EXPERIMENTAL STUDY ON SOIL MOISTURE USING DUAL-FREQUENCY MICROWAVE RADIOMETER

1INTRODUCTIONSoil is the basis of human's living. Soil moisture plays asignificant role in studying the matter and energy ex-changes in global hydrology sphere. The evaporation ofsoil moisture has an influence on the water vapor cycle.Meanwhile soil moisture is also one of the firsthandmeasurable parameters in crop yield estimation and wa-ter resources management (JACKSON et al., 1993). Theinfluence of the interaction of land and atmosphere onsoil moisture can bring about anomalous cli…     

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.     

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。     

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。     

农田表面粗糙度参数的测量与精度分析

表面粗糙度是有效解释雷达后向散射系数和微波辐射亮度温度的关键参数之一。表面粗糙度参数的测量精度受到测量方法、测量仪器、数据预处理等的影响,如何获取到表面粗糙度的"真"值是地表粗糙度测量急需解决的问题,并且有助于提高利用微波遥感技术反演地表参数的能力。本文利用激光扫描仪的二维高度数据和蒙特卡罗方法模拟的一维表面高度数据,分析了重复采样次数、采样间隔、采样剖面长度、空间自相关函数类型和大尺度结构(数据倾斜和农田垄行结构)对表面粗糙度精度的影响,研究表明:在大于20次重复采样、小于10mm的采样间隔、200倍相关长度的剖面长度的条件下,农田表面粗糙度参数的测量精度约为80%;分形相关函数与实测农田表面的空间自相关系数的吻合性要高于高斯函数和指数函数;数据倾斜和农田垄行结构严重影响表面粗糙度参数的结果,在进行表面粗糙度参数的计算之前,需从剖面高度分布数据中去除以上两个因素的影响。     

基于SVR的旱区稀疏植被覆盖下土壤水分遥感反演

中国西北半干旱区降水稀少、蒸散强烈,土壤水分作为重要的生态因子,影响着土壤-大气界面的能量平衡。支持向量回归模型具有估算精度高、可处理非线性问题、泛化能力强等优点,近年来被应用于土壤水分反演研究中,但已有模型极少考虑地表粗糙度因素的影响,导致反演精度受到一定限制。因此,本文以内蒙古乌审旗为研究区,采用水云模型去除地表稀疏植被覆盖的影响,提取全极化Radarsat-2 SAR影像裸土后向散射系数（ {\sigma }_{\mathrm{soil}}^0 ）,并利用AIEM模型和Oh模型建立后向散射系数数据库,采用LUT法模拟地表有效粗糙度参数,构建基于支持向量回归的土壤水分反演模型,并系统地对比分析了不同极化方式的后向散射系数作为数据源的土壤水分反演结果。研究结果表明：不考虑粗糙度参数的单数据源作为模型参数时,同极化数据反演结果比交叉极化具有更高的反演精度;当模型参数为考虑粗糙度的多源数据时,不同极化数据的反演精度均有所提高,其中数据源为 {\sigma }_{\mathit{vv}}^0 和粗糙度参数时,反演结果最好（R ²=0.917,MAE=3.980%,RMSE=5.187%）。研究结果可为旱区稀疏植被覆盖地表土壤水分的遥感监测提供技术支持。     

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。     

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...     

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。     

基于GF-3和Landsat 8遥感数据的土壤水分反演研究

基于我国首颗全极化雷达卫星高分三号（GF-3）和Landsat8数据,研究浓密植被覆盖地表土壤水分反演方法。为了提高浓密植被覆盖地表土壤水分反演精度,首先利用PROSAIL模型、实测植被参数及Landsat8光学数据分析了8种植被指数与植被冠层含水量的相关性,从中优选出归一化差异水指数（NDWI₅）用于反演植被冠层含水量,并通过分析植被含水量和植被冠层含水量的关系,构建植被含水量模型;然后结合植被含水量反演模型和简化MIMICS模型校正了植被对雷达后向散射系数的影响,最后基于AIEM建立裸土后向散射系数模拟数据集,发展一种主动微波和光学数据协同反演浓密植被覆盖地表土壤水分模型,并以山东省禹城市为研究区,实现了玉米覆盖下HH、VV和HH+VV 3种模式土壤水分反演。实验结果表明： ① NDWI₅为最佳植被指数,对于去除植被影响有较好效果;② 基于此方法,利用GF-3和Landsat8卫星数据反演得到的土壤水分具有较高的精度;③ 相比HH和VV两种极化模式,HH+VV双通道模式对土壤水分反演结果更好,决定系数（R2）为0.4037,均方根误差（RMSE）为0.0667 m ³m ^-3。     

基于GNSS+R裸土延迟多普勒图理论研究

侧重散射机理研究,以GPS相关函数和相关功率模型为基础,主要通过对双站雷达散射截面计算模型进行修改,得到裸土的GPS散射信号模型。其中裸土的双站圆极化雷达散射截面采用双站随机粗糙面散射模型计算得到,利用极化合成的方法使模型具备全极化计算功能。通过修改后的模型,模拟分析不同极化下裸土参数（土壤水分和地表粗糙度）对延迟多普勒图DDM（delay Doppler map）的影响。该理论模型研究有助于机载/星载GNSS-R接收机数据解释、GNSS-R实验设计以及后向反演算法的开发。同时,延迟多普勒图模型的研究结果也为GNSS+R土壤水分遥感研究提供了一定的理论依据。     

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.     

Effects of elevated air temperatures on soil thermal and hydrologic processes in the active layer in an alpine meadow ecosystem of the Qinghai-Tibet Plateau

In this study,effects of elevated air temperatures on thermal and hydrologic process of the shallow soil in the active layer were investigated. Open-top chambers(OTCs)were utilized to increase air temperatures 1-2℃ in OTC-1 and 3-5℃ in OTC-2 in the alpine meadow ecosystem on the Qinghai- Tibetan Plateau.Results show that the annual air temperatures under OTC-1 and OTC-2 were 1.21℃ and 3.62℃ higher than the Control,respectively.The entirely-frozen period of shallow soil in the active layer was shortened and the fully thawed period was prolonged with temperature increase.The maximum penetration depth and duration of the negative isotherm during the entirely-frozen period decreased, and soil freezing was retarded in the local scope of the soil profile when temperature increased.Meanwhile, the positive isotherm during the fully-thawed period increased,and the soil thawing was accelerated.Soil moisture under different manipulations decreased with the temperature increase at the same depth. During the early freezing period and the early fully- thawed period,the maximum soil moisture under the Control manipulation was at 0.2 m deep,whereas under OTC-1 and OTC-2 manipulations,the maximum soil moisture were at 0.4-0.5 m deep. These results indicate that elevated temperatures led to a decrease of the moisture in the surface soil.The coupled relationship between soil temperature and moisture was significantly affected by the temperature increase.During the freezing and thawing processes, the soil temperature and moisture under different manipulations fit the regression model given by the equationθV=a/{1+exp[b(TS+c)]}+d.     

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.     

微波与光学遥感协同反演旱区地表土壤水分研究

土壤水分是水文循环中的关键因素,尤其对旱区的生态环境具有十分重要的意义。微波遥感是反演土壤水分的有效手段,而植被是影响土壤水分反演精度的重要因素。因此,对土壤水分的反演需要考虑植被的影响。本文以内蒙古乌审旗为研究区,利用Radarsat-2雷达数据与TM光学数据,对旱区稀疏植被覆盖地表土壤水分反演进行研究。利用TM数据,分别选取NDVI和NDWI指数对植被含水量进行反演,通过水云模型消除植被层对土壤后向散射系数的影响;在此基础上,根据研究区地表植被特性,提出一种基于AIEM 模型的反演土壤水分的改进算法,反演了不同粗糙度参数、不同极化（VV极化和HH极化）条件下的研究区土壤水分。反演结果与野外实测数据的对比结果表明,本文提出的基于地表植被特性的土壤水分改进算法,具有更好的适应性;土壤水分反演模式\begin{array}{c}M{v}_{\mathit{\sigma vv}\left(1\right)}^{\mathit{lh}}\end{array}（VV极化方式下采用NDVI去除植被影响的反演模式）更适合于旱区考虑稀疏植被覆盖影响的地表土壤水分的反演。     

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%。     

The intrinsic acidity constants and specific surface area of marine solid particles

This paper deals with the infrared spectra of “amino acid-clay, calcium carbonate and γ-AlOOH” and “Cu(II)-clay-amino acid” model systems, and shows that the model of the ternary surface complex is M-OHLCu (L=amino acid) for marine solid particle-Cu (II)-amino acid. Study of the formation mechanism of the ternary surface complex shows that the specific surface area, and especially the intrinsic acidity constant, determine whether the ternary surface complex is easily formed, and that factor,F _TSC, quantifies the relationship between the promoting effect of organics on M_t-marine solid particle ion exchange and the intrinsic acidity constant and specific surface area.     

Research on heavy metals in Ruditapes philippinarum and soda industry wastes

Heavy metals pollution in Jiaozhou Bay was studied inRuditapes. philippinarum, a bioindicator of seawater pollution. Heavy metals in soda industry wastes “white mud” were also studied. Comparison of microwave digestion method with general digestion method revealed that microwave digestion is superior to general digestion in precision, recovery, digestion speed and efficiency, etc. Cd concentration in HongdaoR. philippinarum samples exceeded the national standard by 0.046 mg/kg, that of Yinghai sample by 0.02 mg/kg, and that of Hongshiya sample by 0.22 mg/kg. Sample Pb concentration in Hongshiya was found to exceed the national standard by 0.02 mg/kg. However the heavy metals concentration inR. philippinarum near the Qingdao Alkaline Factory was complied with the standard. This was proved byPenaeus chinensis culture experiment. Therefore, the possible contamination source may come from other land areas. This study is supported by Project 973 (NO.2002CB 412402), and key project of the Chinese Academy of Sciences (Grant NO.KJCX315W-215).     

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.     

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.