Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
Department of Environmental Sciences, University of Virginia, P.O. Box 400123, Charlottesville, USA.
Sci Total Environ. 2018 Jun 1;625:1208-1217. doi: 10.1016/j.scitotenv.2017.12.268. Epub 2018 Jan 12.
Normalized Difference Vegetation Index (NDVI) has been extensively used in continuous and long-term drought monitoring over large-scale, but with late response to drought-related changes of photosynthesis. Instead, solar-induced chlorophyll fluorescence (SIF) is more closely related to photosynthesis and thus is proposed to track the impacts of drought on vegetation growth. However, the detailed difference between SIF and NDVI in responding to drought has not been thoroughly explored. Here we present continuous ground measurements of NDVI and SIF at 760nm over four plots of wheat with different intensities of drought (well-watered treatment, moderate drought, severe drought and extreme drought). The average values of seasonal SIF were significantly lower under severe drought and extreme drought, while NDVI means only showed significant reduction in extreme drought. In the seasonal patterns, daily SIF could clearly separate the difference of drought gradient, while the difference of daily NDVI was clearer in the end of the field campaign. Daily SIF also significantly and positively correlated with soil moisture, indicating that SIF could be considered as an estimator of soil moisture to detect the information about agricultural drought. Furthermore, in extreme drought plot, the correlation of SIF and soil moisture was higher than that of NDVI and soil moisture in a shorter time lag (<15-day) but weaker in a longer time lag (longer than 30-day). The relationships of growth parameters with SIF and NDVI were further analyzed, showing a saturation of NDVI and unsaturation of SIF at high values of leaf area index and relative water content. These results suggested that SIF is better fit in early drought monitoring, especially over closure canopy, while NDVI is more feasible when drought lasted over a long time scale. Our findings in the study might provide deep insight into the utility of SIF in drought monitoring.
归一化差异植被指数（NDVI）已广泛用于大规模、连续和长期的干旱监测，但对与光合作用相关的干旱变化的响应较慢。相比之下，太阳诱导叶绿素荧光（SIF）与光合作用更为密切相关，因此被提议用于跟踪干旱对植被生长的影响。然而，SIF 和 NDVI 对干旱的响应之间的详细差异尚未得到彻底探索。在这里，我们在四个不同干旱程度（充分浇水处理、中度干旱、严重干旱和极端干旱）的小麦试验区进行了连续的地面 760nm 波段 NDVI 和 SIF 测量。在严重干旱和极端干旱条件下，SIF 的季节平均值显著降低，而 NDVI 均值仅在极端干旱条件下显示出显著降低。在季节模式中，SIF 可以清楚地分离出干旱梯度的差异，而 NDVI 的日变化差异在野外考察的后期更为明显。SIF 与土壤湿度也呈显著正相关，这表明 SIF 可以作为土壤湿度的估计值，用于探测农业干旱信息。此外，在极端干旱试验区，SIF 与土壤湿度的相关性在较短的时间滞后（<15 天）内高于 NDVI 与土壤湿度的相关性，但在较长的时间滞后（超过 30 天）内较弱。进一步分析了生长参数与 SIF 和 NDVI 的关系，表明在叶面积指数和相对水含量较高时，NDVI 趋于饱和而 SIF 不饱和。这些结果表明，SIF 更适合早期干旱监测，特别是在冠层闭合的情况下，而 NDVI 在干旱持续时间较长的情况下更可行。本研究中的发现可能为 SIF 在干旱监测中的应用提供了深入的见解。
