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Moisture Stress Index (MSI)
Moisture Stress Index (MSI) is a simple water ratio index for the estimation of leaf relative water content (%) and equivalent water thickness (EWT, g cm–2) of different plant species (Hunt and Rock 1989). It is calculated as R1600 nm/R820 nm. In this study, the SWIR band as there is strong water absorption bands at SWIR spectrum are more sensitive to moisture variation than other optical spectral regions. The weak water absorption at NIR band makes it less sensitive to water variation thus the ratio between SWIR and NIR bands can effectively reduce the scattering effect of the single band and highlight the water variation in vegetation leaves."
Data Source: Visible Infrared Imaging Radiometer Suite (VIIRS) dataset available in Google Earth Engine (GEE)
| Spatial Resolution: | 500m |
| Temporal Resolution | 8 day |
| Value Range: | 0 to 3+ (Nodata: -9999) |
Atmospherically Resistant Vegetation Index (ARVI)
The Atmospherically Resistant Vegetation Index (ARVI) was originally designed for use with MODIS. It is an enhancement to the NDVI that is relatively resistant to atmospheric factors (for example, aerosol). It uses blue reflectance to correct red reflectance for atmospheric scattering. It is most useful in regions of high atmospheric aerosol content, including tropical regions contaminated by soot from slash-and-burn agriculture.
Data Source: Visible Infrared Imaging Radiometer Suite (VIIRS) dataset available in Google Earth Engine (GEE)
| Spatial Resolution: | 500m |
| Temporal Resolution | 8 day |
| Value Range: | -1 to +1 (Nodata: -9999) |
Visible and Shortwave infrared Drought Index (VSDI)
Visible and Shortwave infrared Drought Index (VSDI) developed by Zhang et al. 2013. VSDI is based on the combination of optical spectral bands located in Blue, Red, and Shortwave infrared (SWIR) regions. It shows potential advantages for monitoring both soil and vegetation moisture and for drought monitoring throughout plant growing seasons, which distinguish it from other drought indices that were either designed for vegetation water content estimation or confined to soil moisture monitoring.
Data Source: Visible Infrared Imaging Radiometer Suite (VIIRS) dataset available in Google Earth Engine (GEE)
| Spatial Resolution: | 500m |
| Temporal Resolution | 8 day |
| Value Range: | 0 to 2/3 (Nodata: -9999) |
Soil Adjusted Vegetation Index (SAVI)
The Soil Adjusted Vegetation Index (SAVI) was proposed by HUETE (1988).It is aimed at minimizing the soil influence on vegetation quantification by introducing the soil adjustment factor L. This index is best used in areas with relatively sparse vegetation where soil is visible through the canopy. For high vegetation cover the value of L is 0.0 (or 0.25), and for low vegetation cover 1.0. For intermediate vegetation cover L = 0.5, and this value is used most widely.
Data Source: Visible Infrared Imaging Radiometer Suite (VIIRS) dataset available in Google Earth Engine (GEE)
| Spatial Resolution: | 500m |
| Temporal Resolution | 8 day |
| Value Range: | -1 to +1 (Nodata: -9999) |
Enhanced Vegetation Index (EVI)
As the name suggests, Enhanced Vegetation Index (EVI) is an optimized index over the standard NDVI. The EVI was specifically developed to be more sensitive to changes in areas having high biomass (a serious shortcoming of NDVI), reduce the influence of atmospheric conditions on vegetation index values, and correct for canopy background signals. EVI tends to be more sensitive to plant canopy differences like leaf area index (LAI), canopy structure, and plant phenology and stress than does NDVI which generally responds just to the amount of chlorophyll present.
Data Source: Visible Infrared Imaging Radiometer Suite (VIIRS) dataset available in Google Earth Engine (GEE)
| Spatial Resolution: | 500m |
| Temporal Resolution | 8 day |
| Value Range: | 0 to +1 (Nodata: -9999) |
Normalized Difference Vegetation Index (NDVI)
Normalized Difference Vegetation Index (NDVI) was developed from work done by Tarpley et al. and Kogan with the National Oceanic and Atmospheric Administration (NOAA) in the United States. Uses the global vegetation index data, which are produced by mapping 4 km daily radiance. Radiance values measured in both the visible and near-infrared channels are used to calculate NDVI. It measures greenness and vigor of vegetation over a seven-day period as a way of reducing cloud contamination and can identify drought-related stress to vegetation.
Data Source: Visible Infrared Imaging Radiometer Suite (VIIRS) dataset available in Google Earth Engine (GEE)
| Spatial Resolution: | 500m |
| Temporal Resolution | 8 day |
| Value Range: | -1 to +1 (Nodata: -9999) |
Normalized Difference Vegetation Index (NDVI) Anomaly
Monthly anomaly is calculated based on the 2012-2019 period for each month. 2015 and 2016 are discarded from the climatology calculation as those are severe drought years.
Data Source: Visible Infrared Imaging Radiometer Suite (VIIRS) dataset available in Google Earth Engine (GEE)
| Spatial Resolution: | 500m |
| Temporal Resolution | 8 day |
| Value Range: | -2 to +2 (Nodata: -9999) |
Keetch-Byram Drought Index (KBDI)
The Keetch-Byram drought index (KBDI) is a continuous reference scale for estimating the dryness of the soil and duff layers. The index increases for each day without rain (the amount of increase depends on the daily high temperature) and decreases when it rains. The scale ranges from 0 (no moisture deficit) to 800. The range of the index is determined by assuming that there is 8 inches of moisture in a saturated soil that is readily available to the vegetation. High values of the KBDI are an indication that conditions are favorable for the occurrence and spread of wildfires, but drought is not by itself a prerequisite for wildfires. Other weather factors, such as wind, temperature, relative humidity and atmospheric stability, play a major role in determining the actual fire danger.
Data Source: Google Earth Engine (GEE)
| Spatial Resolution: | 4000m |
| Temporal Resolution | 1 day |
| Value Range: | 0 to +800 (Nodata: -9999) |
Combined Drought Index (CDI)
This index is calculated using SPI3, the anomalies of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and soil suction (pF) . Four warning categories are available as Watch: Precipitation deficit, Warning: Soil moisture deficit, Alert 1: Vegetation stress following precipitation deficit, Alert 2: Vegetation stress following precipitation/soil moisture deficit. Acknowledge reference:https://goo.gl/1dBSVe)
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: | 0 to +800 (Nodata: -9999) |
Standardized Precipitation Index (1 month)
Reflects short-term wet and dry conditions.
Application: Short-term soil moisture and crop stress (especially during the growing season)
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: | -3 to +3 |
Standardized Precipitation Index (3 month)
Reflects short- and medium-term moisture conditions Application: A seasonal estimation of precipitation
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: | -3 to +3 |
Standardized Runoff Index (1 month)
Shows short-term potential to complement existing climate indices and local hydro-climatological information.
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: | -3 to +3 |
Standardized Runoff Index (3 month)
Shows short- and medium-term potential to complement existing climate indices and local hydro-climatological information.
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: | -3 to +3 |
Soil Moisture Deficit Index (SMDI)
Can be used as an indicator of short-term drought. Useful for identifying and monitoring drought affecting agriculture.
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: | -3 to +3 |
Drought Severity
Agricultural drought severity is derived from the root zone soil moisture expressed as a percentile of the 1981-2010 climatology. Low percentile values show low drought severity.
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: | 0 to 100 |
Dry Spell Events
Dry Spell Events show dry days lasting at least 14 days.
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: |
Root Zone Soil Moisture (RZSM)
Shows soil moisture content [mm] at surface and root zone level.
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: |
Soil Moisture
Shows soil total moisture content [mm] in 0-10 cm soil layer.
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: |
Soil Temperature
Shows soil temperature [C] in 0-10 cm soil layer.
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: |
Potential Evapotranspiration (PET)
Shows the amount of evapotranspiration (in mm) by a large vegetation of natural green crops calculated from the VIC land surface model.
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: |
Evapotranspiration
Total net evaporation [mm] shows the sum of evaporation from bare soil, canopy interception and plant transpiration calculated from the VIC land surface model.
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: |
Baseflow
Shows the portion of streamflow that comes from the sum of deep subsurface flow and delayed shallow subsurface flow (mm/day).
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: |
Surface Runoff
Excess water from rain, snow melt or other sources that does not infiltrate due to soil saturation or high intensity but instead flows overland (in mm).
Data Source: CHIRPS, CHIRP, GPM-IMERG, NCEP, NMME, SMAP, SMOS, MODIS through Regional Hydrological Extreme Assessment (RHEAS) model
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: |
Vegetation Health Index (VHI)
The Vegetation Health index (VHI) is based on a combination of products extracted from vegetation signals, namely the Normalized Difference Vegetation Index (NDVI) and from the brightness temperatures, both derived from earth observation sensors. VH users rely on a strong inverse correlation between NDVI and land surface temperature, since increasing land temperatures are assumed to act negatively on vegetation vigour and consequently to cause stress.
Data Source: MODIS (Moderate Resolution Imaging Spectroradiometer) dataset available in Google Earth Engine (GEE)
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: | 0 to 100 (Nodata: -9999) |
Vegetation Condition Index (VCI)
The Vegetation Condition Index (VCI) compares the current NDVI to the range of values observed in the same period in previous years. The VCI is expressed in % and gives an idea where the observed value is situated between the extreme values (minimum and maximum) in previous years. Lower and higher values indicate bad and good vegetation state conditions, respectively. VCI varies from 0 for extremely unfavorable conditions, to 100 for optimal.
Data Source: MODIS (Moderate Resolution Imaging Spectroradiometer) dataset available in Google Earth Engine (GEE)
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: | 0 to 100 (Nodata: -9999) |
Temperature Condition Index (TCI)
Temperature Condition Index (TCI) is used to determine stress on vegetation caused by temperatures and excessive wetness. Conditions are estimated relative to the maximum and minimum temperatures and modified to reflect different vegetation responses to temperature. TCI varies from 0, for extremely unfavorable conditions to 100 for optimal conditions.
Data Source: MODIS (Moderate Resolution Imaging Spectroradiometer) dataset available in Google Earth Engine (GEE)
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: | 0 to 100 (Nodata: -9999) |
Evaporative Stress Index (ESI)
The Evaporative Stress Index (ESI) describes temporal anomalies in evapotranspiration (ET), highlighting areas with anomalously high or low rates of water use across the land surface. Here, ET is retrieved via energy balance using remotely sensed land-surface temperature (LST) time-change signals. LST is a fast-response variable, providing proxy information regarding rapidly evolving surface soil moisture and crop stress conditions at relatively high spatial resolution. The ESI also demonstrates capability for capturing early signals of “flash drought," brought on by extended periods of hot, dry, and windy conditions leading to rapid soil moisture depletion.
Data Source: MODIS (Moderate Resolution Imaging Spectroradiometer) dataset available in Google Earth Engine (GEE)
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: | 0 to 100 (Nodata: -9999) |
Crop Water Stress Index (CWSI)
The crop water stress index (CWSI) is developed as a normalized index to quantify stress and overcome the effects of other environmental parameters affecting the relationship between stress and plant temperature. This index has been widely used for crop water status monitoring.
Data Source: MODIS (Moderate Resolution Imaging Spectroradiometer) dataset available in Google Earth Engine (GEE)
| Spatial Resolution: | 5000m |
| Temporal Resolution | 8 day |
| Value Range: | 0 to 100 (Nodata: -9999) |