Abstract
The use of remote sensing for monitoring and managing droughts is examined in this review study. Drought has a significant impact on how water resources are managed and agricultural production is produced, and remote sensing is a vital technique for assessing and monitoring the severity of drought. A number of remote sensing data sources are discussed in the paper; including precipitation, groundwater and surface water storage, soil moisture, land surface temperature, evaporation, and agricultural indicators. With the use of these data sources, drought indices and indicators that measure the severity and spatiotemporal fluctuations of the drought may be developed. The novel approach of this review study emphasizes the benefits of using remote sensing to gain a full understanding of drought dynamics and to accurately capture fine-scale fluctuations in drought conditions. However, the study also highlights certain limitations, including issues related to data accessibility, data interpretation, and validation difficulties. It emphasizes the significance of using remote sensing to promote the developing policies and strategies to enhance drought resilience and adaptation. The importance of continuous research, technical development, and stakeholder cooperation in order to fully realize remote sensing's promise for tackling the complex problems associated with drought and promoting sustainable water resource management.
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Abbreviations
- (SPI):
-
Standardized Precipitation Index
- (CMI):
-
Crop Moisture Index
- (NDVI):
-
Normalized Difference Vegetation Index
- (SAR):
-
Synthetic Aperture Radar
- (VTCI):
-
Vegetation Temperature Condition Index
- (MODIS):
-
Moderate-resolution Imaging Spectro- radiometer
- (SWIR):
-
Shortwave-Infrared Reflectance’s
- (AVIRIS):
-
Airborne Visible/Infrared Imaging Spectrometer
- (ALS):
-
Amyotrophic Lateral Sclerosis
- (EVI):
-
Enhanced Vegetation Index
- (DPAI):
-
Dimensionless Precipitation Anomaly Index
- (GIS):
-
Geospatial Information System
- (PCA):
-
Pincipal Component Analysis
- (GPM):
-
Global Precipitation Measurement
- (PERSIANN):
-
Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks
- (CDR):
-
Climate Data Record
- (DSI):
-
Drought Severity Index
- (GIIDI):
-
Geographically Independent Integrated Drought Index
- (SRE):
-
Satellite Rainfall Estimate
- (GSPEI):
-
Gridded Standardized Precipitation Evapotranspiration Index
- (OLI):
-
Operational Land Imager
- (TVDI):
-
Temperature Vegetation Drought Index
- (ATC):
-
Annual Temperature Cycle
- (VHI):
-
Vegetation Health Index
- (SMCI):
-
Soil Moisture Condition Index
- (MIDI):
-
Microwave Integrated Drought Index
- (SDCI):
-
Scale Drought Conditions Index
- (DFNN):
-
Deep Forwarded Neural Network
- (CDMI):
-
Combined Drought Monitoring Index
- (CDMI):
-
Comprehensive Drought Monitoring Index
- (NMDM):
-
National Meteorological Drought Monitoring
- (MLR):
-
Multiple Linear Regression
- (AVHRR):
-
Advanced Very High Resolution Radiometer
- (UAVs):
-
Unmanned Aerial Vehicles
- (PDSI):
-
Palmer Drought Severity Index
- (VCI):
-
Vegetation Condition Index
- (LST):
-
Land Surface Temperature
- (LiDAR):
-
Light Detection and Ranging
- (EOS):
-
Earth Observing System
- (ASTER):
-
Advanced Spaceborne Thermal Emission Reflection Radiometer
- (NIR):
-
Near-Infrared
- (ICESat-2):
-
Ice, Cloud and Land Elevation Satellite-2
- (SMAP):
-
Soil Moisture Active Passive
- (SPEI):
-
Standardized Precipitation Evapotranspiration Index
- (ESI):
-
Evaporative Stress Index
- (SVM):
-
Support Vector Machines
- (t-SNE):
-
T-Distributed Stochastic Neighbor Embedding
- (TRMM):
-
Tropical Rainfall Measuring Mission
- (CHIRPS) :
-
Climate Hazards Group Infrared Precipitation with Station
- (SPE):
-
Satellite Precipitation Estimate
- (MSDI):
-
Multivariate Standardized Drought Index
- (CDI_M):
-
Combined Drought Indicator for Marathwada
- (SAF):
-
Severity-Areal-Frequency
- (GRACE):
-
Gravity Recovery and Climate Experiment
- (SLSTR):
-
Land Surface Temperature Radiometer
- (RSDAST):
-
Remotely Sensed Daily land Surface Temperature reconstruction
- (TCI):
-
Temperament and character inventory
- (PCI):
-
Precipitation Condition Index
- (OVDI):
-
Optimized Vegetation Drought Index
- (OMDI):
-
Optimized Meteorological Drought Index
- (SDI):
-
Synthesized Drought Index
- (SMDI):
-
Soil Moisture Deficit Index
- (MCDI):
-
Membrane Capacitive Deionization
- (TWS):
-
Terrestrial Water Storage
- (XGBoost):
-
Extreme Gradient Boosting
- (RF):
-
Random Forest
- (GEO):
-
Group on Earth Observations
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Conceptualization, (Vijendra Kumar); methodology, (Vijendra Kumar and Kul Vaibhav Sharma); Investigation, (Ayush Kumar Srivastava and Chandra Bogireddy); resources, (Quoc Bao Pham and S M Yadav) writing—original draft preparation, (Vijendra Kumar and Kul Vaibhav Sharma); writing—review and editing, (Quoc Bao Pham); supervision, (Quoc Bao Pham and S M Yadav); project administration, (Vijendra Kumar and Kul Vaibhav Sharma).
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Kumar, V., Sharma, K.V., Pham, Q.B. et al. Advancements in drought using remote sensing: assessing progress, overcoming challenges, and exploring future opportunities. Theor Appl Climatol (2024). https://doi.org/10.1007/s00704-024-04914-w
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DOI: https://doi.org/10.1007/s00704-024-04914-w