Monitoring drought using multi-sensor remote sensing data in cropland of Gansu Province

Various drought monitoring models have been developed from different perspectives, as drought is impacted by various factors (precipitation, evaporation, runoff) and usually reflected in various aspects (vegetation condition, temperature). Cloud not only plays an important role in the earth's energy balance and climate change, but also directly impacts the regional precipitation and evaporation. As a result, the change of cloud cover and cloud type can be used to monitor drought. This paper proposes a new drought composite index, the Drought Composite Index (DCI), for drought monitoring based on multi-sensor remote sensing data in cropland of Gansu Province. This index combines the cloud classification data (CLS) from FY satellite and Vegetation Condition Index (VCI) which was calculated using the maximum and minimum NDVI values for the same time period from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Pearson correlation was performed to correlate NDVI, VCI, CLS and DCI values to precipitation data and soil moisture (SM) data collected from 20 meteorological stations during the growing season of 2011 and 2012. Better agreement was observed between DCI and precipitation as compared with that between NDVI/VCI and precipitation, especially the one-month precipitation, and there is an obvious time lag in the response of vegetation to precipitation. In addition, the results indicated that DCI well reflected precipitation fluctuations in the study area promising a possibility for early drought awareness necessary and near real-time drought monitoring.