Early drought warning based on chlorophyll fluorescence and normalized difference vegetation index in Xilingol League of China

Abstract

Drought is one of the most serious natural disasters affecting global food security and human life. Traditional drought monitoring is based on the impact of water deficit on crop morphology. Due to the time lag of traditional drought monitoring based on vegetation index and land surface temperature, crop yield has been adversely affected when drought detection is delayed. Early drought warning is an effective way to prevent disasters and reduce damage. An early warning model, using the percentage of anomaly vegetation and fluorescence (PAVF) was proposed to determine drought risk level and development trends. The principle of PAVF is to analyze the change in the percentage of anomaly vegetation (PAV) using the normalized difference vegetation index (NDVI), from the moderate-resolution imaging spectroradiometer sensor, and percentage anomaly fluorescence (PAF), from the Global Ozone Monitoring Experiment. A standard score of seasonal cumulative NDVI and Sun-induced chlorophyll fluorescence based on the data from 2007 to 2017 was used as a proxy for the evaluation index. Because PAV and PAF have different sensitivities to monitoring vegetation physiological changes, these parameters can reflect the occurrence and development trend of drought from different perspectives. Drought risk analysis in Xilingol League of China in 2018 demonstrated that the drought warning model could depict the evolution process of agricultural drought and can efficiently predict the occurrence of drought in the next half month. The proposed PAVF provides an available and innovative way to address the complexity of agricultural drought warnings.