基于环境一号卫星的大洋河河口海域营养盐遥感反演

综合利用大洋河河口海域水体调查的实测数据及环境一号CCD影像数据,进行了该区域营养盐反演研究.结果表明,对数函数拟合NO2--N与Rrs(Band2)效果最佳,而线性函数拟合NH4+-N与Rrs(Band2)、NO3--N与Rrs(Band1)、TIN与Rrs(Band1)效果最佳,相关性R2分别可达0.939、0.935、0.945、0.970,未发现较佳的PO43--P反演模型;营养盐在可见光波段并非光敏感性物质,其与等效遥感反射率存在相关性的主要原因在于河口海域陆源输入物质典型的扩散特征,使得营养盐与某些水色参数存在较高的相关性,NO2--N、NH4+-N、NO3--N以及TIN与有色溶解性有机物吸收系数ag(400)的相关性R2分别可达0.836、0.808、0.786、0.854,这决定了在河口海域进行营养盐遥感反演的可行性;从卫星产品专题图可看出,大洋河河口海域NO2--N、NH4+-N、NO3--N以及TIN都存在一个明显的高值区,该高值区营养盐浓度由河口向外逐渐降低,直至外海最低.     

景观格局类型对热岛效应的影响——以福州市中心城区为例

以“火炉”城市之一福州市为例,运用RS与GIS技术、半变异函数分析等方法,以2009年、2018年Landsat TM/OLI系列影像为数据源,从城市尺度分析了景观格局与城市热岛的关系,从景观格局特征尺度分析了景观格局类型对热岛效应的影响.结果表明：从2009年、2018年9个景观格局指数计算结果显示福州市中心城区景观格局破碎化程度增加;2009年与2018年的景观格局特征尺度分别为3600m,2500m,表明后者的景观格局更为丰富;虽然主城区的热岛效应得到了一定缓解,但城市热岛的范围逐渐扩大,八个新城分别成为了新的热岛中心;不同景观格局类型的热岛效应的严重情况依次为：纯人工本底格局 > 人工本底的散布格局 > 人工本底的指状、网络格局 > 自然本底的指状、网络格局 > 自然本底的散布格局 > 纯自然本底格局.研究以期通过细化景观格局研究尺度,为具有热岛降温作用的景观格局提出优化的方向.     

Validation of MODIS aerosol retrievals and evaluation of potential cloud contamination in East Asia

MODIS aerosol retrievals onboard Terra/Aqua and ground truth data obtained from AERONET(Aerosol Robtic Network) solar direct radiance measurements are collocated to evaluate the quality of the former in East Asia. AERONET stations in East Asia are separated into two groups according to their locations and the preliminary validation results for each station. The validation results showed that the accuracy of MODIS aerosol retrievals in East Asia is a little worse than that obtained in other regions such as Eastern U.S., Western Europe, Brazil and so on. The primary reason is due to the improper aerosol model used in MODIS aerosol retrieval algorithm, so it is of significance to characterize aerosol properties properly according to long term ground-based remote sensing or other relevant in situ observations in order to improve MODIS retrievals in East Asia. Cloud contamination is proved to be one of large errors, which is demonstrated by the significant relation between MODIS aerosol retrievals versus cloud fraction, as well as notable improvement of linear relation between satellite and ground aerosol data after potential cloud contamination screened. Hence, it is suggested that more stringent clear sky condition be set in use of MODIS aerosol data. It should be pointed out that the improvement might be offset by other error sources in some cases because of complex relation between different errors. Large seasonal variation of surface reflection and uncertainties associated with it result in large intercepts and random error in MODIS aerosol retrievals in northern inland of East Asia. It remains to be a big problem to retrieve aerosols accurately in inland characterized by relatively larger surface reflection than the requirement in MODIS aerosol retrieval algorithm.     

基于MODIS数据的云南省地表温度反演研究

基于MODIS数据反演云南省地表温度,采用分裂窗算法需要参数较少、易实现。研究表明:云南省地表温度由北向南递增,东部高、西部低,基本符合云南省实际情况。利用气象观测站实测数据进行验证,平均误差为3.16℃,两组数据基本接近,证明基于MODIS数据和分裂窗算法反演云南省地表温度是可行的。     

基于Sentinel-3 OLCI影像的秦皇岛海域悬浮物浓度遥感反演

基于2013~2021年期间秦皇岛海域遥感反射率、悬浮物浓度及叶绿素a浓度等实测数据,开展了该海域Sentinel-3 OLCI影像的悬浮物浓度遥感反演模型研究.结果表明,文献中常用的典型经验模型形式均不适用于秦皇岛海域,以490、620及708.75nm为悬浮物反演的敏感波段,以560nm为参比波段,将各敏感波段与参比波段的比值作为自变量,最终建立了适用于秦皇岛海域的Sentinel-3 OLCI四波段悬浮物浓度遥感反演模型（R²=0.69,MAPE=24.79%,RMSE=2.82mg/L）;并采用2021年7月24日Sentinel-3 OLCI影像进行悬浮物浓度遥感反演产品的真实性检验,得到反演值与实测值的平均相对误差为13.24%.将上述四波段模型用于2021年1~12月秦皇岛海域的Sentinel-3 OLCI影像,反演得到月均悬浮物浓度,发现秦皇岛海域悬浮物浓度整体呈现沿岸海域高、离岸海域低,秋冬季高、春夏季低的时空变化特征;且2018~2021年秦皇岛海域悬浮物浓度的年均值逐年递减,水体越来越澄清.     

基于GOCI影像和水体光学分类的内陆湖泊叶绿素a浓度遥感估算

叶绿素a作为水质参数之一,常用来作为衡量水体富营养化程度的指示标准.利用从太湖及洞庭湖获取的326个实测数据,基于实测遥感反射率对水体光谱进行光学分类,结果表明所采集的样点可分为3种水体类型.结合GOCI的波段设置,建立了不同类型水体的叶绿素a浓度反演模型.水体类型一可以利用490 nm(3波段)和555 nm(4波段)来反演,水体类型二可利用660 nm(5波段)和443 nm(2波段),水体类型三利用745 nm(7波段)和680 nm(6波段).精度分析表明,分类后的平均相对误差明显下降,类型一为38.91%、类型二为24.19%、类型三为22.90%;类型一均方根误差为4.87μg·L-1、类型二为8.13μg·L-1、类型三为11.66μg·L-1;分类前后的总体平均相对误差由49.78%降低到29.59%,总体均方根误差由14.10μg·L-1降低到9.29μg·L-1,分类后反演精度得到了显著提高.利用2013年5月13日8景GOCI影像反演了太湖的叶绿素a浓度,结果表明,2013年5月13日太湖叶绿素a浓度日变化显著,高值区主要集中在竺山湾、梅梁湾、贡湖湾,低值区主要集中在湖心区以及南部区域,10:00以后太湖西南部沿岸的叶绿素a浓度显著降低.这种先分类后反演的方法对于二类水体的模型反演精度的提高具有重要作用.     

基于实测光谱的烟台四十里湾水体叶绿素a遥感反演

水体叶绿素a含量是二类水体水色遥感反演的主要参数之一。通过分析烟台近海水体实测反射光谱曲线，选取了特征波段及波段组合，建立了研究区叶绿素a含量遥感反演模型。通过对比不同反演模型可知：三波段模型是烟台近海水体叶绿素a遥感反演的最优模型，决定系数为0?6608，均方根误差（RMSE）为0?59μg／L；其次是归一化荧光高度模型和反射峰面积模型，决定系数分别为0?6213和0?5589，RMSE分别为0?62μg／L和0?67μg／L。     

地震科技期刊国际化发展的构想

分析了加入WTO后 ,地震科技期刊国际化发展的迫切性和必要性 ,论述了地震科技期刊国际化发展的 5种构想 :(1)调整和优化地震科技期刊 ;(2 )提高科技论文质量 ;(3)办好英文科技期刊 ;(4 )编委、作者队伍国际化 ;(5 )加快期刊上网 ,使更多的地震期刊进入国际检索系统。     

多山地区植被类型遥感调查 ——以金沙江流域滇西北部分为例

通常,要想从卫星图片获得象滇西北这样多山地区的信息和植被图或土地利用图是很困难的.由于地表覆盖多样性和电磁辐射率有限,很难解读这些地区的遥感资料 .由于同物异谱或同谱异物,电脑监测分类尤难解读.为满足区域调查快速发展的要求,提高自读能力,本文介绍了一种分区解读,分层解读和人机互助的方法,这些地区现有的研究结果也用于调查,获得了令人满意的结果.     

PIXGRO: A model for simulating the ecosystem CO2 exchange and growth of spring barley

A model, PIXGRO, developed by coupling a canopy flux sub-model (PROXEL_NEE; PROcess-based piXEL Net Ecosystem CO₂ Exchange) to a vegetation structure submodel (CGRO), for simulating both net ecosystem CO₂ exchange (NEE) and growth of spring barley is described. PIXGRO is an extension of the stand-level CO₂ and H₂O-flux model PROXEL_NEE, that simulates the NEE on a process basis, but goes further to include the dry matter production, partitioning, and crop development for spring barley. Dry matter partitioned to the leaf was converted to leaf area index (LAI) using relationships for the specific leaf area (SLA). The canopy flux component, PROXEL_NEE was calibrated using information from the literature on C3 plants and was tested using CO₂ flux data from an eddy-covariance (EC) method in Finland with long-term observations. The growth component (CGRO) was calibrated using data from the literature on spring barley as well as data from the Finland site. It was then validated against field data from two sites in Germany and partly via the use of MODIS remotely sensed LAI from the Finland site.Both the diurnal and the seasonal patterns of gross CO₂ uptake were very well simulated (R² = 0.92). A slight seasonal bias may be attributed to leaf ageing. Crop growth was also well simulated; simulated dry matter agreed with field observed data from Germany (R² = 0.90). For LAI, the agreement between the simulated and observed was good (R² = 0.80), giving an indication that functions describing the conversion of fixed CO₂ to dry matter and the subsequent partitioning leaf dry matter and LAI simulation were robust and provided reliable estimates.The MODIS LAI at a resolution of 1000 m agreed poorly (R² = 0.45) with the PIXGRO simulated LAI and the observed LAI at the Finland site in 2001. We attributed this to the coarse resolution of the image and/or the small size of the barley field (about 17 ha or 0.25 km²) at the Finland site. By deriving a regression relation between the observed LAI and NDVI from a higher resolution MODIS (500 m resolution), the MODIS-recalculated LAI agreed better with the PIXGRO-simulated LAI (R² = 0.86).PIXGRO provides a prototype model bridging the disciplines of plant physiology, crop modeling and remote sensing, for use in a spatial context in evaluating carbon balances and plant growth at stand level, landscape, regional, and with some care, continental scales. Since almost 50% of the European land surface is covered by crops, such a model is needed for the dynamic estimation of LAI and NEE of croplands.