# Drought: A Common Environmental Disaster
## 4 Remote Sensing and Drought Monitoring
Drought information can be provided with high geographical and temporal precision using new systems that combine data from many sensors and platforms, or multi-sensors from satellite constellations [75; 76], to overcome this spatiotemporal divide. MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (originally known as EOS AM-1) and Aqua (originally known as EOS PM-1) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon [77]. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data improve our understanding of global dynamics and processes occurring on the land, in the oceans and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global and interactive Earth system models that are able to predict global change accurately enough to assist policymakers in making sound decisions concerning the protection of our environment [77; 78; 79]. Drought can be monitored using contemporary remote-sensing techniques or conventional climatic drought indices [80]. Drought monitoring must be efficient and timely to prevent the effects of global warming on drought [81]. Environmental variables, such as rainfall and temperature, help to solve the problem of erroneous data in spare stations [81; 82]. The NDVI and Monthly Precipitation Anomaly Percentage (PA) have traditionally been used to monitor drought [82; 83; 26]. However, because the drought event does not coincide with changes in vegetation greenness, there is sometimes a lag between real drought incidence and vegetation indicators. VCI-MODIS is a new approach that combines the use of land surface temperature (LST) and NDVI [84]. The Vegetation Supply Water Index (VSWI) was proposed by [PERSON] et al. [85] and is based on dividing the NDVI and LST. The TVDI, a triangular space index based on the NDVI and LST, was also introduced by [PERSON] et al. [86]. These indexes were extensively used for drought monitoring in recent years, and they combine the benefits of LST and NDVI [87]. The TVDI, on the other hand, accurately representsthe drought condition because it is based on data [86]. Various indices are available for drought monitoring and there is no one-size fit all approach; it depends on the drought in question, focus of the work, choice of dataset and scope. In locations with a limited number of sampling gauges or that are inaccessible, remote-sensing data may be the only available information sources for drought monitoring [88]. Drought indices based on satellite data, such as the normalized difference vegetation index (NDVI)-based vegetation condition index (VCI) [89], have been widely used to detect the onset of drought and to measure the intensity, duration and impact of drought around the world [90]. VCI has the apparent advantage of being simple to compute because it does not require station observation data, and it can offer near real-time data around the world at a relatively high spatial resolution as a satellite-based drought product [91]. The utility of the NDVI-derived VCI for monitoring drought conditions has been studied in various parts of the world, and it is strongly correlated with agricultural production in South America, Africa, Asia, North America and Europe, especially during critical crop growth periods [92]. In Kazakhstan, the VCI based on NDVI was compared to vegetation density, biomass and field reflectance data; it was shown to be a good predictor of the impact of weather on vegetation conditions and health [93]. Other research, on the other hand, has indicated that the NDVI-derived VCI should be utilised with caution.