基于SPOT-VGT可见光/短波红外波段数据对AMSR-E土壤湿度的降尺度研究

基于SPOT-VGT数据,由短波红外、红和蓝波段反射率计算了表征地表土壤湿度的可见光—短波红外干旱指数(VSDI),通过对1km空间分辨率的VSDI影像进行空间升尺度处理,采用多种函数建立了25km空间分辨率AMSR-E土壤湿度数据与VSDI指数的关系,发现二者关系最符合S型曲线模型,拟合残差在空间上呈现随机分布的特征。基于S曲线函数关系下的1km预测土壤湿度和残差值,对AMSR-E土壤湿度进行降尺度模拟,得到1km空间分辨率的土壤湿度。将原始AMSR-E土壤湿度和实测数据对降尺度结果分别比较验证后,表明基于该方法获得的土壤湿度模拟精度较高。     

AMSR-E亮温数据与MODIS陆表分类产品结合反演全球陆表温度

AMSR-E被动微波传感器获取的亮温数据与MODIS陆表分类产品(MOD12)相结合,将全球陆表分为16类,并假设每种类型的地表在各个被动微波通道具有较一致的发射率,在此基础上针对每种陆表类型分别建立了陆表温度反演算法。在算法的建立过程中,为了避免混合像元以及冻土、积雪发射率不确定性带来的影响,仅对单一地表类型占90%以上以及MODIS陆表温度产品高于273K的被动微波像元进行回归。同时,考虑到降雨对回归结果的影响,在数据选择中加入了降雨判识,在被动微波亮温数据中除去了降雨像元。利用上述算法,用2004年1～10月的全球部分地区AMSR-E数据在MODIS陆表分类产品的基础上对每种地表类型分别进行了陆表温度反演,并与MODIS陆表温度产品进行对比,结果显示相关性较好,均方根误差为2～4 K。     

星载微波辐射计AMSR-E在台风分析中的应用

基于洋面上的微波辐射传输模型,采用多元线性回归算法,建立了海洋-大气参数的反演算式,并对其进行了验证。在此基础上,根据AMSR-E的反演结果,结合微波图像,对2005年“麦莎”和“泰利”两次台风的发生发展过程进行了初步分析和比较。结果表明,反演算式的精度接近于美国雪冰数据中心公布的业务产品,反演结果比较符合实际情况,微波图像可以较好地反映台风发生发展过程中的变化特征。     

利用无线传输的BDS--R土壤湿度反演系统

针对野外地区土壤湿度监测时间长、数据采集量大的需求,提出了基于无线传输的BDS—R土壤湿度反演系统。以UM220—III N和GPRS DTU为核心构建了北斗信号数据处理与无线传输模块,将圆极化天线采集的北斗信号数据处理成NMEA 0183格式串行数据,并经GPRS与Internet传送至COMWAY无线串口服务器,再通过Microsoft Visual Studio 2008软件构建的数据存储软件提取服务器上的数据,并保存至本地磁盘。选取了连续1个月的北斗C05卫星信号信息进行反射系数法反演土壤湿度实验,反演结果与原位湿度值相关系数达0.69,平均相对误差为16.86%,表明系统工作可靠性强,适用于长期远距离的野外地区土壤湿度反演。     

基于TVDI的矿区土壤湿度反演及地形因子影响分析

为给我国西部资源开发、生态保护及旱灾预警等领域提供科学依据,以神东矿区为研究区,探索矿区土壤湿度变化。根据2000—2018年的MODIS长时间序列遥感影像,提取归一化植被指数（NDVI）和地表温度（Ts）,构建NDVI-Ts二维光谱特征空间,计算研究区的温度植被干旱指数,绘制矿区的地表土壤湿度等级分布图,从矿区尺度上给出地表土壤湿度的时空变化趋势,分析矿区的地形因子对地表土壤湿度的影响。结果表明：1）TVDI法能够较好的反演出矿区的土壤湿度;2）矿区土壤湿度呈现从西北部向东南部逐渐增加的趋势;3）高程、坡度和坡向3种地形因子在不同范围内,均对地表土壤湿度有着不同程度的影响。整体来看,2000—2018年,神东矿区土壤湿度有增有减,呈微弱上升的趋势,矿区环境得到改善。     

基于TVDI的矿区土壤湿度反演及地形因子影响分析

为给我国西部资源开发、生态保护及旱灾预警等领域提供科学依据,以神东矿区为研究区,探索矿区土壤湿度变化。根据2000—2018年的MODIS长时间序列遥感影像,提取归一化植被指数（NDVI）和地表温度（Ts）,构建NDVI-Ts二维光谱特征空间,计算研究区的温度植被干旱指数,绘制矿区的地表土壤湿度等级分布图,从矿区尺度上给出地表土壤湿度的时空变化趋势,分析矿区的地形因子对地表土壤湿度的影响。结果表明：1) TVDI法能够较好地反演出矿区的土壤湿度;2）矿区土壤湿度呈现从西北部向东南部逐渐增加的趋势;3）高程、坡度和坡向3种地形因子在不同范围内,均对地表土壤湿度有着不同程度的影响。整体来看,2000—2018年,神东矿区土壤湿度有增有减,呈微弱上升的趋势,矿区环境得到改善。     

利用AMSR-E遥感数据反演地表温度——以2008年广东省春季寒害为例

基于2008年1月25日至2008年2月5日期间的AMSR-E/Aqua L2A微波亮度温度数据,以广东省为研究对象,依据微波极化差异指数（MPDI）、归一化植被指数（NDVI）和比率植被指数（RVI）等3种植被指数,将广东省地表植被覆盖情况分为裸地、草地、灌木林、针叶林和阔叶林等5种类型,利用逐步回归分析方法,建立了基于不同植被覆盖类型的微波亮度温度与地面气象温度多元回归模型。同步地面气象温度数据验证表明,本文建立的基于地表植被覆盖分类的多波段地表温度反演模型,地表温度反演精度基本可达到3.0℃,其中有大约86%的地区地表温度反演精度可以控制在2.5℃以内,为广东省作物寒害预测提供了微波遥感技术支持。     

仪征地区农田深层土壤湿度遥感反演初探

利用MODIS合成产品数据MOD11A2和MOD13A2获取的陆地表面温度(Ts)和归一化植被指数(NDVI)构建Ts/NDVI特征空间,依据该特征空间计算温度植被干旱指数(TVDI),进而反演了仪征地区不同季节的40 cm土壤相对湿度。使用野外同步实测数据进行验证,结果显示,总体平均相对误差为11.83%,2004年11月误差最小,为4.30%。遥感反演的仪征地区土壤湿度分布图表明该地区存在两个土壤湿度高值区,分别位于仪征南部的长江冲积平原和西北部的谷底平原地带,并且土壤平均相对湿度越大,其高值区与低值区之间的差异越小。     

利用移动窗口卷积神经网络反演土壤湿度北大核心CSCD

针对遥感指数反演土壤湿度(soil moisture,SM)精度易受数据类型、植被覆盖等因素影响,以及不同场景下湿度模型迁移应用问题,提出了一种基于移动窗口特征的卷积神经网络(convolutional neural network based on window features,CNN_W)土壤湿度反演方法。该方法考虑临近地物反射辐射影响,将输入特征进行尺度化处理,利用一维卷积核强大的非线性拟合能力对SM进行提取,提高了SM反演精度。进一步通过参数微调的方式将上述模型应用至那曲县SM反演,解决少样本情况下样本训练困难的问题。实验结果表明:CNN_W能实现复杂农业地SM精确反演(R、RMSE、MAE、MAPE分别为0.832、0.038 cm 3/cm 3、0.028 cm 3/cm 3、9.813%),较卷积神经网络方法精度有提升;常用遥感干旱指数在该地区不适用;迁移学习方法在少样本情况下实现了模型异地应用(R、RMSE分别为0.824、0.045 cm 3/cm 3),具有良好应用前景。     

AO辅助LSSVM的GNSS-IR土壤湿度反演方法

全球导航卫星系统多径干涉遥感技术(global navigation satellite system interferometric reflection, GNSS-IR)已成为目前研究的热点,用其测量的数据可以对土壤湿度值等进行估算。针对当前该方法存在土壤湿度反演精度较低的问题,文章以美国板块边界观测网络(PBO)中p043测站为研究对象,并对该测站的GNSS信噪比数据进行分析,提取L2频段反射信号的延迟相位作为输入,PBO H₂O的土壤湿度值作为输出,构建了基于AO-LSSVM土壤湿度反演模型,并将该模型与BP神经网络和PSO-LSSVM进行对比。实验结果表明,基于AO-LSSVM方法得到的PRN10卫星反演结果与土壤湿度真值之间的决定系数为0.920,均方根误差为0.021,平均绝对误差为0.017,相比BP神经网络和PSO-LSSVM更加贴近土壤湿度真值,证明了利用该方法能够有效提高土壤湿度反演的精度。     

Vegetation and surface roughness effects on AMSR-E land observations

Characteristics of the land surface including soil moisture, vegetation cover, and soil roughness among others influence the microwave emissivity and brightness temperature of the surface as observed from space. Knowledge of the variability of microwave signatures of vegetation and soil roughness is necessary to separate these influences from those of soil moisture for remote sensing applications to global hydrology and climate. We describe here a characterization of vegetation and soil roughness at the frequencies and spatial resolution of the EOS Aqua Advanced Microwave Scanning Radiometer (AMSR-E). A single parameter has been used to approximate the combined effects of vegetation and roughness. AMSR-E data have been analyzed to determine the frequency dependence of this parameter and to generate a global vegetation/roughness map and an estimate of seasonal variability. A physical model is used for the analysis with approximations appropriate to the AMSR-E footprint scale and coefficients calibrated empirically against the AMSR-E data. The spatial variabilities of roughness and vegetation cannot be estimated independently using this approach, but their temporal dynamics allow separation of predominantly static roughness effects from time-varying vegetation effects using multitemporal analysis. Global signals of time-varying vegetation water content derived from this analysis of AMSR-E data are consistent with time-varying biomass estimates obtained by optical/infrared remote sensing techniques.     

Aircraft based soil moisture retrievals under mixed vegetation and topographic conditions

An unresolved issue in global soil moisture retrieval using passive microwave sensors is the spatial integration of heterogeneous landscape features to the nominal 50 km footprint observed by most low frequency satellite systems. One of the objectives of the Soil Moisture Experiments 2004 (SMEX04) was to address some aspects of this problem, specifically variability introduced by vegetation, topography and convective precipitation. Other goals included supporting the development of soil moisture data sets that would contribute to understanding the role of the land surface in the concurrent North American Monsoon System. SMEX04 was conducted over two regions: Arizona — semi-arid climate with sparse vegetation and moderate topography, and Sonora (Mexico) — moderate vegetation with strong topographic gradients. The Polarimetric Scanning Radiometer (PSR/CX) was flown on a Naval Research Lab P-3B aircraft as part of SMEX04 (10 dates of coverage over Arizona and 11 over Sonora). Radio Frequency Interference (RFI) was observed in both PSR and satellite-based (AMSR-E) observations at 6.92 GHz over Arizona, but no detectable RFI was observed over the Sonora domain. The PSR estimated soil moisture was in agreement with the ground-based estimates of soil moisture over both domains. The estimated error over the Sonora domain (SEE = 0.021 cm³/cm³) was higher than over the Arizona domain (SEE = 0.014 cm³/cm³). These results show the possibility of estimating soil moisture in areas of moderate and heterogeneous vegetation and high topographic variability.     

基于MODIS和AMSR-E遥感数据的土壤水分降尺度研究

微波传感器获得的土壤水分产品空间分辨率一般都很粗,而流域尺度上的研究需要中高分辨率的土壤水分数据。用MODIS逐日地表温度产品MOD11A1和逐日地表反射率产品MOD09GA构建温度-植被指数特征空间,并计算得到TVDI(Temperature Vegetation Dryness Index)指数,它与土壤水分呈负相关关系,能够反映土壤水分的空间分布模式,但并不是真实的土壤水分值。在AMSR-E像元尺度上求得TVDI与土壤水分的负相关系数,进而对VUA AMSR-E土壤水分产品进行降尺度计算得到0.01°分辨率的真实土壤水分值。经NAFE06(The National Airborne Field Experiment 2006)试验地面采样数据验证,降尺度后的土壤水分均方根误差平均值为6.1%。     

集群系统中自适应负载反馈平衡策略的研究

当前在集群系统中，负载平衡策略虽然很多，但是为了减少反馈开销，一般策略为采用在前端估计后端负载，所以不能很好地完成负载平衡的任务。针对这一问题，提出了一种自适应负载反馈平衡策略，各个服务器根据自身负载的变化来决定负载反馈的时机，前端根据负载信息和请求率计算出各个服务器的负载权值，最后根据负载权值来调度服务器处理请求，以实现负载平衡。由于采用了自适应的反馈策略，在获得各个服务器负载信息的同时减少了负载反馈的开销，实现了系统的负载均衡。测试结果表明该策略表现出了一定的优势。     

Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe

Global soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. Active and passive microwave sensors are generally considered as the best technologies for retrieving soil moisture from space. The Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) on-board the Aqua satellite and the Advanced SCATterometer (ASCAT) on-board the MetOp (Meteorological Operational) satellite are among the sensors most widely used for soil moisture retrieval in the last years. However, due to differences in the spatial resolution, observation depths and measurement uncertainties, validation of satellite data with in situ observations and/or modelled data is not straightforward. In this study, a comprehensive assessment of the reliability of soil moisture estimations from the ASCAT and AMSR-E sensors is carried out by using observed and modelled soil moisture data over 17 sites located in 4 countries across Europe (Italy, Spain, France and Luxembourg). As regards satellite data, products generated by implementing three different algorithms with AMSR-E data are considered: (i) the Land Parameter Retrieval Model, LPRM, (ii) the standard NASA (National Aeronautics and Space Administration) algorithm, and (iii) the Polarization Ratio Index, PRI. For ASCAT the Vienna University of Technology, TUWIEN, change detection algorithm is employed. An exponential filter is applied to approach root-zone soil moisture. Moreover, two different scaling strategies, based respectively on linear regression correction and Cumulative Density Function (CDF) matching, are employed to remove systematic differences between satellite and site-specific soil moisture data. Results are shown in terms of both relative soil moisture values (i.e., between 0 and 1) and anomalies from the climatological expectation.Among the three soil moisture products derived from AMSR-E sensor data, for most sites the highest correlation with observed and modelled data is found using the LPRM algorithm. Considering relative soil moisture values for an ~ 5 cm soil layer, the TUWIEN ASCAT product outperforms AMSR-E over all sites in France and central Italy while similar results are obtained in all other regions. Specifically, the average correlation coefficient with observed (modelled) data equals to 0.71 (0.74) and 0.62 (0.72) for ASCAT and AMSR-E-LPRM, respectively. Correlation values increase up to 0.81 (0.81) and 0.69 (0.77) for the two satellite products when exponential filtering and CDF matching approaches are applied. On the other hand, considering the anomalies, correlation values decrease but, more significantly, in this case ASCAT outperforms all the other products for all sites except the Spanish ones. Overall, the reliability of all the satellite soil moisture products was found to decrease with increasing vegetation density and to be in good accordance with previous studies. The results provide an overview of the ASCAT and AMSR-E reliability and robustness over different regions in Europe, thereby highlighting advantages and shortcomings for the effective use of these data sets for operational applications such as flood forecasting and numerical weather prediction.     

风云三号微波成像仪积雪参数反演算法初步研究

选择新疆地区作为实验区,为风云三号(FY-3)微波成像仪(MWRI)发展中国区域的积雪参数半经验反演算法。使用2003年4个月的新疆地区的台站观测资料和AMSR-E 18.7 GHz,36.5GHz和89 GHz水平和垂直极化亮温作为FY-3 MWRI的模拟数据,在Chang建立的半经验模型的基础上,采用多元线性回归分析,建立一个新算法。用已有方法去除水体、降雨、湿雪、冻土的像元后,用新算法反演了新疆地区的2004年1月的积雪参数,并分别与AMSR-E雪水当量产品和台站观测值进行比较,结果表明新算法在新疆地区优于AMSR-E的反演算法。     

松嫩平原玉米带土壤水分利用率研究

通过对自1995-1998年4年的田间及微区试验结果进行总结统计,阐述了松嫩平原春玉米带土壤水分利用率的变化特性及其相应的影响因素,阐述了气候、种植方式、管理水平、土壤肥力及区域与品种类型间对玉米带水分利用率的影响趋势及特性,以期对这一区域的玉米种植提供指导性参考依据。