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
2020年超长梅雨期内的持续强降雨,导致安徽省发生全域性洪涝灾害,为了快速、准确地提取洪涝淹没范围,为防汛救灾提供科学支撑,选取安徽境内巢湖流域和淮河流域的灾前和灾中Sentinel-1A数据,首先,在快速预处理基础上,采用双极化水体指数(...2020年超长梅雨期内的持续强降雨,导致安徽省发生全域性洪涝灾害,为了快速、准确地提取洪涝淹没范围,为防汛救灾提供科学支撑,选取安徽境内巢湖流域和淮河流域的灾前和灾中Sentinel-1A数据,首先,在快速预处理基础上,采用双极化水体指数(Sentinel-1A dual-polarized water index,SDWI)法,并结合地形因子对平原和山区分别提取水体信息,建立一套洪水淹没区监测流程;然后通过该流程利用灾前、灾中两期合成孔径雷达数据提取2020年7月27日巢湖流域、淮河流域行蓄洪区洪水淹没范围。结果显示:SDWI比直接用后向散射系数提取水体具有优势;7月27日巢湖流域洪水淹没区面积为524.8 km^(2),其中受洪灾较重的是白石天河子流域,西河子流域次之;淮河流域安徽境内行蓄洪区,沿淮的4个地市淹没面积从大到小依次为淮南市、阜阳市、六安市、蚌埠市。研究表明,基于Sentinel-1A数据,采用SDWI和地形因子建立的洪水淹没区监测流程对平原和山区都具有较好的准确性、适用性,且具有较高的时效性,便于及时开展洪水灾害监测。展开更多
为了能够利用遥感图像快速准确地提取围海养殖矢量信息,本文选取养殖水体、堤坝及育苗室等交错分布的海参围海养殖区域作为研究区域,根据研究区域Sentinel-2遥感影像的光谱特征,选用归一化差异水体指数(Normalized Difference Water Ind...为了能够利用遥感图像快速准确地提取围海养殖矢量信息,本文选取养殖水体、堤坝及育苗室等交错分布的海参围海养殖区域作为研究区域,根据研究区域Sentinel-2遥感影像的光谱特征,选用归一化差异水体指数(Normalized Difference Water Index,NDWI)、改进归一化差异水体指数(Modified Normalized Difference Water Index,MNDWI)和增强水体指数(Enhanced Water Index,EWI)三类水体指数,分别进行提取实验,利用同时期高空间分辨率的高分二号卫星(GF-2)影像作为参考,验证不同方法的提取精度,精度评价结果表明:相较MNDWI和EWI两类水体指数,NDWI的分类精度更高,且利用NDWI提取研究区域的围海养殖信息的效果更好,所以该方法可在养殖区域的动态监测和规划管理中发挥数据支撑作用。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.
文摘2020年超长梅雨期内的持续强降雨,导致安徽省发生全域性洪涝灾害,为了快速、准确地提取洪涝淹没范围,为防汛救灾提供科学支撑,选取安徽境内巢湖流域和淮河流域的灾前和灾中Sentinel-1A数据,首先,在快速预处理基础上,采用双极化水体指数(Sentinel-1A dual-polarized water index,SDWI)法,并结合地形因子对平原和山区分别提取水体信息,建立一套洪水淹没区监测流程;然后通过该流程利用灾前、灾中两期合成孔径雷达数据提取2020年7月27日巢湖流域、淮河流域行蓄洪区洪水淹没范围。结果显示:SDWI比直接用后向散射系数提取水体具有优势;7月27日巢湖流域洪水淹没区面积为524.8 km^(2),其中受洪灾较重的是白石天河子流域,西河子流域次之;淮河流域安徽境内行蓄洪区,沿淮的4个地市淹没面积从大到小依次为淮南市、阜阳市、六安市、蚌埠市。研究表明,基于Sentinel-1A数据,采用SDWI和地形因子建立的洪水淹没区监测流程对平原和山区都具有较好的准确性、适用性,且具有较高的时效性,便于及时开展洪水灾害监测。
文摘为了能够利用遥感图像快速准确地提取围海养殖矢量信息,本文选取养殖水体、堤坝及育苗室等交错分布的海参围海养殖区域作为研究区域,根据研究区域Sentinel-2遥感影像的光谱特征,选用归一化差异水体指数(Normalized Difference Water Index,NDWI)、改进归一化差异水体指数(Modified Normalized Difference Water Index,MNDWI)和增强水体指数(Enhanced Water Index,EWI)三类水体指数,分别进行提取实验,利用同时期高空间分辨率的高分二号卫星(GF-2)影像作为参考,验证不同方法的提取精度,精度评价结果表明:相较MNDWI和EWI两类水体指数,NDWI的分类精度更高,且利用NDWI提取研究区域的围海养殖信息的效果更好,所以该方法可在养殖区域的动态监测和规划管理中发挥数据支撑作用。
文摘SAR影像对于水体和地表形变具有很好的辨识性,因此常用来进行水体识别、土壤湿度反演和地表形变检测研究与应用。利用载有C波段合成孔径雷达的Sentinel-1卫星数据对大范围的水体信息进行识别,提出了SDWI(Sentinel-1 Dual-Polarized Water Index)水体信息提取方法。该方法受到NDVI和NDWI方法的启发,结合微波遥感中水体信息在影像中的特点,进一步研究了Sentinel-1双极化数据(VV和VH)之间水体信息提取的关系,以此关系达到增强水体特征的目的,同时消除土壤和植被的存在。分别以Sentinel-1A巢湖区域和Sentinel-1B鄱阳湖区域SAR影像为例来提取水体信息,实验结果表明该方法显著有效,但对影像中阴影的处理是未来研究的难点。
