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.     

A method for surface roughness parameter estimation in passive microwave remote sensing

Surface roughness parameter is an important factor and obstacle for retrieving soil moisture in passive microwave remote sensing.Two statistical parameters,root mean square (RMS) height (s) and correlation length (l),are designed for describing the roughness of a randomly rough surface.The roughness parameter measured by traditional way is independence of frequency,soil moisture and soil heterogeneity and just the ″geometric″ roughness of random surface.This ″geometric″ roughness can not fully explain the scattered thermal radiation by the earth's surface.The relationship between ″geometric″ roughness and integrated roughness (contain both ″geometric″ roughness and ″dielectric″ roughness) is linked by empirical coefficient.In view of this problem,this paper presents a method for estimating integrated surface roughness from radiometer sampling data at different frequencies,which mainly based on the flourier relationship between power spectral density distribution and spatial autocorrelation function.We can obtain integrated surface roughness at different frequencies by this method.Besides "geometric" roughness,this integrated surface roughness not only contains "dielectric" roughness but also includes frequency dependence.Combined with Q/H model the polarization coupling coefficient can also be obtained for both H and V polarization.Meanwhile,the simulated numerical results show that radiometer with a sensitivity of 0.1 K can distinguish the different surface roughness and the change of roughness with frequency for the same rough surface.This confirms the feasibility of radiometer sampling method for estimating the surface roughness theoretically.This method overcomes the problem of ″dielectric″ roughness measurement to some extent and can achieve the integrated surface roughness within a microwave pixel which can serve soil moisture inversion better than the ″geometric″ roughness.     

GNSS-R/IR土壤水分遥感研究现状

根据不同遥感平台，详细叙述地基、塔基、机载和星载GNSS-R技术土壤水分监测的发展现状，综述辐射计联合GNSS-R技术进行土壤水分监测的发展状态及GNSS-R地基和星载接收机的发展现状，探讨GNSS-R/IR进行土壤水分反演的重难点。     

Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center (NDBC), root mean square errors are 1.2 m/s and 30° for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.     

基于GNSS-IR双系统组合的土壤湿度多星线性回归反演模型

提出一种GPS/BDS双系统组合的土壤湿度多星线性回归反演模型,并以GNSS接收机实测数据为例,对比分析不同GPS和BDS卫星组合反演土壤湿度的效果。实验表明：1)GPS和BDS双系统组合相对于单系统在短观测时间内可以提高有效卫星数,通过多元线性回归原理可实现双系统多卫星的有效融合,提高土壤湿度反演的精度;2)当GPS和BDS组合卫星数达到6颗以上时,反演效果趋于稳定,反演结果与土壤湿度的相关系数均优于0.90,RMSE相对于单星至少提高25.8%。     

全球36 km格网土壤水分逐日估算

土壤水分是陆面生态系统和能量循环的核心变量之一,利用微波遥感技术获得的土壤水分产品的时间分辨率一般是2-3 d,因此精确地获得具有较高时间分辨率的土壤水分成了人们关注的焦点。本文尝试将SMAP (the Soil Moisture Passive and Active）土壤水分和MODIS光学数据相结合,利用广义回归神经网络进行全球36 km土壤水分的估算,提升SMAP土壤水分的时间分辨率。结果显示,广义回归神经网络估算土壤水分与SMAP保持了高相关性(r = 0.7528),但其却保留了较高的误差 (rmse = 0.0914 m³/m³)。尽管如此,估算的土壤水分能够很好地保持SMAP土壤水分的整体空间变化,并且提升了土壤水分的时间分辨率（1 d）。此处,本文研究了SMAP土壤水分与MODIS光学数据之间的关系,这对今后利用机器学习进行SMAP土壤水分降尺度研究提供了重要的参考价值。     

不同施工机械碾压下矿区复垦土壤物理性质的变化

以国家自然科学基金为依托建立模拟实验区,探究不同施工机械和碾压次数下的土壤物理指标(压实度、容重、孔隙度、含水量、电导率和温度)的变化。研究结果表明:随土层深度的增加,压实度、容重和电导率逐渐递增,孔隙度、含水量和温度逐渐递减;随碾压次数的增加,压实度和容重逐渐递增,孔隙度逐渐递减,土壤含水量、电导率和温度先增加后降低,其中使用自卸汽车时,3次碾压指标值最高,使用履带式推土机时,5次碾压指标值最高,而使用履带式推土机的处理效果要好于使用自卸汽车;通过对各处理与对照间的拟合度分析发现,使用履带式推土机碾压5次的土壤中各指标与对照拟合度最高,表明采用"履带式推土机×碾压5次"的组合,复垦土壤中物理性质与正常土壤最为接近。     

地表组合粗糙度不确定性引起的SAR反演土壤水分的不确定性分析

地表粗糙度的不确定性是引起SAR土壤水分反演结果不确定性的主要因素,现有研究大多着重于研究单个粗糙度参数（主要是相关长度）的不确定性,直接研究地表组合粗糙度不确定性的较少。本文使用偏度、峰度和四分位距3个指标来量化不确定性,通过在组合粗糙度中加入不同量级高斯噪声进行随机扰动的方法,研究组合粗糙度不确定性在反演过程中的传递,并对反演土壤水分的不确定性进行定量分析。进一步研究反演土壤水分的均方根误差对组合粗糙度不同比例误差范围的响应特征,得到满足反演精度要求的组合粗糙度误差控制范围。样区的实验分析结果表明：组合粗糙度高斯噪声标准差在0-0.045之间时,峰度取值从-0.1984到1.2501,偏度取值从0.0191到0.6791,四分位距取值从0.0018到0.0167,3个量化指标都随组合粗糙度高斯噪声量级的增大而增大,土壤水分反演值有集中在众数附近的趋势,土壤水分低估倾向比高估倾向更明显;本文提出的组合粗糙度误差控制范围可满足反演精度要求,误差控制范围与入射角负相关。     

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。     

A new algorithm for sea-surface wind-speed retrieval based on the L-band radiometer onboard Aquarius

Aquarius is the second satellite mission to focus on the remote sensing of sea-surface salinity from space and it has mapped global sea-surface salinity for nearly 3 years since its launch in 2011. However,benefiting from the high atmospheric transparency and moderate sensitivity to wind speed of the L-band brightness temperature(TB),the Aquarius L-band radiometer can actually provide a new technique for the remote sensing of wind speed. In this article,the sea-surface wind speeds derived from TBs measured by Aquarius' L-band radiometer are presented,the algorithm for which is developed and validated using multisource wind speed data,including Wind Sat microwave radiometer and National Data Buoy Center buoy data,and the Hurricane Research Division of the Atlantic Oceanographic and Meteorological Laboratory wind field product. The error analysis indicates that the performance of retrieval algorithm is good. The RMSE of the Aquarius wind-speed algorithm is about 1 and 1.5 m/s for global oceans and areas of tropical hurricanes,respectively. Consequently,the applicability of using the Aquarius L-band radiometer as a near all-weather wind-speed measuring method is verified.     

Advances in Research on Soil Moisture by Microwave Remote Sensing in China

Soil moisture is an important factor in global hydrologic circulation and plays a significant role in the research of hydrology, climatology, and agriculture. Microwave remote sensing is less limited by climate and time, and can measure in large scale. With these characteristics, this technique becomes an effective tool to measure soil moisture. Since the 1980s, Chinese researchers have investigated the soil moisture using microwave instruments. The active re- mote sensors are characteristic of high spatial resolution, thus with launch of a series of satellites, active microwave remote sensing of soil moisture will be emphasized. The passive microwave remote sensing of soil moisture has a long research history, and its retrieval algorithms were developed well, so it is an important tool to retrieve large scale moisture information from satellite data in the future.     

Estimating purple-soil moisture content using Vis-NIR spectroscopy

Soil moisture is essential for plant growth in terrestrial ecosystems. This study investigated the visible-near infrared(Vis-NIR) spectra of three subgroups of purple soils(calcareous, neutral, and acidic) from western Chongqing, China, containing different water contents. The relationship between soil moisture and spectral reflectivity(R) was analyzed using four spectral transformations, and estimation models were established for estimating the soil moisture content(SMC) of purple soil based on stepwise multiple linear regression(SMLR) and partial least squares regression(PLSR). We found that soil spectra were similar for different moisture contents, with reflectivity decreasing with increasing moisture content and following the order neutral calcareous acidic purple soil(at constant moisture content). Three of the four spectral transformations can highlight spectral sensitivity to SMC and significantly improve the correlation between the reflectance spectra and SMC. SMLR and PLSRmethods provide similar prediction accuracy. The PLSR-based model using a first-order reflectivity differential(R ?) is more effective for estimating the SMC, and gave coefficient of determination(v2), root mean square errors of validation(RMSEV), and ratio of performance to inter-quartile distance(RPIQ)values of 0.946, 1.347, and 6.328, respectively, for the calcareous purple soil, and 0.944, 1.818, and 6.569,respectively, for the acidic purple soil. For neutral purple soil, the best prediction was obtained using the SMLR method with R ? transformation, yieldingv2,RMSEV and RPIQ values of 0.973, 0.888 and 8.791,respectively. In general, PLSR is more suitable than SMLR for estimating the SMC of purple soil.     

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...     

克里格法的土壤水分遥感尺度转换

 尺度效应往往会制约着定量遥感反演的精度,对地学信息进行空间尺度转换是生产实践的必然要求,而常用的尺度转换模型多利用光谱数据进行差值计算,不适合升尺度和降尺度转换。由于土壤含水量数据具有区域变化量的随机性和结构性特点,本文以15m分辨率的ASTER图像像元为基本单元,采用点克里格法完成ASTER 15m至7.5m分辨率的土壤含水量数据降尺度转换,从分维数的相似程度上来看,转换结果是合理的;并利用块状克里格法对地面实测样点数据进行点到7.5m分辨率的面数据升尺度转换,将升尺度和降尺度转换结果与实测样点均值相比较,结果表明:7.5m分辨率的实测样点土壤水均值误差在1.5782-5.019之间,块状克里格法获取的升尺度土壤含水量数据与点克里格法获取的降尺度土壤含水量数据之间误差则为1.2825-5.0481,可见克里格法考虑了点与周边的关系,所获得的土壤含水量值要优于未考虑空间异质性的土壤含水量平均值。     

融入土壤湿度指标的青藏高原近地表土壤冻融机器学习监测算法

青藏高原作为中低纬度地区最大的高山冻土区,多年冻土和季节冻土广泛分布。高精度的地表冻融监测结果对研究该区域的水热交换、碳氮循环和土壤冻融侵蚀非常重要。本文基于4个青藏高原典型地区的土壤温湿度观测网数据,开展利用LightGBM算法和随机森林算法进行土壤冻融循环监测的研究。在构建土壤冻融监测模型的过程中,发现土壤湿度是影响冻融判别的一个关键因子。使用AMSR2亮温数据和ERA5-Land土壤湿度数据,基于两种机器学习算法判别地表冻融状态,将结果与传统冻融判别式算法进行对比分析。结果表明：相比冻融判别式算法,LightGBM算法在白天和夜间的总体判对率提高了12.09%;14.45%,随机森林算法在白天和夜间的总体判对率提高了13.23%和14.96%。近80%的错分样本分布在-4.0 ℃~4.0 ℃之间,说明2个机器学习算法能够识别出稳定的土壤冻结状态和融化状态。另外,LightGBM算法和随机森林算法得到的日冻融转换天数的平均RMSE降低了112.82和117.00;冻结天数的平均RMSE降低了47.87和53.96;融化天数的平均RMSE降低了37.10和39.80。同时,基于随机森林算法计算了2014年7月—2015年6月青藏高原冻结天数、融化天数、日冻融转换天数。得到的青藏高原冻结天数图,以中国冻土区划图为参考进行精度评价,总体分类精度为96.78%。     

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.     

Assimilating ASAR data for estimating soil moisture profile using an ensemble Kalman filter

Active microwave remote sensing data were used to calculate the near-surface soil moisture in the vegetated areas. In this study, Advanced Synthetic Aperture Radar （ASAR） observations of surface soil moisture content were used in a data assimilation framework to improve the estimation of the soil moisture profile at the middle reaches of the Heihe River Basin, Northwest China. A one-dimensional soil moisture assimilation system based on the ensemble Kalman filter （EnKF）, the forward radiative transfer model, crop model, and the Distributed Hydrology-Soil-Vegetation Model （DHSVM） was developed. The crop model, as a semi-empirical model, was used to estimate the surface backscattering of vegetated areas. The DHSVM is a distributed hydrology-vegetation model that explicitly represents the effects of topography and vegetation on water fluxes through the landscape. Numerical experiments were con- ducted to assimilate the ASAR data into the DHSVM and in situ soil moisture at the middle reaches of the Heihe River Basin from June 20 to July 15, 2008. The results indicated that EnKF is effective for assimilating ASAR observations into the hydrological model. Compared with the simulation and in situ observations, the assimilated results were significantly improved in the surface layer and root layer, and the soil moisture varied slightly in the deep layer. Additionally, EnKF is an efficient approach to handle the strongly nonlinear problem which is practical and effective for soil moisture estimation by assimilation of remote sensing data. Moreover, to improve the assimilation results, further studies on obtaining more reliable forcing data and model parameters and increasing the efficiency and accuracy of the remote sensing observations are needed, also improving estimation accuracy of model operator is important.     

Factors affecting soil respiration in reference with temperature’s role in the global scale

Soil respiration is CO₂ evolution process from soil to atmosphere, mainly produced by soil micro-organism and plant roots. It is affected not only by biological factors (vegetation, micro-organism, etc.) and environmental factors (temperature, moisture, pH, etc.), but also more and more strongly by man-made factors. Based on literature survey, main factors affecting soil respiration were reviewed. The relationship of soil respiration to latitude and to mean annual temperature were analyzed by using the data measured from forest vegetation in the world. As a result, soil respiration rate decreased exponentially with an increase of latitude, and increased with increasing temperature. Following the relationship between soil respiration and temperature, Q₁₀ value (law of Van Hoff) was obtained as 1.57 in the global scale. This project was supported by National Natural Science Foundation of China to FJY (No. 3940003).     

FACTORS AFFECTING SOIL RESPIRATION IN REFERENCE WITH TEMPERATURE'S ROLE IN THE GLOBAL SCALE

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