Water is an important component in agricultural production for both yield quantity and quality. Although all weather conditions are driving factors in the agricultural sector, the precipitation in rainfed agriculture ...Water is an important component in agricultural production for both yield quantity and quality. Although all weather conditions are driving factors in the agricultural sector, the precipitation in rainfed agriculture is the most limiting weather parameter. Water deficit may occur continuously over the total growing period or during any particular growth stage of the crop. Optical remote sensing is very useful but, in cloudy days it becomes useless. Radar penetrates the cloud and collects information through the backscattering data. Normalized Difference Vegetation Index (NDVI) was extracted from Landsat 8 satellite data and used to calculate Crop Coefficient (Kc). The FAO-Penman-Monteith equation was used to calculate reference evapotranspiration (ETo). NDVI and Land Surface Temperature (LST) were calculated from satellite data and integrated with air temperature measurements to estimate Crop Water Stress Index (CWSI). Then, both CWSI and potential crop evapotranspiration (ETc) were used to calculate actual evapotranspiration (ETa). Sentinel-1 radar data were calibrated using SNAP software. The relation between backscattering (dB) and CWSI was an inverse relationship and R2 was as high as 0.82.展开更多
The crop water stress index(CWSI)is a complex instrument to effectively monitor the degree of water stress of crops and provides guidance for timely irrigation.In an experiment utilizing the CWSI with off-season green...The crop water stress index(CWSI)is a complex instrument to effectively monitor the degree of water stress of crops and provides guidance for timely irrigation.In an experiment utilizing the CWSI with off-season green peppers planted in barrels in a greenhouse in Liaoning Province,Northeast China,this study monitors the sub-indexes--such as canopy temperature,environmental factors and yield--determines the changing law of each constituent,achieves an empirical model as well as a baseline formula for the canopy temperature of the peppers with a sufficient water supply,and verifies the rationality of the formula with corresponding experimental data.The results obtained by using the CWSI show that the optimal time to determine the water deficit for off-season green peppers is at noon,by measuring the diurnal variation in the peppers with different water supplies.There is a nonlinear relationship between the yield and the average CWSI at the prime fruit-bearing period;moreover,the optimal time to supply water for off-season green peppers comes when the average water stress index ranges between 0.2 and 0.4 during the prime fruiting stage,thereby ensuring a high yield.展开更多
为探究作物冠层受阳光直射或阴影遮挡对无人机热红外遥感诊断作物水分胁迫、监测土壤含水率的影响,该研究以不同灌溉处理的夏玉米为研究对象,将热红外图像划分为光照冠层、阴影冠层、光照土壤、阴影土壤4个部分,分别提取光照温度与阴影...为探究作物冠层受阳光直射或阴影遮挡对无人机热红外遥感诊断作物水分胁迫、监测土壤含水率的影响,该研究以不同灌溉处理的夏玉米为研究对象,将热红外图像划分为光照冠层、阴影冠层、光照土壤、阴影土壤4个部分,分别提取光照温度与阴影温度后计算了11:00、13:00、15:00的冠气温差(冠层温度与大气温度之差,ΔT)、作物水分胁迫指数(crop water stress index,CWSI)、蒸发比(潜热通量与有效能量的比值,evaporative fraction,EF),并对比了3种指数在不同时刻使用光照温度(ΔT_(L)、CWSI_(L)、EF_(L))与阴影温度(ΔT_(S)、CWSI_(S)、EF_(S))后对土壤含水率的监测效果变化情况。结果表明:1)3种指数的监测效果会随时间发生变化,11:00与15:00时EF监测效果较好,13:00时CWSI监测效果较好,ΔT的监测效果较差但随时间波动最小;2)拔节期在区分光照温度与阴影温度后监测效果在11:00时提升幅度最大,EF、EF_(S)、EF_(L)的R^(2)分别为0.54、0.65、0.78,CWSI、CWSI_(S)、CWSI_(L)的R^(2)分别为0.47、0.64、0.70,抽雄期与灌浆期使用光照温度对监测效果提升不大,但使用阴影温度的指数监测效果有明显降低,在13:00时CWSIS较CWSI有最大降幅,R^(2)降幅分别为0.11、0.06;3)在拔节期与抽雄期使用11:00的EFL,在灌浆期使用13:00的CWSI能取得最好的土壤含水率监测效果,验证期预测土壤含水率的R2分别为0.75、0.75、0.89。该研究可以为无人机热红外监测土壤含水率提供参考。展开更多
为了准确提取作物冠层温度,监测作物水分亏缺状态,以不同水分处理的生菜为研究对象,分别利用手持式热像仪和佳能相机获取生菜的热红外和可见光图像,计算生菜冠层可见光图像与热红外图像的仿射变换参数,并进行配准融合,以获取生菜冠层区...为了准确提取作物冠层温度,监测作物水分亏缺状态,以不同水分处理的生菜为研究对象,分别利用手持式热像仪和佳能相机获取生菜的热红外和可见光图像,计算生菜冠层可见光图像与热红外图像的仿射变换参数,并进行配准融合,以获取生菜冠层区域的热红外图像,而后计算不同处理下的基于冠层温度的水分胁迫指数(Crop Water Stress Index, CWSI)与日蒸散量(Evapotranspiration,ET),分析不同灌溉处理下CWSI同ET的相关关系来监测生菜水分亏缺程度。结果表明,基于仿射变换的热红外目标提取方法可以实现生菜冠层的准确提取,剔除背景后生菜冠层的平均温度值由20.25℃下降至19.25℃。不同水分处理下的生菜热红外冠层的CWSI值展示出明显的差异,且CWSI与ET呈显著负相关,当CWSI越大,ET越小,表明CWSI可以应用在生菜水分胁迫状态监测,能够很好的反应土壤水分含量变化状况。展开更多
冠层温度(canopy temperature,T_(c))是作物水分胁迫计算的基础。准确地剔除热红外图像中的土壤背景,可以提高作物水分的监测精度。该研究以4种水分处理的拔节期夏玉米为研究对象,借助无人机可见光和热红外图像,采用红绿比值指数(red-gr...冠层温度(canopy temperature,T_(c))是作物水分胁迫计算的基础。准确地剔除热红外图像中的土壤背景,可以提高作物水分的监测精度。该研究以4种水分处理的拔节期夏玉米为研究对象,借助无人机可见光和热红外图像,采用红绿比值指数(red-green ratio index,RGRI)法提取研究区域的面状玉米冠层温度的空间分布信息,并分析每幅热红外图像上冠层温度的累积频率。该并提出了两种改进作物水分胁迫指数(crop water stress index,CWSI)性能的方法,一是使用基于正态分布的不同统计分位数分割冠层温度,并基于不同统计分位数上的平均冠层温度计算CWSI(记为CWSI_(TcF%))。二是基于冠层温度方差(canopy temperature variance,V_(ar)),将玉米冠层数据分为4个区间:区间Ⅰ,T_(c)≤40,V_(ar)≤10;区间Ⅱ,T_(c)≤40,10<V_(ar)≤20;区间Ⅲ,35<T_(c)<45,Var>20;区间Ⅳ,40<T_(c)<50,0<V_(ar)≤20,并在各自区间上选择最敏感的统计分位数计算CWSI(记为CWSI_(n))。研究结果表明:1)利用2020年和2021年两年数据计算的CWSI_(n)与作物生理指标(气孔导度G_(s)、净光合速率P_(n)、蒸腾速率T_(r))间的决定系数R2分别为0.72、0.52、0.62,nRMSE分别为23.96%、24.06%、25.60%,模型拟合精度高于原始CWSI(R^(2)分别为0.73、0.34、0.46,nRMSE分别为23.69%、28.27%、30.21%),但与CWSITcF%差别不大(R2分别为0.74、0.54、0.61,nRMSE分别为22.87%、23.74%、25.61%);2)虽然CWSI_(TcF%)能提高诊断作物水分胁迫的精度,但最敏感的冠层温度区间在年际间相差较大(2020,61.17%;2021,49.38%;两年数据,83.51%),而CWSI_(n)稳定性更高(与生理指标间的nRMSE分别为:2020年16.60%、27.37%、28.49%;2021年21.60%、18.95%、22.64%)。因此,综合来看CWSI_(n)可以更加精确地监测作物水分胁迫,利用该改进方法可为无人机遥感精准监测作物水分胁迫状况提供参考。展开更多
为促进黄土高原农业和生态环境可持续发展,基于2001—2020年MODIS蒸散产品和气象站点数据,利用作物缺水指数(crop water stress index,CWSI)、Theil-Sen Median趋势分析、Mann-Kendall检验和偏相关分析等方法,探讨了黄土高原干旱变化特...为促进黄土高原农业和生态环境可持续发展,基于2001—2020年MODIS蒸散产品和气象站点数据,利用作物缺水指数(crop water stress index,CWSI)、Theil-Sen Median趋势分析、Mann-Kendall检验和偏相关分析等方法,探讨了黄土高原干旱变化特征及其影响因素。结果表明:①年际上,2001—2020年黄土高原CWSI呈显著线性下降过程,下降速率为0.0058 a^(-1)(P<0.01)。干旱程度呈先减(2001—2018年)后增(2018—2020年)过程;②空间上,CWSI以减少趋势为主,显著减少趋势占区域总面积91.24%,其中青海、甘肃、宁夏、陕西南部地区CWSI下降速率较快;③黄土高原CWSI与气温以负偏相关关系为主,显著负偏相关占区域面积的7.78%,集中分布于青海东部、山西南部和河南北部;与降水以负偏相关关系为主,显著负偏相关占区域总面积60.88%,集中分布于黄土高原西南部和东部地区。气温升高背景下,降水增加促使黄土高原实际蒸发显著增加、潜在蒸发不显著减少,导致CWSI显著下降。研究结论认为降水是黄土高原CWSI下降的主要影响因素,年降水量增加减缓了黄土高原干旱趋势。展开更多
文摘Water is an important component in agricultural production for both yield quantity and quality. Although all weather conditions are driving factors in the agricultural sector, the precipitation in rainfed agriculture is the most limiting weather parameter. Water deficit may occur continuously over the total growing period or during any particular growth stage of the crop. Optical remote sensing is very useful but, in cloudy days it becomes useless. Radar penetrates the cloud and collects information through the backscattering data. Normalized Difference Vegetation Index (NDVI) was extracted from Landsat 8 satellite data and used to calculate Crop Coefficient (Kc). The FAO-Penman-Monteith equation was used to calculate reference evapotranspiration (ETo). NDVI and Land Surface Temperature (LST) were calculated from satellite data and integrated with air temperature measurements to estimate Crop Water Stress Index (CWSI). Then, both CWSI and potential crop evapotranspiration (ETc) were used to calculate actual evapotranspiration (ETa). Sentinel-1 radar data were calibrated using SNAP software. The relation between backscattering (dB) and CWSI was an inverse relationship and R2 was as high as 0.82.
基金The authors express appreciation for the financial support granted by the Education Department of Liaoning Province,China(Project No.L2012239)and the Ministry of Agriculture,China(Project No.201303125)We also thank Dr.Wang Yingkuan for his valuable suggestions for improving this paper and Dr.Cheryl Rutledge(Florida,USA)for her English editorial assistance.
文摘The crop water stress index(CWSI)is a complex instrument to effectively monitor the degree of water stress of crops and provides guidance for timely irrigation.In an experiment utilizing the CWSI with off-season green peppers planted in barrels in a greenhouse in Liaoning Province,Northeast China,this study monitors the sub-indexes--such as canopy temperature,environmental factors and yield--determines the changing law of each constituent,achieves an empirical model as well as a baseline formula for the canopy temperature of the peppers with a sufficient water supply,and verifies the rationality of the formula with corresponding experimental data.The results obtained by using the CWSI show that the optimal time to determine the water deficit for off-season green peppers is at noon,by measuring the diurnal variation in the peppers with different water supplies.There is a nonlinear relationship between the yield and the average CWSI at the prime fruit-bearing period;moreover,the optimal time to supply water for off-season green peppers comes when the average water stress index ranges between 0.2 and 0.4 during the prime fruiting stage,thereby ensuring a high yield.
文摘为探究作物冠层受阳光直射或阴影遮挡对无人机热红外遥感诊断作物水分胁迫、监测土壤含水率的影响,该研究以不同灌溉处理的夏玉米为研究对象,将热红外图像划分为光照冠层、阴影冠层、光照土壤、阴影土壤4个部分,分别提取光照温度与阴影温度后计算了11:00、13:00、15:00的冠气温差(冠层温度与大气温度之差,ΔT)、作物水分胁迫指数(crop water stress index,CWSI)、蒸发比(潜热通量与有效能量的比值,evaporative fraction,EF),并对比了3种指数在不同时刻使用光照温度(ΔT_(L)、CWSI_(L)、EF_(L))与阴影温度(ΔT_(S)、CWSI_(S)、EF_(S))后对土壤含水率的监测效果变化情况。结果表明:1)3种指数的监测效果会随时间发生变化,11:00与15:00时EF监测效果较好,13:00时CWSI监测效果较好,ΔT的监测效果较差但随时间波动最小;2)拔节期在区分光照温度与阴影温度后监测效果在11:00时提升幅度最大,EF、EF_(S)、EF_(L)的R^(2)分别为0.54、0.65、0.78,CWSI、CWSI_(S)、CWSI_(L)的R^(2)分别为0.47、0.64、0.70,抽雄期与灌浆期使用光照温度对监测效果提升不大,但使用阴影温度的指数监测效果有明显降低,在13:00时CWSIS较CWSI有最大降幅,R^(2)降幅分别为0.11、0.06;3)在拔节期与抽雄期使用11:00的EFL,在灌浆期使用13:00的CWSI能取得最好的土壤含水率监测效果,验证期预测土壤含水率的R2分别为0.75、0.75、0.89。该研究可以为无人机热红外监测土壤含水率提供参考。
文摘为了准确提取作物冠层温度,监测作物水分亏缺状态,以不同水分处理的生菜为研究对象,分别利用手持式热像仪和佳能相机获取生菜的热红外和可见光图像,计算生菜冠层可见光图像与热红外图像的仿射变换参数,并进行配准融合,以获取生菜冠层区域的热红外图像,而后计算不同处理下的基于冠层温度的水分胁迫指数(Crop Water Stress Index, CWSI)与日蒸散量(Evapotranspiration,ET),分析不同灌溉处理下CWSI同ET的相关关系来监测生菜水分亏缺程度。结果表明,基于仿射变换的热红外目标提取方法可以实现生菜冠层的准确提取,剔除背景后生菜冠层的平均温度值由20.25℃下降至19.25℃。不同水分处理下的生菜热红外冠层的CWSI值展示出明显的差异,且CWSI与ET呈显著负相关,当CWSI越大,ET越小,表明CWSI可以应用在生菜水分胁迫状态监测,能够很好的反应土壤水分含量变化状况。
文摘冠层温度(canopy temperature,T_(c))是作物水分胁迫计算的基础。准确地剔除热红外图像中的土壤背景,可以提高作物水分的监测精度。该研究以4种水分处理的拔节期夏玉米为研究对象,借助无人机可见光和热红外图像,采用红绿比值指数(red-green ratio index,RGRI)法提取研究区域的面状玉米冠层温度的空间分布信息,并分析每幅热红外图像上冠层温度的累积频率。该并提出了两种改进作物水分胁迫指数(crop water stress index,CWSI)性能的方法,一是使用基于正态分布的不同统计分位数分割冠层温度,并基于不同统计分位数上的平均冠层温度计算CWSI(记为CWSI_(TcF%))。二是基于冠层温度方差(canopy temperature variance,V_(ar)),将玉米冠层数据分为4个区间:区间Ⅰ,T_(c)≤40,V_(ar)≤10;区间Ⅱ,T_(c)≤40,10<V_(ar)≤20;区间Ⅲ,35<T_(c)<45,Var>20;区间Ⅳ,40<T_(c)<50,0<V_(ar)≤20,并在各自区间上选择最敏感的统计分位数计算CWSI(记为CWSI_(n))。研究结果表明:1)利用2020年和2021年两年数据计算的CWSI_(n)与作物生理指标(气孔导度G_(s)、净光合速率P_(n)、蒸腾速率T_(r))间的决定系数R2分别为0.72、0.52、0.62,nRMSE分别为23.96%、24.06%、25.60%,模型拟合精度高于原始CWSI(R^(2)分别为0.73、0.34、0.46,nRMSE分别为23.69%、28.27%、30.21%),但与CWSITcF%差别不大(R2分别为0.74、0.54、0.61,nRMSE分别为22.87%、23.74%、25.61%);2)虽然CWSI_(TcF%)能提高诊断作物水分胁迫的精度,但最敏感的冠层温度区间在年际间相差较大(2020,61.17%;2021,49.38%;两年数据,83.51%),而CWSI_(n)稳定性更高(与生理指标间的nRMSE分别为:2020年16.60%、27.37%、28.49%;2021年21.60%、18.95%、22.64%)。因此,综合来看CWSI_(n)可以更加精确地监测作物水分胁迫,利用该改进方法可为无人机遥感精准监测作物水分胁迫状况提供参考。
