A multitemporal and non-parametric approach for assessing the impacts of drought on vegetation greenness: A case study for Latin America

This study evaluates the relationship between the frequency and duration of meteorological droughts and the subsequent temporal changes on the quantity of actively photosynthesizing biomass (greenness) estimated from satellite imagery on rainfed croplands in Latin America. An innovative non-parametric and non-supervised approach, based on the Fisher-Jenks optimal classification algorithm, is used to identify multi-scale meteorological droughts on the basis of empirical cumulative distributions of 1, 3, 6, and 12-monthly precipitation totals. As input data for the classifier, we use the gridded GPCC Full Data Reanalysis precipitation time-series product, which ranges from January 1901 to December 2010 and is interpolated at the spatial resolution of 1 degree (decimal degree, DD). Vegetation greenness composites are derived from 10-daily SPOT-VEGETATION images at the spatial resolution of 1/112 degree DD for the period between 1998 and 2010. The time-series analysis of vegetation greenness is performed during the growing season with a non-parametric method, namely the seasonal Relative Greenness (RG) of spatially accumulated fAPAR. The Global Land Cover map of 2000 and the GlobCover maps of 2005/2006 and 2009 are used as reference data to select study cases only on geographic areas that did not undergo land cover changes during the analysis period. The multi-scale information is integrated at the lowest spatial resolution available, i.e. 1 degree DD, and the impacts of meteorological drought episodes on seasonal greenness of rainfed crops are assessed at the regional scale. Final results suggest that the agricultural cycle at the regional scale is more correlated with long-standing and uninterrupted small timescale drought conditions that occur prior to vegetation growing season than with isolated and short long-term timescale drought events.

本研究针对拉丁美洲雨养农田，探究气象干旱的发生频次、持续时长与基于卫星影像估算的活跃光合生物量（绿度，greenness）随时间的后续变化之间的关联。本研究采用一种基于Fisher-Jenks最优分类算法的创新非参数无监督方法，通过1、3、6、12个月降水总量的经验累积分布识别多尺度气象干旱事件；本研究所用分类器的输入数据为网格化GPCC全数据再分析降水时序产品，其时间跨度为1901年1月至2010年12月，空间插值分辨率为1度（十进制度，DD）。植被绿度合成数据源自1998年至2010年的10日合成SPOT-VEGETATION影像，空间分辨率为1/112度（十进制度，DD）；植被绿度时序分析在植被生长季内开展，采用非参数方法，即空间累积光合有效辐射吸收比率（fAPAR）的季节相对绿度（RG）。本研究以2000年全球土地覆盖图、2005/2006年及2009年的GlobCover土地覆盖图作为参考数据，仅选取分析时段内未发生土地覆盖变化的地理区域作为研究样区；将多尺度信息整合至当前可用的最低空间分辨率，即1度（十进制度，DD），并在区域尺度上评估气象干旱事件对雨养作物季节绿度的影响。最终结果表明，区域尺度上的农业周期与植被生长季前出现的持续无间断短时尺度干旱条件相关性更高，而非孤立且持续时间较短的长时尺度干旱事件。