土壤水分微波反演方法进展和发展趋势

土壤水分是连接地表水循环和能量循环的关键参量,精确获取该参量对于理解气候变化、地表水文过程、地气间能量交换机理等具有重要意义。微波遥感由于其较为合适的探测深度和坚实的理论基础在观测地表浅层土壤水分上具有很大优势,结合反演方法可以获取空间连续的土壤水分含量,有助于更加客观认知土壤水分的时空演变机理。随着微波遥感数据的不断丰富,多种微波遥感土壤水分反演方法相继涌现,为了更好地了解其发展和趋势,本文总结了当前土壤水分微波反演常用的卫星遥感数据并分析其发展趋势,后从主动微波反演、被动微波反演和多源协同反演3个方面梳理了各类土壤水分微波反演方法的原理、发展和优缺点,最终总结出目前微波遥感土壤水分反演方法的发展趋势：即土壤水分微波反演方法的时空普适性逐渐增强、面向高时空分辨率的土壤水分微波协同反演方法快速发展以及土壤水分微波反演方法的智能化水平不断提高。     

热红外地表方向性辐射温度与半球辐射温度关系研究

地表温度是陆面过程的一个重要影响因素,利用地表温度的遥感反演算法只能获取卫星传感器观测角度条件下的地表温度（即某个方向上的辐射温度）,但地球表面普遍存在非同温像元,反演得到的像元地表辐射温度具有方向性特征。本文利用热红外辐射传输模型4 SAIL（Scattering by Arbitrarily Inclined Leaves）,以及方向性热辐射参数化模型,针对非同温均匀冠层,考虑冠层结构、太阳位置和观测角等因素的影响,模拟得到方向性辐射温度数据,与半球辐射温度数据比较,得到估算半球辐射温度的最佳观测角度。此外,开展热红外地面观测试验,对热红外地表辐射温度的角度效应,以及利用模拟数据得到的半球辐射温度最佳观测角度进行了验证。结果表明,当太阳高度角较低时,均匀草地的地表辐射温度,会随着观测天顶角的增大而增加,受观测方位角的影响较小,当观测天顶角为75°时,倾斜观测与垂直观测得到的辐射温度差值达到2.7 K,说明热辐射存在明显的方向性特征。同时,将热红外地表方向性辐射温度与同步观测的半球辐射温度进行对比分析,当叶面积指数小于1.0时,半球辐射温度的最佳替代角度为51°,与模拟结果相符。     

城市地表热辐射方向性研究进展综述

热辐射方向性是指从不同方向观测同一目标地物时,测得的热辐射值各异的现象,通常体现为方向性辐亮度或亮温各异。随着高空间分辨率遥感数据的出现,面对高精度地表温度产品的需求,热辐射方向性效应不可忽视,如今已成为热红外遥感重点关注的问题之一。对于物质组成不均一、几何结构复杂的城市地表来说,热辐射方向性尤为显著。本文整理、分析了在城市地区开展的一系列热辐射方向性观测试验和正演模型,其中也包括一些对地表温度真值的有益探索;并对城市热辐射方向性强度的影响因素进行了归纳,包括观测季节与时间、地表几何结构、材料自身的物理属性、观测角度、视场角等,这些因素会使热辐射方向性的强度呈现出一定的时空规律性。最后,针对提高城市地表温度的反演精度、如何更好地开展城市热辐射方向性研究提出了5点展望。     

GPT3模型反演GNSS大气可降水量精度评定

采用中国区域2017~2018年与GNSS站并址的49个探空站资料对GPT3模型估算的气象参数的精度进行评估,再利用49个GNSS站结合GPT3模型估算的气象参数反演日均大气可降水量PWV,并采用与GNSS站并址的探空站数据对其精度进行评定。实验得出：1）在中国地区,1°分辨率的GPT3模型的精度和稳定性优于5°分辨率,其气压、气温和大气加权平均温度T_m的偏差均值分别为0.73 hPa、1.34 K和－1.67 K,均方根误差均值分别为4.21 hPa、3.75 K和4.15 K;2）利用GPT3模型提供的气温结合Bevis经验公式反演的PWV与GPT3模型提供的Tm反演的PWV精度相当,且2种方法反演的PWV和探空资料实测地表温度反演的PWV呈现很好的一致性,在我国青藏高原和西北地区反演PWV的精度优于我国南方和北方地区。     

卫星红外遥感技术在我国环保领域中的应用与发展分析

本文较为系统地分析了卫星红外遥感技术在我国秸秆焚烧、沙尘、气溶胶、颗粒物、灰霾等大气环境遥感监测,水华、水质参数、水表温度、热污染、核电厂温排水等水环境遥感监测,以及土壤含水量、地表温度、干旱、城市热岛效应等生态环境遥感监测的应用。同时,指出目前国产卫星红外载荷业务化应用程度不高、辐射定标能力不足、应用反演算法原创性不强、地面观测和试验验证能力不足等问题;并提出需大力发展国产红外传感器、提高辐射定标能力、发展国内原创监测算法、建设大型环保应用综合试验场等建议,以促进红外遥感技术在环境保护领域的应用和发展。     

地表温度的多源遥感数据反演算法对比分析

 多源遥感数据的综合应用是提高地表温度反演精度的有效途径.MODIS数据和Landsat TM数据在我国同一地区获取的时间相差不大,可以获取近似同步的MODIS数据和TM数据.本文将基于MODIS数据反演的大气参数应用于TM影像的地表温度反演,分别对单窗口算法和普适性单通道算法进行了实验研究,应用气象站实测的地表温度数据对反演结果进行了检验,并对比分析了不同土地覆盖条件下两种算法的精度差异.结果表明:两种算法反演精度均较高,单窗口算法反演精度为0.76K,普适性单通道算法反演精度为1.23K;在不同的土地覆盖条件下,两种算法表现出明显的差异性,水体区反演结果差异最小,均值差异仅为0.02K,植被区差异最大,均值差异为0.62K.     

利用GNSS反射信号监测海面高度变化——基于法国BRST站2019～2021年数据

GNSS反射测量(GNSS-R)技术凭借其数据来源广泛、低成本、高时空分辨率等优势,在地表与海洋环境监测等方面展现巨大潜力,已成为海面高度(SSH)反演的重要技术途径。现有研究大多聚焦于3～6个月内的短期GPS潮位反演,难以反映海面高度的季节性变化及年际特征,且在动态海面改正时仅考虑了垂向速度的影响,忽视了海面波动的垂向加速度,导致低潮位与高潮位的反演精度较差。基于此,以法国某一岸基跟踪站——BRST站为例,利用其连续3年的BDS/GPS/GLONASS/Galileo四系统反射信号,通过Lomb-Scargle谱分析和二阶动态潮位改正模型,采取稳健回归策略反演海面高度,并将最终结果与验潮站观测值进行对比,分析潮位变化趋势。结果表明：GNSS-R技术反演结果与验潮站观测值具有较好的一致性,反演精度有逐年提升的趋势,均方根误差(RMSE)为7.57 cm,相关系数为0.935;海面高度的季节性变化特征明显,秋、冬季平均海面高度偏高,夏季平均海面高度偏低,且海面高度的季节性变化与温度的季节性变化存在着相反的趋势;M2、S2、K1、O1、N2、K2、P1、Q1、M4等9个分潮的振幅差为0.0...     

玉米叶面积指数的CHRIS/PROBA数据反演分析

叶面积指数(LAI)是衡量植被生态状况和估算作物产量的一个重要指标。LAI的反演是定量遥感研究的重要内容。传统的经验统计反演方法基于单一观测角度的遥感数据进行,忽略了地物反射率的方向性。若在反演中加入多观测角度的信息,则有可能提升LAI反演的精度。以2008年甘肃省张掖市玉米实验区为研究区,利用欧空局的CHRIS/PROBA多角度高光谱数据对比分析了传统植被指数NDVI、RVI、EVI的变化规律及其反演玉米叶面积指数LAI的精度,并根据NDVI随观测角度的变化规律,构造出新型多角度归一化植被指数MNDVI,分别对实测叶面积指数进行线性回归并利用实测数据对估算LAI进行精度验证,结果表明：新型MNDVI指数相比于传统NDVI、RVI、EVI对LAI的反演精度有了显著提升,估算模型决定系数R²达到0.716,精度验证均方根误差为0.127,平均减小了33.3%。     

基于RTTOV模式的大气二氧化碳反演参数敏感性分析

大气二氧化碳是开展全球气候变化和碳循环研究的关键数据。卫星遥感技术与模式模拟相结合的反演方法已成为获取该数据的重要手段,但模式输入参数本身的误差会对大气二氧化碳反演精度产生影响,须在反演算法设计中加以关注。本文利用RTTOV10快速辐射传输模式模拟Aqua/AIRS红外探测仪17个大气二氧化碳反演通道,计算了这些通道上大气顶出射辐射对温度廓线、臭氧廓线、水汽廓线、地表温度和地表发射率的参数误差的不确定性,并与二氧化碳增加0.5%时造成的不确定性进行对比,分析二氧化碳对上述参数误差的敏感性。结果表明,温度廓线误差是干扰AIRS大气二氧化碳反演的主要因素,其次是臭氧廓线误差,而水汽廓线、地表温度和地表发射率的误差对二氧化碳反演的影响在除去个别通道后可忽略不计。最后,本文以通道为单位,确定了各通道上的高敏感参数、敏感参数和不敏感参数,为二氧化碳反演通道的选择和反演算法的设计提供了参考。     

细粒子气溶胶光学厚度和谱分布偏振的反演

 细粒子气溶胶物理和光学参数定量卫星遥感反演一直是环境和气候领域研究人员关注的重要课题。气溶胶参数卫星业务遥感产品主要是反演气溶胶光学厚度,它体现大气中气溶胶总含量的信息,而获取气溶胶谱分布函数有助于进一步了解气溶胶物理特性,并提高气溶胶其他参数的遥感探测精度。目前,陆地气溶胶卫星反演面临两个关键问题:一是气溶胶模式;二是地表反射贡献的去除,偏振遥感在这两方面有其独有的优势。本文以多角度偏振方法,采用RT3辐射传输模型建立矢量查找表,利用法国PARASOL探测器一级数据反演了京津唐地区的细粒子气溶胶光学厚度和谱分布参数,并使用AERONET地基观测数据对反演结果进行验证,结果表明:偏振方法能较高精度地实现细粒子气溶胶光学厚度反演,而谱分布的反演还需进一步改进。     

A comparison of Argo nominal surface and near-surface temperature for validation of AMSR-E SST

Satellite SST(sea surface temperature) from the Advanced Microwave Scanning Radiometer for the Earth Observing System(AMSR-E) is compared with in situ temperature observations from Argo profiling floats over the global oceans to evaluate the advantages of Argo NST(near-surface temperature: water temperature less than 1 m from the surface). By comparing Argo nominal surface temperature(~5 m) with its NST, a diurnal cycle caused by daytime warming and nighttime cooling was found, along with a maximum warming of 0.08±0.36°C during 14:00–15:00 local time. Further comparisons between Argo 5-m temperature/Argo NST and AMSR-E SST retrievals related to wind speed, columnar water vapor, and columnar cloud water indicate warming biases at low wind speed(5 m/s) and columnar water vapor 28 mm during daytime. The warming tendency is more remarkable for AMSR-E SST/Argo 5-m temperature compared with AMSR-E SST/Argo NST, owing to the effect of diurnal warming. This effect of diurnal warming events should be excluded before validation for microwave SST retrievals. Both AMSR-E nighttime SST/Argo 5-m temperature and nighttime SST/Argo NST show generally good agreement, independent of wind speed and columnar water vapor. From our analysis, Argo NST data demonstrated their advantages for validation of satellite-retrieved SST.     

APPLICATION OF NONLINEAR MULTI-CHANNEL ALGORITHMS FOR ESTIMATING SEA SURFACE TEMPERATURE WITH NOAA 14 AVHRR DATA

ＩＮＴＲＯＤＵＣＴＩＯＮＳｉｎｃｅｔｈｅｅａｒｌｙ 1 970ｓ,ｔｈｅＡｄｖａｎｃｅｄＶｅｒｙＨｉｇｈＲｅｓｏｌｕｔｉｏｎＲａｄｉｏｍｅｔｅｒ(ＡＶＨＲＲ)ｏｎｂｏａｒｄｔｈｅＮａｔｉｏｎａｌＯｃｅａｎｉｃａｎｄＡｔｍｏｓｐｈｅｒｉｃＡｄｍｉｎｉｓｔｒａｔｉｏｎ (ＮＯＡＡ)ｓｅｒｉｅｓｏｆＰｏｌａｒ ｏｒｂｉｔｉｎｇＯｐｅｒａｔｉｏｎａｌＥｎ ｖｉｒｏｎｍｅｎｔａｌＳａｔｅｌｌｉｔｅｓ (ＰＯＥＳ)ｈａｓｂｅｅｎｕｓｅｄｆｏｒｓｅａｓｕｒｆａｃｅｔｅｍｐｅｒａｔｕｒｅ (ＳＳＴ)ｒｅｔｒｉｅｖａｌａｎｄｃｌｏｕ…     

Chaos particle swarm optimization combined with circular median filtering for geophysical parameters retrieval from Windsat

This paper established a geophysical retrieval algorithm for sea surface wind vector, sea surface temperature, columnar atmospheric water vapor, and columnar cloud liquid water from WindSat, using the measured brightness temperatures and a matchup database. To retrieve the wind vector, a chaotic particle swarm approach was used to determine a set of possible wind vector solutions which minimize the difference between the forward model and the WindSat observations. An adjusted circular median filtering function was adopted to remove wind direction ambiguity. The validation of the wind speed, wind direction, sea surface temperature, columnar atmospheric water vapor, and columnar liquid cloud water indicates that this algorithm is feasible and reasonable and can be used to retrieve these atmospheric and oceanic parameters. Compared with moored buoy data, the RMS errors for wind speed and sea surface temperature were 0.92 m s~(-1) and 0.88℃, respectively. The RMS errors for columnar atmospheric water vapor and columnar liquid cloud water were 0.62 mm and 0.01 mm, respectively, compared with F17 SSMIS results. In addition, monthly average results indicated that these parameters are in good agreement with AMSR-E results. Wind direction retrieval was studied under various wind speed conditions and validated by comparing to the Quik SCAT measurements, and the RMS error was 13.3?. This paper offers a new approach to the study of ocean wind vector retrieval using a polarimetric microwave radiometer.     

光学信息分解被动微波土壤湿度方法

土壤水分是一个重要生态参量,以被动微波反演土壤水分,不受天气影响,且其算法成熟.但是星载被动微波数据的空间分辨率较低,可适合大区域尺度研究.本文将1km分辨率光学数据MODIS和25km分辨率被动微波数据AMSR- E2级土壤湿度产品结合,利用NDVI-Ts特征空间,去除植被影响,结合前人提出的裸土蒸散模型,将研究区被...     

基于MODIS和AMSR-E反演京津冀地区大气水汽含量及比较

为了长时间、大范围获取水汽数值,利用2005~2008年光学遥感的MODIS近红外、红外水汽产品,以及微波遥感AMSR-E数据,2种方法反演水汽。微波AMSR-E亮温数据采用Merritt N.Deeter(2007)亮温极化差方法,选取18.7GHz和23.8GHz 2个波段,得到AMSR-E升轨、降轨大气水汽数值。以京津冀地区为研究区域,通过地统计相关性分析、时间序列分析、年际间变化分析,可知2种方法4种资料反演的大气水汽数值的R2都达到0.95,时间分布符合中国雨带移动规律,空间分布不均。MODIS数据反演值比AMSR-E值要低,得到2种方法反演水汽的各自优缺点。     

Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network

It is more difficult to retrieve land surface temperature(LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2(AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System(AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies(ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.     

Satellite retrieval of hurricane wind speeds using the AMSR2 microwave radiometer

The AMSR2 microwave radiometer is the main payload of the GCOM-W1 satellite,launched by the Japan Aerospace Exploration Agency in 2012. Based on the pre-launch information extraction algorithm,the AMSR2 enables remote monitoring of geophysical parameters such as sea surface temperature,wind speed,water vapor,and liquid cloud water content. However,rain alters the properties of atmospheric scattering and absorption,which contaminates the brightness temperatures measured by the microwave radiometer. Therefore,it is difficult to retrieve AMSR2-derived sea surface wind speeds under rainfall conditions. Based on microwave radiative transfer theory,and using AMSR2 L1 brightness temperature data obtained in August 2012 and NCEP reanalysis data,we studied the sensitivity of AMSR2 brightness temperatures to rain and wind speed,from which a channel combination of brightness temperature was established that is insensitive to rainfall,but sensitive to wind speed. Using brightness temperatures obtained with the proposed channel combination as input parameters,in conjunction with HRD wind field data,and adopting multiple linear regression and BP neural network methods,we established an algorithm for hurricane wind speed retrieval under rainfall conditions. The results showed that the standard deviation and relative error of retrievals,obtained using the multiple linear regression algorithm,were 3.1 m/s and 13%,respectively. However,the standard deviation and relative error of retrievals obtained using the BP neural network algorithm were better(2.1 m/s and 8%,respectively). Thus,the results of this paper preliminarily verified the feasibility of using microwave radiometers to extract sea surface wind speeds under rainfall conditions.