作者:Wang, SH (Wang, Siheng)[ 1,2 ] ; Huang, CP (Huang, Changping)[ 1 ] ; Zhang, LF (Zhang, Lifu)[ 1 ] ; Lin, Y (Lin, Yi)[ 3 ] ; Cen, Y (Cen, Yi)[ 1 ] ; Wu, TX (Wu, Taixia)[ 1 ]

REMOTE SENSING

卷: 8  期: 2

文献号: 61

DOI: 10.3390/rs8020061

出版年: FEB 2016

摘要

We examined the relationship between satellite measurements of solar-induced chlorophyll fluorescence (SIF) and several meteorological drought indices, including the multi-time-scale standard precipitation index (SPI) and the Palmer drought severity index (PDSI), to evaluate the potential of using SIF to monitor and assess drought. We found significant positive relationships between SIF and drought indices during the growing season (from June to September). SIF was found to be more sensitive to short-term SPIs (one or two months) and less sensitive to long-term SPI (three months) than were the normalized difference vegetation index (NDVI) or the normalized difference water index (NDWI). Significant correlations were found between SIF and PDSI during the growing season for the Great Plains. We found good consistency between SIF and flux-estimated gross primary production (GPP) for the years studied, and synchronous declines of SIF and GPP in an extreme drought year (2012). We used SIF to monitor and assess the drought that occurred in the Great Plains during the summer of 2012, and found that although a meteorological drought was experienced throughout the Great Plains from June to September, the western area experienced more agricultural drought than the eastern area. Meanwhile, SIF declined more significantly than NDVI during the peak growing season. Yet for senescence, during which time the reduction of NDVI still went on, the reduction of SIF was eased. Our work provides an alternative to traditional reflectance-based vegetation or drought indices for monitoring and assessing agricultural drought.

通讯作者地址: Huang, CP (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

[ 3 ] Peking Univ, Inst Remote Sensing & Geog Informat Syst, Sch Earth & Space Sci, Beijing 100871, Peoples R China