scholarly journals Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States

2011 ◽  
Vol 24 (8) ◽  
pp. 2025-2044 ◽  
Author(s):  
Martha C. Anderson ◽  
Christopher Hain ◽  
Brian Wardlow ◽  
Agustin Pimstein ◽  
John R. Mecikalski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Drought Index ◽  
Temporal Correlation ◽  
Meteorological Drought ◽  
Rainfall Data ◽  
Added Value ◽  
Stress Index ◽  
Drought Indices ◽  
Shallow Water Table

Abstract The reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on remote sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000–09 growing seasons. Spatial and temporal correlation analyses suggest that the ESI performs similarly to short-term (up to 6 months) precipitation-based indices but can be produced at higher spatial resolution and without requiring any precipitation data. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall: for example, in areas of intense irrigation or shallow water table. Normalization by PET serves to isolate the ET signal component responding to soil moisture variability from variations due to the radiation load. This study suggests that the ESI is a useful complement to the current suite of drought indicators, with particular added value in parts of the world where rainfall data are sparse or unreliable.

scholarly journals Development of a Multiple Linear Regression Model for Meteorological Drought Index Estimation Based on Landsat Satellite Imagery

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3393
Author(s):  
Seon Woo Kim ◽  
Donghwi Jung ◽  
Yun-Jae Choung
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Drought Index ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Meteorological Drought ◽  
Drought Indices ◽  
Index Estimation ◽  
Cumulative Precipitation

Climate polarization due to global warming has increased the intensity of drought in some regions, and the need for drought estimation studies to help minimize damage is increasing. In this study, we constructed remote sensing and climate data for Boryeong, Chungcheongnam-do, Korea, and developed a model for drought index estimation by classifying data characteristics and applying multiple linear regression analysis. The drought indices estimated in this study include four types of standardized precipitation indices (SPI1, SPI3, SPI6, and SPI9) used as meteorological drought indices and calculated through cumulative precipitation. We then applied statistical analysis to the developed model and assessed its ability as a drought index estimation tool using remote sensing data. Our results showed that its adj.R2 value, achieved using cumulative precipitation for one month, was very low (approximately 0.003), while for the SPI3, SPI6, and SPI9 models, the adj.R2 values were significantly higher than the other models at 0.67, 0.64, and 0.56, respectively, when the same data were used.

scholarly journals FORECASTING AND MONITORING AGRICULTURAL DROUGHT IN THE PHILIPPINES

2016 ◽  
Vol XLI-B8 ◽  
pp. 1263-1269
Author(s):  
G. J. Perez ◽  
M. Macapagal ◽  
R. Olivares ◽  
E. M. Macapagal ◽  
J. C. Comiso
Keyword(s):  
Land Surface ◽  
Drought Index ◽  
Vegetation Index ◽  
The Philippines ◽  
Agricultural Drought ◽  
Stress Index ◽  
Drought Indices ◽  
Drought Event ◽  
Ancillary Data ◽  
Vulnerability Maps

A monitoring and forecasting sytem is developed to assess the extent and severity of agricultural droughts in the Philippines at various spacial scales and across different time periods. Using Earth observation satellite data, drought index, hazard and vulnerability maps are created. The drought index, called Standardized Vegetation-Temperature Ratio (SVTR), is derived using the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). SVTR is evaluated by correlating its values with existing agricultural drought index, particulary Evaporative Stress Index (ESI). Moreover, the performance of SVTR in detecting drought occurrences was assessed for the 2015-2016 drought event. This period is a strong El Niño year and a large portion of the country was affected by drought at varying degrees, making it a good case study for evaluating drought indices. Satellitederived SVTR was validated through several field visits and surveys across different major agricultural areas in the country, and was found to be 73% accurate. The drought hazard and vulnerability maps are produced by utilizing the evapotranspration product of MODIS, rainfall climatology from the Tropical Rainfall Microwave Mission (TRMM) and ancillary data, including irrigation, water holding capacity and land use. Finally, we used statistical techniques to determine trends in NDVI and LST and generate a sixmonth forecast of drought index. Outputs of this study are being assessed by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) and the Department of Agriculture Bureau of Soils and Water Management (DABSWM) for future integration in their operations.

scholarly journals FORECASTING AND MONITORING AGRICULTURAL DROUGHT IN THE PHILIPPINES

2016 ◽  
Vol XLI-B8 ◽  
pp. 1263-1269 ◽  
Author(s):  
G. J. Perez ◽  
M. Macapagal ◽  
R. Olivares ◽  
E. M. Macapagal ◽  
J. C. Comiso
Keyword(s):  
Land Surface ◽  
Drought Index ◽  
Vegetation Index ◽  
The Philippines ◽  
Agricultural Drought ◽  
Stress Index ◽  
Drought Indices ◽  
Drought Event ◽  
Ancillary Data ◽  
Vulnerability Maps

A monitoring and forecasting sytem is developed to assess the extent and severity of agricultural droughts in the Philippines at various spacial scales and across different time periods. Using Earth observation satellite data, drought index, hazard and vulnerability maps are created. The drought index, called Standardized Vegetation-Temperature Ratio (SVTR), is derived using the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). SVTR is evaluated by correlating its values with existing agricultural drought index, particulary Evaporative Stress Index (ESI). Moreover, the performance of SVTR in detecting drought occurrences was assessed for the 2015-2016 drought event. This period is a strong El Niño year and a large portion of the country was affected by drought at varying degrees, making it a good case study for evaluating drought indices. Satellitederived SVTR was validated through several field visits and surveys across different major agricultural areas in the country, and was found to be 73% accurate. The drought hazard and vulnerability maps are produced by utilizing the evapotranspration product of MODIS, rainfall climatology from the Tropical Rainfall Microwave Mission (TRMM) and ancillary data, including irrigation, water holding capacity and land use. Finally, we used statistical techniques to determine trends in NDVI and LST and generate a sixmonth forecast of drought index. Outputs of this study are being assessed by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) and the Department of Agriculture Bureau of Soils and Water Management (DABSWM) for future integration in their operations.

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.

scholarly journals Drought Monitoring over Yellow River Basin from 2003–2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine

2021 ◽  
Vol 13 (18) ◽  
pp. 3748
Author(s):  
Xiaoyang Zhao ◽  
Haoming Xia ◽  
Li Pan ◽  
Hongquan Song ◽  
Wenhui Niu ◽  
...  
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Drought Index ◽  
Yellow River ◽  
Yellow River Basin ◽  
Condition Index ◽  
Drought Indices ◽  
The Yellow River ◽  
Drought Conditions ◽  
The Yellow River Basin

Drought is one of the most complex and least-understood environmental disasters that can trigger environmental, societal, and economic problems. To accurately assess the drought conditions in the Yellow River Basin, this study reconstructed the Land Surface Temperature (LST) using the Annual Temperature Cycle (ATC) model and the Normalized Difference Vegetation Index (NDVI). The Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature-Vegetation Drought Index (TVDI), which are four typical remote sensing drought indices, were calculated. Then, the air temperature, precipitation, and soil moisture data were used to evaluate the applicability of each drought index to different land types. Finally, this study characterized the spatial and temporal patterns of drought in the Yellow River Basin from 2003 to 2019. The results show that: (1) Using the LST reconstructed by the ATC model to calculate the drought index can effectively improve the accuracy of drought monitoring. In most areas, the reconstructed TCI, VHI, and TVDI are more reliable for monitoring drought conditions than the unreconstructed VCI. (2) The four drought indices (TCI, VCI, VH, TVDI) represent the same temporal and spatial patterns throughout the study area. However, in some small areas, the temporal and spatial patterns represented by different drought indices are different. (3) In the Yellow River Basin, the drought level is highest in the northwest and lowest in the southwest and southeast. The dry conditions in the Yellow River Basin were stable from 2003 to 2019. The results in this paper provide a basis for better understanding and evaluating the drought conditions in the Yellow River Basin and can guide water resources management, agricultural production, and ecological protection of this area.

scholarly journals Agricultural Drought Assessment in East Asia Using Satellite-Based Indices

2020 ◽  
Vol 12 (3) ◽  
pp. 444 ◽  
Author(s):  
Dong-Hyun Yoon ◽  
Won-Ho Nam ◽  
Hee-Jin Lee ◽  
Eun-Mi Hong ◽  
Song Feng ◽  
...  
Keyword(s):  
International Development ◽  
Land Surface ◽  
Joint Venture ◽  
The United States ◽  
Agricultural Drought ◽  
Stress Index ◽  
Drought Indices ◽  
Area Index ◽  
Drought Assessment ◽  
Wide Range

Drought is the meteorological phenomenon with the greatest impact on agriculture. Accordingly, drought forecasting is vital in lessening its associated negative impacts. Utilizing remote exploration in the agricultural sector allows for the collection of large amounts of quantitative data across a wide range of areas. In this study, we confirmed the applicability of drought assessment using the evaporative stress index (ESI) in major East Asian countries. The ESI is an indicator of agricultural drought that describes anomalies in actual/reference evapotranspiration (ET) ratios that are retrieved using remotely sensed inputs of land surface temperature (LST) and leaf area index (LAI). The ESI is available through SERVIR Global, a joint venture between the National Aeronautics and Space Administration (NASA) and the United States Agency for International Development (USAID). This study evaluated the performance of ESI in assessing drought events in South Korea. The evaluation of ESI is possible because of the availability of good statistical data. Comparing drought trends identified by ESI data from this study to actual drought conditions showed similar trends. Additionally, ESI reacted to the drought more quickly and with greater sensitivity than other drought indices. Our results confirmed that the ESI is advantageous for short and medium-term drought assessment compared to vegetation indices alone.

scholarly journals ASSESSING DROUGHT CHARACTERISTICS OF MELAKA BASIN USING JOINT METEOROLOGICAL AND STREAMFLOW INDICES

2020 ◽  
Vol 80 (1) ◽  
Author(s):  
Kee An Hong ◽  
Jer Lang Hong ◽  
Izihan Ibrahim
Keyword(s):  
Drought Index ◽  
Meteorological Drought ◽  
Hydrological Drought ◽  
Drought Indices ◽  
Severe Drought ◽  
Frequency Of Occurrence ◽  
Streamflow Data ◽  
Hydrological Droughts ◽  
Drought Occurrence ◽  
Meteorological Droughts

In this study, drought occurrence in the Melaka basin has been assessed using the meteorological and hydrological drought indices. A continuous rainfall and streamflow data of 40 years were used for drought analysis. Results show that in terms of meteorological drought index, the severe drought occurred in 1986-1988. The streamflow drought index indicates that the extreme drought occurred in 1982-1984. Further analysis based on seasonal precipitation and streamflow data shows that there is no drought for 79% of the time for the period 1960-2000 where there are hydrological records. For most of the dry and wet seasons, it is more likely that the frequency of occurrence of hydrological droughts only is higher than the frequency of occurrence of meteorological and hydrological droughts simultaneously or only meteorological droughts.

Flash Drought in CMIP5 Models

2021 ◽  
Author(s):  
David Hoffmann ◽  
Ailie J. E. Gallant ◽  
Mike Hobbins
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Climate Models ◽  
Standardized Precipitation Index ◽  
Rapid Onset ◽  
Stress Index ◽  
Cmip5 Models ◽  
Drought Indices ◽  
Similar Proportion ◽  
Evaporative Demand

Abstract‘Flash drought’ (FD) describes the rapid onset of drought on sub-seasonal times scales. It is of particular interest for agriculture as it can deplete soil moisture for crop growth in just a few weeks. To better understand the processes causing FD, we evaluate the importance of evaporative demand and precipitation by comparing three different drought indices that estimate this hazard using meteorological and hydrological parameters from the CMIP5 suite of models. We apply the Standardized Precipitation Index (SPI); the Evaporative Demand Drought Index (EDDI), derived from evaporative demand (E0); and the Evaporative Stress Index (ESI), which connects atmospheric and soil moisture conditions by measuring the ratio of actual and potential evaporation. The results show moderate-to-strong relationships (r2 > 0.5) between drought indices and upper level soil moisture on daily time scales, especially in drought-prone regions. We find that all indices are able to identify FD in the top 10-cm layer of soil moisture in a similar proportion to that in the models’ climatologies. However, there is significant inter-model spread in the characteristics of the FDs identified. This spread is mainly caused by an overestimation of E0, indicating stark differences in the land surface models and coupling in individual CMIP5 models. Of all indices, the SPI provides the highest skill in predicting FD prior to or at the time of onset in soil moisture, while both EDDI and ESI show significantly lower skill. The results highlight that the lack of precipitation is the main contributor to FDs in climate models, with E0 playing a secondary role.

scholarly journals Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

2019 ◽  
Vol 11 (23) ◽  
pp. 2742 ◽  
Author(s):  
Tran ◽  
Tran ◽  
Myint ◽  
Latorre-Carmona ◽  
Ho ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Drought Index ◽  
Negative Impact ◽  
Statistical Significance ◽  
Standardized Precipitation Index ◽  
Agricultural Drought ◽  
Series Data ◽  
Drought Indices ◽  
Drought Severity

Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001–2018 period, along with the Mann–Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.

scholarly journals Association between drought and agricultural productivity using remote sensing data: a case study of Gujarat state of India

2020 ◽  
Vol 11 (S1) ◽  
pp. 189-202 ◽  
Author(s):  
Koyel Sur ◽  
M. M. Lunagaria
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Standardized Precipitation Index ◽  
Remote Sensing Data ◽  
Meteorological Drought ◽  
Drought Indices ◽  
Small Scale ◽  
Agricultural Crop ◽  
Vegetation Condition ◽  
Spatio Temporal

Abstract Drought is a complex hazard which directly affects the water balance of any region. It impacts agricultural, ecological and socioeconomical spheres. It is a global concern. The occurrence of drought is triggered by climatic phenomena which cannot be eliminated. However, its effect can be well managed if actual spatio-temporal information related to crop status influenced by drought is available to decision-makers. This study attempted to assess the efficiency of remote sensing products from space sensors for monitoring the spatio-temporal status of meteorological drought in conjunction with impact on vegetation condition and crop yield. Time series (2000–2019) datasets of the Tropical Rainfall Measuring Mission (TRMM) were used to compute Standardized Precipitation Index (SPI) and MODIS (MODerate resolution Imaging Spectroradiometer) was used to compute Vegetation Condition Index (VCI). Association between SPI and VCI was explored. YAI was calculated from the statistical data records. Final observations are that the agricultural crop yield changed as per the climate variability specific to location. The study indicates drought indices derived from remote sensing give a synoptic view because of the course resolution of the satellite images. It does not reveal the precise relationship to the small-scale crop yield. Remote sensing can be an effective way to monitor and understand the dynamics of the drought and agriculture pattern over any region.

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