What are the most effective ways to use remote sensing data to monitor drought?

1 Drought indices

One of the most common ways to use remote sensing data to monitor drought is to calculate drought indices. Drought indices are numerical values that represent the severity and duration of drought based on different indicators. For example, the Normalized Difference Vegetation Index (NDVI) measures the greenness of vegetation, which reflects its photosynthetic activity and water stress. The Standardized Precipitation Index (SPI) measures the deviation of precipitation from the historical average, which indicates the meteorological drought. The Evaporative Stress Index (ESI) measures the ratio of actual to potential evapotranspiration, which indicates the soil moisture and agricultural drought. You can use GIS software to calculate and map these indices from remote sensing data, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Global Precipitation Measurement (GPM).

2 Drought monitoring systems

Another way to use remote sensing data to monitor drought is to access drought monitoring systems that provide near-real-time information and products based on remote sensing and other data sources. Drought monitoring systems are online platforms that integrate and analyze multiple drought indicators and provide maps, charts, reports, and alerts for different regions and scales. For example, the U.S. Drought Monitor (USDM) is a weekly map that shows the extent and intensity of drought across the United States, based on a combination of remote sensing, climatic, hydrologic, and soil data. The Global Drought Information System (GDIS) is a web portal that provides global and regional drought maps and products based on remote sensing and model data. You can use GIS software to download and display these products, and to compare them with your own data and analysis.

3 Drought modeling and forecasting

A third way to use remote sensing data to monitor drought is to use them as inputs or validations for drought modeling and forecasting. Drought modeling and forecasting are methods that use mathematical equations and algorithms to simulate and predict the occurrence and evolution of drought based on different factors and scenarios. For example, the Palmer Drought Severity Index (PDSI) is a model that estimates the soil moisture balance based on precipitation and temperature data. The Seasonal Drought Outlook (SDO) is a forecast that projects the likelihood of drought improvement, persistence, or development for the next three months based on climate models and observations. You can use GIS software to run and visualize these models and forecasts, and to assess their accuracy and uncertainty using remote sensing data.