This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 mill...This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software w<span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ere</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands </span><i><span style="font-family:Verdana;">i.e</span></i></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 46.1314</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;">, and, 18.3437</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 30.9693</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> respectively. Results of Kumasi also show a higher range of temperatures from 32.6986</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 19.1077<span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span></span><span style="font-family:Verdana;">C</span><span style="font-family:Verdana;"> during the dry season. In the wet season, temperatures ranged from 26.4142</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to </span><span style="font-family:Verdana;">-</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">0</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.898728</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;">. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.</span></span></span></span>展开更多
Recent studies have focused on the improvement of rice productivity under aerobic conditions for times when water resources and food production are limited. This study aimed to evaluate the adaptability of high-yieldi...Recent studies have focused on the improvement of rice productivity under aerobic conditions for times when water resources and food production are limited. This study aimed to evaluate the adaptability of high-yielding rice cultivars to moderately water-stressed upland conditions in order to contribute breeding. A three-year field experiment in the temperate climate of Kyoto, Japan, indicated that the decrease in yield was mainly derived from a decrease in above-ground total dry matter (TDM) rather than a decrease in harvest index (HI). Although the decrease in TDM was mostly caused by a decrease in radiation use efficiency (RUE), we determined that the key to adapting high-yielding cultivars to upland conditions is intercepted radiation per day (IRPD), governed by leaf area index (LAI). Although the effect was not robust, LAI growth under upland conditions was associated with root length density. RUE was dependent on leaf water potential (LWP), indicating that a plant’s ability to maintain LWP under water-stressed conditions is important. The results also suggest the necessity of a canopy analyzer to evaluate LAI, as well as an infrared radiation thermometer to evaluate RUE. Performing such measurements during breeding efforts allows us to select for genotypes that are suitable for less stressed aerobic conditions.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
为了给以作物高效灌溉制度提供理论依据,对不同供水条件下冬小麦冠层温度进行了多年田间观测,模拟了以土壤水分条件为主导的冠气温差、叶水势、水分亏缺指数等的变化规律及其对影响因素的响应。结果表明,冬小麦各生育阶段不同供水处理...为了给以作物高效灌溉制度提供理论依据,对不同供水条件下冬小麦冠层温度进行了多年田间观测,模拟了以土壤水分条件为主导的冠气温差、叶水势、水分亏缺指数等的变化规律及其对影响因素的响应。结果表明,冬小麦各生育阶段不同供水处理冠层温度(T c)受土壤水分影响明显,处理间冠气温差(ΔT)差异极显著。叶水势(LW P)与ΔT、作物水分胁迫指数(CW S I)相关显著。LW P=-1.8M Pa,CW S I=0.40是指示冬小麦发生水分胁迫的关键性指标。综合各指标,为了达到节水目的,使ΔT维持在0^-4℃,可获得冬小麦产量最优值,此时冬小麦灌溉量下限应使土壤相对含水量达到58.7%。展开更多
文摘This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software w<span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ere</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands </span><i><span style="font-family:Verdana;">i.e</span></i></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 46.1314</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;">, and, 18.3437</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 30.9693</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> respectively. Results of Kumasi also show a higher range of temperatures from 32.6986</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 19.1077<span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span></span><span style="font-family:Verdana;">C</span><span style="font-family:Verdana;"> during the dry season. In the wet season, temperatures ranged from 26.4142</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to </span><span style="font-family:Verdana;">-</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">0</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.898728</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;">. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.</span></span></span></span>
文摘Recent studies have focused on the improvement of rice productivity under aerobic conditions for times when water resources and food production are limited. This study aimed to evaluate the adaptability of high-yielding rice cultivars to moderately water-stressed upland conditions in order to contribute breeding. A three-year field experiment in the temperate climate of Kyoto, Japan, indicated that the decrease in yield was mainly derived from a decrease in above-ground total dry matter (TDM) rather than a decrease in harvest index (HI). Although the decrease in TDM was mostly caused by a decrease in radiation use efficiency (RUE), we determined that the key to adapting high-yielding cultivars to upland conditions is intercepted radiation per day (IRPD), governed by leaf area index (LAI). Although the effect was not robust, LAI growth under upland conditions was associated with root length density. RUE was dependent on leaf water potential (LWP), indicating that a plant’s ability to maintain LWP under water-stressed conditions is important. The results also suggest the necessity of a canopy analyzer to evaluate LAI, as well as an infrared radiation thermometer to evaluate RUE. Performing such measurements during breeding efforts allows us to select for genotypes that are suitable for less stressed aerobic conditions.
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。
文摘为了给以作物高效灌溉制度提供理论依据,对不同供水条件下冬小麦冠层温度进行了多年田间观测,模拟了以土壤水分条件为主导的冠气温差、叶水势、水分亏缺指数等的变化规律及其对影响因素的响应。结果表明,冬小麦各生育阶段不同供水处理冠层温度(T c)受土壤水分影响明显,处理间冠气温差(ΔT)差异极显著。叶水势(LW P)与ΔT、作物水分胁迫指数(CW S I)相关显著。LW P=-1.8M Pa,CW S I=0.40是指示冬小麦发生水分胁迫的关键性指标。综合各指标,为了达到节水目的,使ΔT维持在0^-4℃,可获得冬小麦产量最优值,此时冬小麦灌溉量下限应使土壤相对含水量达到58.7%。