To better understand soil moisture dynamics in the Yangtze River Estuary （YRE） and predict its variation in a simple way, a field monitoring experiment was carried out along the north branch of the Yangtze River, wh...To better understand soil moisture dynamics in the Yangtze River Estuary （YRE） and predict its variation in a simple way, a field monitoring experiment was carried out along the north branch of the Yangtze River, where seawater intrusion was strong and salt-water variation is one of the limiting factors of local agriculture. In present paper, relation between antecedent precipitation index （API） and soil water content is studied, and effects of groundwater depth on soil water content was analyzed. A relatively accurate prediction result of soil water content was reached using a neural network model. The impact analysis result showed that the variation of the API was consistent with soil water content and it displayed significant correlations with soil water content in both 20 and 50 cm soil layer, and higher correlation was observed in the layer of 20 cm. Groundwater impact analysis suggested that soil moisture was affected by the depth of groundwater, and was affected more greatly by groundwater at depth of 50 cm than that at 20 cm layer. By introducing API, groundwater depth and temperature together, a BP artificial network model was established to predict soil water content and an acceptable agreement was achieved. The model can be used for supplementing monitoring data of soil water content and predicting soil water content in shallow groundwater areas, and can provide favorable support for the research of water and salt transport in estuary area.展开更多
通过高分卫星遥感影像计算植被供水指数来反演亚高寒草甸土壤水分含量,结合高分辨率遥感影像(GF-2)和中分辨率的遥感影像(Landsat-7)进行土壤水分反演模型建模验证,揭示高分遥感影像结合植被供水指数法在青藏高原东北缘亚高寒草甸草原...通过高分卫星遥感影像计算植被供水指数来反演亚高寒草甸土壤水分含量,结合高分辨率遥感影像(GF-2)和中分辨率的遥感影像(Landsat-7)进行土壤水分反演模型建模验证,揭示高分遥感影像结合植被供水指数法在青藏高原东北缘亚高寒草甸草原上的适用性,同时分析研究区土壤水分分布及其影响因素。基于高分二号(GF-2)、Landsat-7影像数据,以甘南藏族自治州当周草原为研究区,利用植被供水指数(VSWI,vegetation supply water index)构建土壤水分反演模型得到研究区土壤水分含量反演图,通过半方差函数及主成分分析法探索研究区土壤水分空间分布及影响因素。结果表明:研究区土壤水分含量分布状态呈现出一定程度的空间变异,体现在整个研究区内以及各个地块之间,土壤水分含量主要介于0.11%~60.44%之间;土壤水分含量与坡度、海拔、坡向、NDVI、地表温度均呈正相关关系,分布主要受NDVI、坡向、坡度、海拔的影响。综上,利用植被供水指数法结合高分遥感影像监测土壤水分含量是可行的,基于GF-2遥感影像所建立的模型拟合度最优,较Landsat-7遥感影像更具优势。展开更多
水稻冠层叶片含水量(leaf water content,LWC)快速无损监测对指导稻田精准灌溉和提高水稻水分利用效率具有重要意义。试验设置3个不同水分处理(传统淹灌、轻度干湿交替-15 kPa、重度干湿交替-30 kPa),于水分敏感期(抽穗灌浆期)动态监测...水稻冠层叶片含水量(leaf water content,LWC)快速无损监测对指导稻田精准灌溉和提高水稻水分利用效率具有重要意义。试验设置3个不同水分处理(传统淹灌、轻度干湿交替-15 kPa、重度干湿交替-30 kPa),于水分敏感期(抽穗灌浆期)动态监测顶1叶(L_(1))、顶2叶(L_(2))和顶3叶(L_(3))的光谱数据和叶绿素荧光参数,通过全光谱波段筛选出水分敏感波段,建立新型植被指数,结合叶绿素荧光参数,以期建立基于叶位组合的水稻冠层LWC精准监测模型。结果表明:水稻叶片水分敏感波段在近红外波段(1000~1400 nm),所构建新型植被指数NDSII_((1114,1387))较传统植被指数能更好地监测LWC;通过筛选与LWC有高相关性的荧光参数,基于实际光量子产量Y(Ⅱ)和植被指数NDSII_((1114,1387))的耦合监测模型较单一植被指数NDSII_((1114,1387))模型精度提高71.807%~83.976%。与单叶相比,L_(2)和L_(3)叶位组合的Y(Ⅱ)和植被指数NDSII_((1114,1387))耦合模型对水稻冠层LWC监测精度相较L_(2)、L_(3)分别显著(P<0.05)提高11.641%和23.029%。由此表明,基于叶位组合的叶片反射光谱与叶绿素荧光耦合可有效监测水稻冠层LWC,为光学仪器监测水稻LWC提供理论基础,并对未来利用反射光谱与荧光参数进行作物光合作用研究提供理论支持。展开更多
基金financially supported by the Ecological and Environmental Monitoring Project (JJ[2011]-017)funded by the Executive Office of the Three Gorges Project Construction Committee of the State Council of China+1 种基金the National Non-Profit Research Program of China (200903001)the National Basic Research Program of China(2010CB429001)
文摘To better understand soil moisture dynamics in the Yangtze River Estuary （YRE） and predict its variation in a simple way, a field monitoring experiment was carried out along the north branch of the Yangtze River, where seawater intrusion was strong and salt-water variation is one of the limiting factors of local agriculture. In present paper, relation between antecedent precipitation index （API） and soil water content is studied, and effects of groundwater depth on soil water content was analyzed. A relatively accurate prediction result of soil water content was reached using a neural network model. The impact analysis result showed that the variation of the API was consistent with soil water content and it displayed significant correlations with soil water content in both 20 and 50 cm soil layer, and higher correlation was observed in the layer of 20 cm. Groundwater impact analysis suggested that soil moisture was affected by the depth of groundwater, and was affected more greatly by groundwater at depth of 50 cm than that at 20 cm layer. By introducing API, groundwater depth and temperature together, a BP artificial network model was established to predict soil water content and an acceptable agreement was achieved. The model can be used for supplementing monitoring data of soil water content and predicting soil water content in shallow groundwater areas, and can provide favorable support for the research of water and salt transport in estuary area.
文摘通过高分卫星遥感影像计算植被供水指数来反演亚高寒草甸土壤水分含量,结合高分辨率遥感影像(GF-2)和中分辨率的遥感影像(Landsat-7)进行土壤水分反演模型建模验证,揭示高分遥感影像结合植被供水指数法在青藏高原东北缘亚高寒草甸草原上的适用性,同时分析研究区土壤水分分布及其影响因素。基于高分二号(GF-2)、Landsat-7影像数据,以甘南藏族自治州当周草原为研究区,利用植被供水指数(VSWI,vegetation supply water index)构建土壤水分反演模型得到研究区土壤水分含量反演图,通过半方差函数及主成分分析法探索研究区土壤水分空间分布及影响因素。结果表明:研究区土壤水分含量分布状态呈现出一定程度的空间变异,体现在整个研究区内以及各个地块之间,土壤水分含量主要介于0.11%~60.44%之间;土壤水分含量与坡度、海拔、坡向、NDVI、地表温度均呈正相关关系,分布主要受NDVI、坡向、坡度、海拔的影响。综上,利用植被供水指数法结合高分遥感影像监测土壤水分含量是可行的,基于GF-2遥感影像所建立的模型拟合度最优,较Landsat-7遥感影像更具优势。
