Assessment of meteorological and agricultural droughts using in-situ observations and remote sensing data

• Published in: Agricultural water management Vol. 222; pp. 125 - 138
• Main Authors: Zuo, Depeng, Cai, Siyang, Xu, Zongxue, Peng, Dingzhi, Kan, Guangyuan, Sun, Wenchao, Pang, Bo, Yang, Hong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2019
• Subjects: Agricultural drought; CDI; Northeast China; Remote sensing; SPEI; SPI; CDI; Northeast China; Agricultural drought; SPI; Remote sensing; SPEI
• ISSN: 0378-3774; 1873-2283
• DOI: 10.1016/j.agwat.2019.05.046

•CDI was estimated based on rainfall and NDVI for agricultural drought assessment.•Weight factors for CDI were identified by SPI and SPEI.•Agricultural drought assessment was verified by LST, ET and crop yield. Most research on drought assessment adopted historical in-situ observations, however, there has been increased data availability from remote sensing during the recent years. This study utilizes the two sources of data in drought assessment. Using the historical in-situ observations, the spatiotemporal variations of meteorological drought were firstly investigated by calculating the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) at 1, 3, 6-month time scales in Northeast China. Using remote sensing data, the combined deficit index (CDI) for agricultural drought assessment was computed based on tri-monthly sum of deficit in antecedent rainfall and deficit in monthly NDVI at land cover type and sub-type levels in the same region. In the end, the agricultural drought calculated by the CDI was evaluated against the deficit in crop yield, as well as deficit in Land Surface Temperature (LST) and Evapotranspiration (ET), in order to verify the applicability of the CDI for agricultural drought assessment in the study region. The results showed that the CDI has better correlations with the SPEI (R2 = 0.48) than the SPI (R2 = 0.05) at 3-month scales with weight factor a = 0.5 in dry farming areas. The spatial pattern of the CDI showed that the area of agricultural drought increased from July to October. In addition, a significant linear correlation was found between the CDI and anomaly in annual agricultural yield (R2 = 0.55), and anomaly in monthly land surface temperature (R2 = 0.42). The results prove that the CDI calculated by remote sensing data is not only a reliable indicator for agricultural drought assessment in Northeast China, but also provides useful information for agricultural drought disaster prevention and mitigation, and water management improvement.