DS. The results obtainable in this study contribute towards an in-depth knowledge of deviation between the EDI and SPI values for South Africa, depicting that these two drought indices values are replaceable in some rainfall districts of South Africa for drought monitoring and prediction, and this is a step towards the selection of the appropriate drought indices.

scholarly journals Different Drought Legacies of Rain-Fed and Irrigated Croplands in a Typical Russian Agricultural Region

2020 ◽  
Vol 12 (11) ◽  
pp. 1700
Author(s):  
Yuanhuizi He ◽  
Fang Chen ◽  
Huicong Jia ◽  
Lei Wang ◽  
Valery G. Bondur
Keyword(s):  
Surface Temperature ◽  
Drought Index ◽  
Growth Period ◽  
Meteorological Drought ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Growth Stages ◽  
Drought Conditions ◽  
Irrigated Lands ◽  
Meteorological Droughts

Droughts are one of the primary natural disasters that affect agricultural economies, as well as the fire hazards of territories. Monitoring and researching droughts is of great importance for agricultural disaster prevention and reduction. The research significance of investigating the hysteresis of agricultural to meteorological droughts is to provide an important reference for agricultural drought monitoring and early warnings. Remote sensing drought monitoring indices can be employed for rapid and accurate drought monitoring at regional scales. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and the surface temperature product are used as the data sources. Calculating the temperature vegetation drought index (TVDI) and constructing a comprehensive drought disaster index (CDDI) based on the crop growth period allowed drought conditions and spatiotemporal evolution patterns in the Volgograd region in 2010 and 2012 to be effectively monitored. The causes of the drought were then analyzed based on the sensitivity of a drought to meteorological factors in rain-fed and irrigated lands. Finally, the lag time of agricultural to meteorological droughts and the hysteresis in different growth periods were analyzed using statistical analyses. The research shows that (1) the main drought patterns in 2010 were spring droughts from April to May and summer droughts from June to August, and the primary drought patterns in 2012 were spring droughts from April to June, with an affected area that reached 3.33% during the growth period; (2) local drought conditions are dominated by the average surface temperature factor. Rain-fed lands are sensitive to the temperature and are therefore prone to summer droughts. Irrigated lands are more sensitive to water shortages in the spring and less sensitive to extremely high temperature conditions; (3) there is a certain lag between meteorological and agricultural droughts during the different growth stages. The strongest lag relationship was found in the planting stage and the weakest one was found in the dormancy stage. Therefore, the meteorological drought index in the growth period has a better predictive ability for agricultural droughts during the appropriately selected growth stages.

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