Step A shows many input satellite, climate, and biophysical data layers. Satellite data types include MODIS Percent of Average Seasonal Greenness (PASG) and the Start of Season Anomaly (SOSA). Climate variables include the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Index (SPI). Five biophysical data layers provide the environmental and geographic setting for the climate and satellite vegetation conditions. They include land use/land cover type, irrigated agricultural land, soil available water capacity, elevation, and ecological region.
The PASG is an indicator of accumulated greenness (or NDVI) during the growing season compared to the historical average for the same point in time. The amount is expressed as a percent of the historical average. A PASG value of 100% means that the current seasonal greenness is equal to the average historical greenness and this is indicative of normal or average vegetation conditions. PASG values less than 100% indicate below average greenness (poorer than normal vegetation conditions) that may be linked to some form of stress (for example, drought, flooding, fire, hail damage, or pest infestation). PASG values greater than 100% indicate higher than average greenness, which reflect above normal vegetation conditions. PASG values are not calculated for a location until the start of season has occurred.
The SOSA is a measure of the temporal difference in the start of the growing season (SOS) for a given year compared to the historical average SOS for a location. SOS can be defined as the time when vegetation initiates growth (that is, photosynthetic activity) after winter as observed from satellite observations. A negative SOSA indicates that the SOS for a specific year is earlier than the average date and a positive SOSA appears when green-up occurs later than normal. The SOSA is used to express the changes in vegetation phenology that may occur from year-to-year.
Two climate variables in VegDRI indicate dry conditions in a meteorological sense, the PDSI and the SPI. The SPI was designed to quantify precipitation differences over multiple time intervals (for example, 1- to 12-month periods) based on fitting a long-term precipitation record at a given location over a specified interval to a probability distribution, which is then transformed into a gamma distribution such that the mean SPI value for that location and time period is zero. SPI values are positive if the precipitation over a specific time period is higher than the historical average precipitation over that same period and negative if precipitation is less than the historical mean.
The PDSI is a prominent drought index that has been widely used to assess agricultural drought. The PDSI is calculated using a simple supply-and-demand water balance model that integrates precipitation and temperature data, as well as the available water holding capacity of the soil. A self-calibrated PDSI calculation is used in VegDRI. This version of PDSI utilizes a mathematical calibration of model coefficients tuned to local climate and soil characteristics. This essentially improves the spatial comparability of PDSI values and calibrates the index so that extreme dry and wet events have a comparable rate of occurrence across the nation. The PDSI is used in VegDRI models as the dependent variable.
The biophysical data layers help to differentiate drought stress by giving information on environmental and geographic context. The land use/land cover (LULC) type represents broad categories of vegetation with various seasonal cycles and climate-vegetation responses. An irrigated agriculture variable differentiates irrigated lands, which are less susceptible to drought stress because of targeted water applications, from rainfed agriculture. The available water capacity (AWC) variable represents the potential of the soil to hold moisture that is available to plants, which in turn influences the sensitivity of vegetation to drought stress. Elevation data provides different gradients that change the typical seasonality of vegetation. Ecological regions (that is, ecoregions) give a wall-to-wall geographic framework that accounts for the wide variability in environmental conditions encountered across the country that can influence sensitivity and responsiveness of vegetation to drought stress. Ecoregions typically have similar ecosystems and environmental resources defined using both abiotic (for example, physiography) and biotic (for example, plant species) criteria.