Drought Monitoring Based on Ground and Satellite Data

2019 ◽  
Vol 44 (11) ◽  
pp. 772-781
Author(s):  
A. D. Kleshchenko ◽  
V. V. Asmus ◽  
A. I. Strashnaya ◽  
V. A. Krovotyntsev ◽  
O. V. Virchenko ◽  
...  
Keyword(s):  
Satellite Data ◽  
Drought Monitoring

Monitoring Drought Using Coarse Resolution Polar-Orbiting Satellite Data

2005 ◽  
Author(s):  
Assaf Anyamba ◽  
Compton J. Tucker
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Electromagnetic Energy ◽  
Heat Fluxes ◽  
Aerial Photographs ◽  
Drought Monitoring ◽  
Resource Monitoring ◽  
Polar Orbiting Satellite ◽  
Pass Through ◽  
Earth Resource

There are two distinct categories of remotely sensed data: satellite data and aerial data or photographs. Unlike aerial photographs, satellite data have been routinely available for most of the earth’s land areas for more than two decades and therefore are preferred for reliably monitoring global vegetation conditions. Satellite data are the result of reflectance, emission, and/or back scattering of electromagnetic energy from earth objects (e.g., vegetation, soil, and water). The electromagnetic spectrum is very broad, and only a limited range of wavelengths is suitable for earth resource monitoring and applications. The gaseous composition (O2, O3, CO2, H2O, etc.) of the atmosphere, along with particulates and aerosols, cause significant absorption and scattering of electromagnetic energy over some regions of the spectrum. This restricts remote sensing of the earth’s surface to certain “atmospheric windows,” or regions in which electromagnetic energy can pass through the atmosphere with minimal interference. Some such windows include visible, infrared, shortwave, thermal, and microwave ranges of the spectrum. The shortwave-infrared (SWIR) wavelengths are sensitive to moisture content of vegetation, whereas the thermal-infrared region is useful for monitoring and detecting plant canopy stress and for modeling latent and sensible heat fluxes. Thermal remote sensing imagery is acquired both during the day and night, and it measures the emitted energy from the surface, which is related to surface temperatures and the emissivity of surface materials. This chapter focuses on the contribution of visible and infrared wavelengths to global drought monitoring, and chapter 6 discusses visible, infrared, and thermal wave contributions. Under microwave windows, the satellite data can be divided into two categories: active microwave and passive microwave. Chapters 7 and 8 describe applications of passive and active microwave remote sensing to drought monitoring, respectively. Early use of satellite data was pioneered by the Landsat series originally known as the Earth Resource Technology Satellite (ERTS; http://landsat7. usgs.gov/index.php). Landsat was the first satellite specifically designed for broad-scale observation of the earth’s land surface.

scholarly journals Mapping daily evapotranspiration and dryness index in the East African highlands using MODIS and SEVIRI data

2011 ◽  
Vol 15 (1) ◽  
pp. 163-170 ◽  
Author(s):  
Z. Sun ◽  
M. Gebremichael ◽  
J. Ardö ◽  
H. A. R. de Bruin
Keyword(s):  
Satellite Data ◽  
Weather Forecast ◽  
Drought Monitoring ◽  
East African ◽  
Daily Fluctuation ◽  
Orbit Satellite ◽  
Combining Data ◽  
Dryness Index ◽  
African Highlands ◽  
East African Highlands

Abstract. Routine information on regional evapotranspiration (ET) and dryness index is essential for agricultural water management, drought monitoring, and studies of water cycle and climate. However, this information is not currently available for the East Africa highlands. The main purpose of this study is to develop (1) a new methodology that produces spatially gridded daily ET estimates on a (near) real-time basis exclusively from satellite data, and (2) a new dryness index that depends only on satellite data and weather forecast data. The methodology that calculates daily actual ET involves combining data from two sensors (MODIS and SEVIRI) onboard two kinds of platforms (Terra – polar orbit satellite and MSG – geostationary orbit satellite). The methodology is applied to the East African highlands, and results are compared to eddy covariance measurements at one site. Results show that the methodology produces ET estimates that accurately reproduce the daily fluctuation in ET but tends to underestimate ET on the average. It is concluded that the synergistic use of the polar-orbiting MODIS data and the geostationary-orbiting SEVIRI data has potential to produce reliable daily ET, but further research is needed to improve the accuracy of the results. This study also proposes an operational new dryness index that can be calculated from the satellite-based daily actual ET estimates and daily reference ET estimates based on SEVIRI data and weather forecast air temperature. Comparison of this index against ground measurements of daily actual ET at one site indicates that the new dryness index can be used for drought monitoring.

scholarly journals Integrated Drought Monitoring and Evaluation through Multi-Sensor Satellite-Based Statistical Simulation

2021 ◽  
Vol 13 (2) ◽  
pp. 272
Author(s):  
Jong-Suk Kim ◽  
Seo-Yeon Park ◽  
Joo-Heon Lee ◽  
Jie Chen ◽  
Si Chen ◽  
...  
Keyword(s):  
Satellite Data ◽  
Drought Index ◽  
Principal Component ◽  
Monitoring And Evaluation ◽  
Statistical Simulation ◽  
Drought Monitoring ◽  
Severe Drought ◽  
Evolutionary Pattern ◽  
Major River ◽  
Hydrological Droughts

To proactively respond to changes in droughts, technologies are needed to properly diagnose and predict the magnitude of droughts. Drought monitoring using satellite data is essential when local hydrogeological information is not available. The characteristics of meteorological, agricultural, and hydrological droughts can be monitored with an accurate spatial resolution. In this study, a remote sensing-based integrated drought index was extracted from 849 sub-basins in Korea’s five major river basins using multi-sensor collaborative approaches and multivariate dimensional reduction models that were calculated using monthly satellite data from 2001 to 2019. Droughts that occurred in 2001 and 2014, which are representative years of severe drought since the 2000s, were evaluated using the integrated drought index. The Bayesian principal component analysis (BPCA)-based integrated drought index proposed in this study was analyzed to reflect the timing, severity, and evolutionary pattern of meteorological, agricultural, and hydrological droughts, thereby enabling a comprehensive delivery of drought information.

scholarly journals Mapping daily evapotranspiration and dryness index in the East African highlands using MODIS and SEVIRI data

2010 ◽  
Vol 7 (4) ◽  
pp. 6285-6303 ◽  
Author(s):  
Z. Sun ◽  
M. Gebremichael ◽  
H. A. R. de Bruin
Keyword(s):  
Satellite Data ◽  
Weather Forecast ◽  
Drought Monitoring ◽  
East African ◽  
Daily Fluctuation ◽  
Orbit Satellite ◽  
Combining Data ◽  
Dryness Index ◽  
African Highlands ◽  
East African Highlands

Abstract. Routine information on regional evapotranspiration (ET) and dryness index is essential for agricultural water management, drought monitoring, and studies of water cycle and climate. However, this information is not currently available for the East Africa highlands. The main purpose of this study is to develop (1) a new methodology that produces spatially gridded daily ET estimates on a (near) real-time basis exclusively from satellite data, and (2) a new dryness index that depends only on satellite data and weather forecast data. The methodology that calculates daily actual ET involves combining data from two sensors (MODIS and SEVIRI) onboard two kinds of platforms (Terra/Aqua – polar orbit satellite and MSG – geostationary orbit satellite). The methodology is applied to the East African highlands, and results are compared to eddy covariance measurements at one site. Results show that the methodology produces ET estimates that have high skills in reproducing the daily fluctuation in ET but tends to underestimate ET on the average. It is concluded that the synergistic use of the polar-orbiting MODIS data and the geostationary-orbiting SEVIRI data has potential to produce reliable daily ET, but further research is needed to improve the accuracy of the results. This study also proposes an operational new dryness index that can be calculated from the satellite-based actual daily ET estimates and reference daily ET estimates based on SEVIRI data and weather forecast air temperature. Comparison of this index against ground measurements of actual daily ET at one site indicates that the new dryness index is operational for drought monitoring.

scholarly journals COMBINATION OF METEOROLOGICAL INDICES AND SATELLITE DATA FOR DROUGHT MONITORING IN TWO DIFFERENT ENVIRONMENTS IN IRAN

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 197-200
Author(s):  
M. Behifar ◽  
A. A. Kakroodi ◽  
M. Kiavarz ◽  
F. Amiraslani
Keyword(s):  
Satellite Data ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Precipitation Index ◽  
Drought Monitoring ◽  
Spatial Characteristic ◽  
Drought Indices ◽  
Precipitation Data ◽  
Spi Index ◽  
The Standardized Precipitation Index

Abstract. The main problem using meteorological drought indices include inappropriate distribution of meteorological stations. Satellite data have reliable spatial and temporal resolution and provide valuable information used in many different applications. The Standardized precipitation index has several advantages. The SPI is based on rainfall data alone and has a variable time scale and is thus conducive to describing drought conditions for different application.This study aims to calculate SPI using satellite precipitation data and compare the results with traditional methods. To do this, satellite-based precipitation data were assessed against station data and then the standardized precipitation index was calculated. The results have indicated that satellite-based SPI could illustrate drought spatial characteristic more accurate than station-based index. Also, the standardized property of the SPI index allows comparisons between different locations, which is one of the remote sensing drought indices limitations.

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