In the summer of 2012, the US Midwest, the most productive agricultural region in the world, experienced the most intense and widespread drought on record for the past hundred years. The 2012 drought, characterized as...In the summer of 2012, the US Midwest, the most productive agricultural region in the world, experienced the most intense and widespread drought on record for the past hundred years. The 2012 drought, characterized as ‘flash drought’, developed in May with a rapid intensification afterwards, and peaked in mid-July. ~76% of crop region and 60% of grassland and pasture regions have been under moderate to severe dry conditions. This study used multiple lines of evidences, i.e., in-situ AmeriFlux measurements, spatial satellite observations, and scaled ecosystem modeling, to provide independent and complementary analysis on the impact of 2012 flash drought on the US Midwest vegetation greenness and photosynthesis carbon uptake. Three datasets consistently showed that 1) phenological activities of all biomes advanced 1–2 weeks earlier in 2012 compared to the other years of 2010–2014;2) the drought had a more severe impact on agroecosystems(crop and grassland) than on forests;3) the growth of crop and grassland was suppressed from June with significant reduction of vegetation index, sun-induced fluorescence(SIF) and gross primary production(GPP), and did not recover until the end of growing season. The modeling results showed that regional total GPP in 2012 was the lowest(1.76 Pg C/yr) during 2010–2014, and decreased by 63 Tg C compared with the other-year mean. Agroecosystems, accounting for 84% of regional GPP assimilation, were the most impacted by 2012 drought with total GPP reduction of 9%, 7%, 6%, and 29% for maize, soybean, cropland, and grassland, respectively. The frequency and severity of droughts have been predicted to increase in future. The results imply the importance to investigate the influences of flash droughts on vegetation productivity and terrestrial carbon cycling.展开更多
小麦条锈病是影响我国小麦产量的主要病害之一,在小麦受到条锈病菌侵染初期探测到病害信息,对小麦条锈病的防控以及产量和品质的提高具有更为重要的意义。反射率光谱主要反映植被生化组分的浓度信息,而日光诱导叶绿素荧光则对植物光合...小麦条锈病是影响我国小麦产量的主要病害之一,在小麦受到条锈病菌侵染初期探测到病害信息,对小麦条锈病的防控以及产量和品质的提高具有更为重要的意义。反射率光谱主要反映植被生化组分的浓度信息,而日光诱导叶绿素荧光则对植物光合生理变化响应灵敏。为了更好地实现小麦条锈病病情严重度的遥感探测,尤其是条锈病的早期探测,对日光诱导叶绿素荧光和反射率光谱数据监测小麦条锈病病情严重度的敏感性进行了对比分析。首先利用地物光谱仪测定了不同病情严重度的小麦冠层光谱数据,基于夫琅和费暗线原理利用3FLD(three-band Fraunhofer Line Discrimination)方法提取了小麦条锈病不同病情严重度下的日光诱导叶绿素荧光数据,然后分别利用反射率光谱数据和日光诱导叶绿素荧光数据构建小麦条锈病不同发病状态下的遥感探测模型,并通过保留样本交叉检验方式对预测模型精度进行了评价。结果表明:(1)当小麦条锈病病情指数低于20%时,日光诱导叶绿素荧光对小麦条锈病病害信息的响应比反射率光谱数据更为敏感,以日光诱导叶绿素荧光为自变量构建的小麦条锈病病情严重度预测模型达到了极显著性水平,能够在植被叶绿素含量或叶面积指数发生变化之前探测到植物的胁迫状态,实现作物病害的早期诊断,而反射率光谱数据则难以探测到条锈病病害信息;(2)在小麦条锈病病情严重度处于中度发病(20%<DI≤45%)状态时,虽然日光诱导叶绿素荧光和反射率光谱数据均能实现小麦条锈病病情严重度的遥感探测,但利用日光诱导叶绿素荧光数据构建的预测模型优于反射率光谱数据;(3)当小麦条锈病病情严重度达到重度水平(DI>45%)时,利用反射率光谱数据和日光诱导叶绿素荧光数据构建的小麦条锈病病情严重度预测模型均达到了极显著性水平,两种数据均能够较展开更多
【目的】检验SCOPE(Soil Canopy Observation of Photosynthesis and Energy fluxes)模型用于模拟樟子松人工林的日光诱导叶绿素荧光(sun-induced chlorophyll fluorescence,SIF)和植被总初级生产力(gross primary productivity,GPP)动...【目的】检验SCOPE(Soil Canopy Observation of Photosynthesis and Energy fluxes)模型用于模拟樟子松人工林的日光诱导叶绿素荧光(sun-induced chlorophyll fluorescence,SIF)和植被总初级生产力(gross primary productivity,GPP)动态变化的可能性。【方法】对科尔沁沙地南缘樟子松人工林,基于样地SIF、GPP及气象协同观测数据,利用SCOPE模型模拟SIF与GPP的日变化与季节变化,评估了SCOPE模型在典型晴天、典型多云日、整个观测期的模拟效果。【结果】结果显示,利用气象观测数据及冠层参数(入射短波辐射、气温、大气实际水汽压、CO_(2)浓度及叶面积指数),可驱动SCOPE模型模拟樟子松人工林的SIF与GPP。典型晴天日与多云日的SIF模拟值和实测值的R^(2)分别为0.42与0.52,RMSE分别为0.19与0.18 W·m^(-2)·μm^(-1)·sr^(-1);GPP模拟值和观测值的R^(2)分别为0.78与0.89,RMSE分别为1.87与2.57μmol·m^(-2)·s^(-1)。在季节尺度上,SIF和GPP模拟值和观测值的R^(2)分别为0.50、0.72,RMSE分别为0.19 W·m^(-2)·μm^(-1)·sr^(-1)和2.64μmol·m^(-2)·s^(-1)。在整个观测期,多云日的SIF(R^(2)=0.31,RMSE=0.22 W·m^(-2)·μm^(-1)·sr^(-1))与GPP(R^(2)=0.80,RMSE=2.42μmol·m^(-2)·s^(-1))的模拟效果优于晴天日(SIF:R^(2)=0.30,RMSE=0.26 W·m^(-2)·μm^(-1)·sr^(-1),GPP:R^(2)=0.64,RMSE=3.64μmol·m^(-2)·s^(-1))。SIF模拟值总体高于观测值,当SIF强度较低时易对SIF高估,反之则易低估。GPP的模拟精度较高,模型对较低与较高GPP有所低估,对中间值有所高估。【结论】SCOPE模型可用于日尺度与季节尺度的SIF与GPP模拟,且多云日的模拟精度更高。SCOPE模型对樟子松人工林的GPP模拟结果优于SIF,推测SIF模拟精度较低的原因可能是模型对SIF的模拟是基于阔叶植物的辐射传输过程。未来应发展针对针叶植物的SIF辐射传输模型,为针叶林的辐射传输与荧光遥感监测提供模型基础。展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.41801340)Natural Science Foundation of Liaoning,China(No.20180550238)the Key Research Program of Frontier Sciences by Chinese Academy of Sciences(No.QYZDB-SSW-DQC005)
文摘In the summer of 2012, the US Midwest, the most productive agricultural region in the world, experienced the most intense and widespread drought on record for the past hundred years. The 2012 drought, characterized as ‘flash drought’, developed in May with a rapid intensification afterwards, and peaked in mid-July. ~76% of crop region and 60% of grassland and pasture regions have been under moderate to severe dry conditions. This study used multiple lines of evidences, i.e., in-situ AmeriFlux measurements, spatial satellite observations, and scaled ecosystem modeling, to provide independent and complementary analysis on the impact of 2012 flash drought on the US Midwest vegetation greenness and photosynthesis carbon uptake. Three datasets consistently showed that 1) phenological activities of all biomes advanced 1–2 weeks earlier in 2012 compared to the other years of 2010–2014;2) the drought had a more severe impact on agroecosystems(crop and grassland) than on forests;3) the growth of crop and grassland was suppressed from June with significant reduction of vegetation index, sun-induced fluorescence(SIF) and gross primary production(GPP), and did not recover until the end of growing season. The modeling results showed that regional total GPP in 2012 was the lowest(1.76 Pg C/yr) during 2010–2014, and decreased by 63 Tg C compared with the other-year mean. Agroecosystems, accounting for 84% of regional GPP assimilation, were the most impacted by 2012 drought with total GPP reduction of 9%, 7%, 6%, and 29% for maize, soybean, cropland, and grassland, respectively. The frequency and severity of droughts have been predicted to increase in future. The results imply the importance to investigate the influences of flash droughts on vegetation productivity and terrestrial carbon cycling.
文摘小麦条锈病是影响我国小麦产量的主要病害之一,在小麦受到条锈病菌侵染初期探测到病害信息,对小麦条锈病的防控以及产量和品质的提高具有更为重要的意义。反射率光谱主要反映植被生化组分的浓度信息,而日光诱导叶绿素荧光则对植物光合生理变化响应灵敏。为了更好地实现小麦条锈病病情严重度的遥感探测,尤其是条锈病的早期探测,对日光诱导叶绿素荧光和反射率光谱数据监测小麦条锈病病情严重度的敏感性进行了对比分析。首先利用地物光谱仪测定了不同病情严重度的小麦冠层光谱数据,基于夫琅和费暗线原理利用3FLD(three-band Fraunhofer Line Discrimination)方法提取了小麦条锈病不同病情严重度下的日光诱导叶绿素荧光数据,然后分别利用反射率光谱数据和日光诱导叶绿素荧光数据构建小麦条锈病不同发病状态下的遥感探测模型,并通过保留样本交叉检验方式对预测模型精度进行了评价。结果表明:(1)当小麦条锈病病情指数低于20%时,日光诱导叶绿素荧光对小麦条锈病病害信息的响应比反射率光谱数据更为敏感,以日光诱导叶绿素荧光为自变量构建的小麦条锈病病情严重度预测模型达到了极显著性水平,能够在植被叶绿素含量或叶面积指数发生变化之前探测到植物的胁迫状态,实现作物病害的早期诊断,而反射率光谱数据则难以探测到条锈病病害信息;(2)在小麦条锈病病情严重度处于中度发病(20%<DI≤45%)状态时,虽然日光诱导叶绿素荧光和反射率光谱数据均能实现小麦条锈病病情严重度的遥感探测,但利用日光诱导叶绿素荧光数据构建的预测模型优于反射率光谱数据;(3)当小麦条锈病病情严重度达到重度水平(DI>45%)时,利用反射率光谱数据和日光诱导叶绿素荧光数据构建的小麦条锈病病情严重度预测模型均达到了极显著性水平,两种数据均能够较
