卫星遥感反演土壤水分研究综述

土壤水分是影响地表过程的核心变量之一。精准地测量土壤水分及其时空分布,长期以来是定量遥感研究领域的难点问题。简要回顾基于光学、被动微波、主动微波和多传感器联合反演等卫星遥感反演土壤水分的主要反演算法、存在的难点和前沿性研究问题,介绍了应用土壤水分反演算法所形成的3种主要全球土壤水分数据集,包括欧洲气象业务卫星（ERS/MetOp）数据集、高级微波扫描辐射计（AMSR-E）数据集、土壤湿度与海洋盐分卫星（SMOS）数据集,并结合目前存在的问题探讨卫星遥感反演土壤水分研究的发展趋势。     

Remote sensing of soil moisture: implications for groundwater recharge

Remote sensing provides information on the land surface. Therefore, linkages must be established if these data are to be used in groundwater and recharge analyses. Keys to this process are the use of remote sensing techniques that provide information on soil moisture and water-balance models that tie these observations to the recharge. Microwave remote sensing techniques are used to map the spatial domain of surface soil moisture and to monitor its temporal dynamics, information that cannot be measured using other techniques. The physical basis of this approach is presented with examples of how microwave remote sensing is utilized in groundwater recharge and related studies. Electronic Publication     

联合机载PLMR微波辐射计和MODIS产品反演黑河中游张掖绿洲土壤水分研究

土壤水分是气候、水文学研究中的重要变量,微波遥感是获取区域地表土壤水分的重要手段,而L波段更是微波土壤水分反演的最优波段。依托HiWATER黑河中游绿洲试验区的地面观测及机载PLMR微波辐射计亮温数据,利用微波辐射传输模型L-MEB,并将MODIS地表温度产品(MOD11A1)和叶面积指数产品(MYD15A2)作为模型及反演中的先验辅助信息,借助LM优化算法,通过PLMR双极化多角度的亮温观测,针对土壤水分、植被含水量(VWC)和地表粗糙度这3个主要参数,分别进行土壤水分单参数反演、土壤水分与VWC或粗糙度的双参数反演以及这3个参数的同时反演。通过对不同反演方法的比较可以得出结论,多源辅助数据及PLMR双极化、多角度信息的应用可以显著降低反演的不确定性,提高土壤水分反演精度。证明在合理的模型参数和反演策略下,SMOS的L-MEB模型和产品算法可以达到0.04 cm3/cm3的反演精度,另外无线传感器网络可以在遥感产品真实性检验中起到重要作用。     

遥感技术在陆面过程研究中的应用进展

探讨了当前陆面过程 (LSP)研究的特点 ,指出遥感在陆面过程研究中的应用以及陆面过程国际合作实验是突出的特点 ,进而对遥感技术的陆面参数获取、地表能量通量的计算以及与 LSP模式的结合研究及进展进行了综述。根据不同特征的地表参数选择光学遥感或微波遥感已成共识 ,而综合利用不同遥感数据获取同一种地表参数也已成为研究热点 ,当前及今后发射的携载多种遥感仪器的众多遥感卫星为此项研究提供了条件 ;遥感与 LSP模式的结合研究是遥感在陆面过程研究中深入应用的一个方面 ,国际陆面过程合作实验是这项研究的重要保证。     

Validation of two gridded soil moisture products over India with <Emphasis Type="Italic">in-situ</Emphasis> observations

Surface level soil moisture from two gridded datasets over India are evaluated in this study. The first one is the UK Met Office (UKMO) soil moisture analysis produced by a land data assimilation system based on Extended Kalman Filter method (EKF), which make use of satellite observation of Advanced Scatterometer (ASCAT) soil wetness index as well as the screen level meteorological observations. Second dataset is a satellite soil moisture product, produced by National Remote Sensing Centre (NRSC) using passive microwave Advanced Microwave Scanning Radiometer 2 measurements. In-situ observations of soil moisture from India Meteorological Department (IMD) are used for the validation of the gridded soil moisture products. The difference between these datasets over India is minimum in the non-monsoon months and over agricultural regions. It is seen that the NRSC data is slightly drier (0.05%) and UKMO soil moisture analysis is relatively wet during southwest monsoon season. Standard AMSR-2 satellite soil moisture product is used to compare the NRSC and UKMO products. The standard AMSR-2 and UKMO values are closer in monsoon season and AMSR-2 soil moisture is higher than UKMO in all seasons. NRSC and AMSR-2 showed a correlation of 0.83 (significant at 0.01 level). The probability distribution of IMD soil moisture observation peaks at 0.25 m³/m³, NRSC at 0.15 m³/m³, AMSR-2 at 0.25 m³/m³ and UKMO at 0.35 m³/m³ during June–September period. Validation results show UKMO analysis has better correlation with in-situ observations compared to the NRSC and AMSR-2 datasets. The seasonal variation in soil moisture is better represented in UKMO analysis. Underestimation of soil moisture during monsoon season over India in NRSC data suggests the necessity of incorporating the actual vegetation for a better soil moisture retrieval using passive microwave sensors. Both products have good agreement over bare soil, shrubs and grassland compared to needle leaf tree, broad leaf tree and urban land cover types.     

Satellite remote sensing applications for surface soil moisture monitoring: A review

Surface soil moisture is one of the crucial variables in hydrological processes, which influences the exchange of water and energy fluxes at the land surface/atmosphere interface. Accurate estimate of the spatial and temporal variations of soil moisture is critical for numerous environmental studies. Recent technological advances in satellite remote sensing have shown that soil moisture can be measured by a variety of remote sensing techniques, each with its own strengths and weaknesses. This paper presents a comprehensive review of the progress in remote sensing of soil moisture, with focus on technique approaches for soil moisture estimation from optical, thermal, passive microwave, and active microwave measurements. The physical principles and the status of current retrieval methods are summarized. Limitations existing in current soil moisture estimation algorithms and key issues that have to be addressed in the near future are also discussed.     

Research advances in passive microwave remote sensing of surface freeze-thaw state北大核心CSCD

Soil freeze-thaw cycles have important effects on surface water and energy balance,and then affect vegetation growth,soil water content,carbon cycle and terrestrial ecosystem. Passive microwave plays an important role in monitoring global and regional surface freeze-thaw processes due to its high temporal resolution,abundant data and sensitivity to soil moisture. With the launch of passive microwave sensors at home and abroad,it provides conditions for the study of permafrost interannual variation,seasonal variation,diurnal variation and long time series of near-surface soil freeze-thaw cycle. In recent years,the study of surface freeze-thaw cycle using passive microwave data has gradually increased. Based on previous studies,this paper summarizes the types of passive microwave remote sensing data and the characteristics of the bands contained in them. Expounded the principle of passive microwave monitoring data used for freezing and thawing,focus on passive microwave data in five categories in the study of freezing and thawing monitoring algorithms,including double index algorithm,the decision tree algorithm,freeze-thaw discriminant algorithm,seasonal threshold algorithm and based on the freezing L-band relative factors discriminant algorithm threshold,and analysis of 5 kinds of algorithms are compared;The freeze-thaw products based on different algorithms and passive microwave data were combed. Finally,the problems and future research directions of passive microwave remote sensing in surface freeze-thaw applications are summarized. In the acquisition of passive microwave data,it is found that the passive microwave data is missing due to the physical characteristics of the sensor,the shape and orbit of the earth,and the low resolution of passive microwave data leads to the low precision of freeze-thaw discrimination. For the problem of missing passive microwave data,it is proposed to use the average value of passive microwave data before and after two days to fill the missing brightness temperature data,or establish statistical function to complement the missing data. For the problem of low passive microwave resolution,the current development trend is to scale down based on passive microwave data and combine with multiple data products,such as ground temperature and active microwave data,or perform probability discrimination on surface freezing-thawing state in pixels,so as to better describe surface freeze-thaw state. In terms of the algorithm for discriminating surface freezing-thawing,based on the problem that dual-index algorithm,decision tree algorithm,freezing-thawing discriminant algorithm and seasonal threshold algorithm cannot accurately distinguish snow and frozen soil,this paper proposes to adopt the method of data assimilation or start from the snow radiation and frozen soil dielectric model. Optimization of the algorithm for the snow covered surface can further improve the accuracy of freeze-thaw classification. Based on existing freeze-thaw products,Although SMAP freeze-thaw products continue to be updated,SAMP satellite was launched late,and SAMP freeze-thaw products have a short time series. In the future,the time span of this algorithm for freezing-thawing products can be extended by combining L-band data provided by SMOS satellite. The problems mentioned above and the direction of further research are of great significance for improving the accuracy of freezing and thawing discrimination and improving the understanding of the variation law of freezing and thawing cycles,and also have certain research space. © 2022 Science Press (China).     

柴达木盆地土壤湿度的遥感反演及对蒸散发的影响

土壤水分是地下水-土壤水-大气水循环系统的核心与纽带,蒸散是该系统的重要驱动力。从区域尺度上研究土壤含水量的分布特征及土壤含水量对蒸散的影响对干旱区的生态环境保护具有重要意义。基于MODIS数据和GLDAS数据,应用表观热惯量法对GLDAS地表0~10 cm土壤湿度数据降尺度处理,估算柴达木盆地平原区2014年间6—9月的月均土壤湿度,并结合归一化植被指数(NDVI)和实测土壤湿度数据对反演结果进行验证;利用地表能量平衡系统(SEBS)模型对平原区9个子流域的日均蒸散量进行计算,分析了土壤湿度与日均蒸散量之间的关系。结果表明：反演得到的表观热惯量(ATI)与GLDAS地表0~10 cm土壤含水量数据相关性较好,决定系数R2整体在07以上;利用ATI对GLDAS数据降尺度处理,得到的土壤含水量与NDVI和实测土壤湿度的决定系数R2分别为0954和0791,因此使用ATI法对GLDAS土壤含水量数据降尺度反演柴达木盆地平原区土壤湿度是可靠的。平原区日蒸散量与土壤湿度呈明显的正相关关系,决定系数R2整体在096以上,在影响蒸散的各考虑因素中,土壤湿度对蒸散的影响远大于其他因素。     

Study on Precipitation Image Downscaling Based on the Method of Ill-posed Problems Solving

Downscaling of remote sensing precipitation products and the forecasting of circulation model are always the intense interests in hydrology and meteorology. The essence of downscaling is primarily to enhance resolution of observation or simulated rainfall field, and to appropriately increase its details or high frequency characteristics. Precipitation, as the main driving factors of the earth’s hydrologic cycle, not only affects the moisture and heat condition of a certain river basin, but also affects the global water and heat circulation. Based on the properties of rainfall self-similarity structure, the mathematically ill-posed precipitation problem solving method was used in low resolution downscaling precipitation for high resolution reconstruction. When solving the downscaling ill-posed problem, the greedy method of orthogonal matching pursuit was introduced so as to get the best high-resolution estimation in an optimal sense. It is hard to imagine that we might be able to find very similar (in mathematical norms) precipitation patterns over relatively large storm-scales. However, finding similar features over sufficiently small sub-storm scales seems more feasible. Based on the characteristics that small scale organized precipitation features tend to recur across different storm environments, the precipitation of both high and low resolution was obtained by training, which could be used to reconstruct the desired high-resolution precipitation field. Multi-source merged precipitation products were used in this experiment. Given the consideration of incompleteness of merged precipitation dataset, it was firstly interpolated based on the method of Fields of Experts (FoEs), which could solve the problem that common interpolation method could hardly work on the interpolation for dataset where consecutive missing data exists. Secondly, ideal experiments of precipitation products downscaling were carried out, where smooth coupling sampling and resampling operator were adopted respectively. Assessment based on the metrics of fidelity and spatial structural similarity demonstrates that the method used in this paper is feasible.     

Extraction of soil moisture from RADARSAT-1 and its role in the formation of the 6 December 2008 landslide at Bukit Antarabangsa,Kuala Lumpur

Active microwave has a huge potential in the estimation of soil moisture especially over large areas where the meteorological observations are seldom. The large contrast in dielectric constant between different types of soil is considered as the main factor for measuring the moisture content. This study is aimed at the extraction of soil moisture over the areas of Bukit Antarabangsa, Malaysia using active microwave remote sensing technique in order to examine the impact of moisture content dynamically on landslides occurrence, which have been a basic challenge that threaten Bukit Antarabangsa area, particularly in falling of monsoon seasons. This study addressed a specific event that took place in 6 December 2008 due to a very high level of precipitation that resulted in a raise in ground water table causing the occurrence of landslide. One Radarsat-1 image acquired in July 2008 before the landslide was used for generating the moisture content map. The resultant moisture content map showed a reasonable distribution of the moisture concentrated over the forest areas which has previous records landslides. Moreover, it was found that the previous landslide events were within the high moisture zone indicating the presence of high moisture content. Subsequently, three moisture maps were extracted from Landsat-7 ETM+, which were then used for validation process. A statistically based validation technique was used by calculating area under the curve that correlates the high moisture values of three images. In order to validate the Landsat-7 ETM+ moisture content, monthly rainfall data was plotted against the high moisture values derived from three Landsat-7 images. The validation result indicated an acceptable compatibility. The spatial relation between high moisture areas in Landsat-7 ETM+ images along the year resulted in a good fitting in the high–low moisture distribution areas with sensitivity ranged of 60–70 %. Finally, the moisture content map generated by Radarsat-1 was validated using a landslide inventory map. The resultant validation produced an area under curve of 0.704 (70 %).     

Potentials and constraints of different types of soil moisture observations for flood simulations in headwater catchments

Flood generation in mountainous headwater catchments is governed by rainfall intensities, by the spatial distribution of rainfall and by the state of the catchment prior to the rainfall, e.g. by the spatial pattern of the soil moisture, groundwater conditions and possibly snow. The work presented here explores the limits and potentials of measuring soil moisture with different methods and in different scales and their potential use for flood simulation. These measurements were obtained in 2007 and 2008 within a comprehensive multi-scale experiment in the Weisseritz headwater catchment in the Ore-Mountains, Germany. The following technologies have been applied jointly thermogravimetric method, frequency domain reflectometry (FDR) sensors, spatial time domain reflectometry (STDR) cluster, ground-penetrating radar (GPR), airborne polarimetric synthetic aperture radar (polarimetric SAR) and advanced synthetic aperture radar (ASAR) based on the satellite Envisat. We present exemplary soil measurement results, with spatial scales ranging from point scale, via hillslope and field scale, to the catchment scale. Only the spatial TDR cluster was able to record continuous data. The other methods are limited to the date of over-flights (airplane and satellite) or measurement campaigns on the ground. For possible use in flood simulation, the observation of soil moisture at multiple scales has to be combined with suitable hydrological modelling, using the hydrological model WaSiM-ETH. Therefore, several simulation experiments have been conducted in order to test both the usability of the recorded soil moisture data and the suitability of a distributed hydrological model to make use of this information. The measurement results show that airborne-based and satellite-based systems in particular provide information on the near-surface spatial distribution. However, there are still a variety of limitations, such as the need for parallel ground measurements (Envisat ASAR), uncertainties in polarimetric decomposition techniques (polarimetric SAR), very limited information from remote sensing methods about vegetated surfaces and the non-availability of continuous measurements. The model experiments showed the importance of soil moisture as an initial condition for physically based flood modelling. However, the observed moisture data reflect the surface or near-surface soil moisture only. Hence, only saturated overland flow might be related to these data. Other flood generation processes influenced by catchment wetness in the subsurface such as subsurface storm flow or quick groundwater drainage cannot be assessed by these data. One has to acknowledge that, in spite of innovative measuring techniques on all spatial scales, soil moisture data for entire vegetated catchments are still today not operationally available. Therefore, observations of soil moisture should primarily be used to improve the quality of continuous, distributed hydrological catchment models that simulate the spatial distribution of moisture internally. Thus, when and where soil moisture data are available, they should be compared with their simulated equivalents in order to improve the parameter estimates and possibly the structure of the hydrological model.     

基于星载GNSS-R获取川藏交通廊道沿线地表土壤湿度

地表土壤湿度影响着陆-气能量交换和水循环,是泥石流、冻土冻融等灾害的重要因子,获取川藏交通廊道沿线地区土壤湿度有助于研究铁路沿线气候变化和冰冻圈灾害风险.基于CYGNSS(cyclone global navigation satellite system)星载GNSS-R(global navigation satellite system reflectometry)信号,结合土地覆盖分类、归一化差分植被指数NDVI(normalized differential vegetation index)和粗糙度等地表土壤湿度影响因子,利用人工神经网络方法建立了地表土壤湿度多参数反演模型,生成了2018—2019年连续两年的川藏交通廊道沿线地区36 km空间分辨率的地表土壤湿度日产品.经土壤水分主被动探测卫星数据检验,生成的地表土壤湿度相关系数R为0.8,均方根误差RMSE(root mean square error)为0.032 cm3/cm3,偏差Bias为0.014 cm3/cm3,可为川藏交通廊道沿线气候变化和地表灾害研究提供高连续性和可靠性的数据.      

基于多源卫星观测的中国土壤湿度时空特征分析

为了揭示中国表层土壤湿度的时空分布特征及主要影响因子,通过SMAP、SMOS、AMSR2、FY3B和FY3C 5个卫星平台的遥感信息,采用集合平均法合成了2015-2016年中国25 km、逐日表层土壤湿度信息,通过比较5套产品之间的差异来分析卫星产品的不确定性,分析了中国土壤湿度的空间和季节分布特征以及同降雨量和蒸散发量的关联。结果表明:中国表层土壤湿度呈较明显带状分布,与干湿气候带的分布基本一致,总体由西北向东南和东北增加;土壤湿度在大部分地区呈现明显季节变化,主要表现为夏高冬低,不同地区的季节变化特征有所不同;中国60%以上地区,土壤湿度受同步和前期降雨量的强控制影响;中国87.5%的地区土壤湿度和蒸散发呈显著正相关和强依存关系。     

Optimization approach to retrieve soil surface parameters from single-acquisition single-configuration SAR data

This study suggests a novel approach to the retrieval of soil surface parameters using a single-acquisition single-configuration synthetic-aperture radar (SAR) system. Soil surface parameters such as soil moisture and surface roughness are key elements for many environmental studies, including Earth surface water cycles, energy exchange, agriculture, and geology. Remote sensing techniques, especially SAR data, are commonly used to retrieve such soil surface parameters over large areas. Several backscattering models have been proposed for soil surface parameters retrieval from SAR data. However, commonly, these backscattering models require multi configuration SAR data, including multi-polarization, multi-frequency, and multi-incidence angle. Here we propose a methodology that employs single-acquisition single-configuration SAR data for the retrieval of soil surface parameters. The originality is to use single-acquisition single-configuration SAR data to retrieve the soil surface parameters using an optimization approach by the genetic algorithm (GA); we have used the modified Dubois model (MDM) in HH polarization as the backscattering model. Three HH polarization and C band data sets from Quebec (Radarsat-1), Ontario (SIR-C), and Oklahoma (AIRSAR) were analyzed. The retrieved values of soil moisture and soil surface roughness were then compared to ground truth measurements with corresponding parameters. We employed diverse criteria, including the mean absolute error (MAE), the root mean square error (RMSE), the coefficient of performance (CP), and the correlation coefficient to investigate the performance of the proposed methodology. This analysis suggests the capability of the GA for the retrieval of soil surface parameters. Based on our findings, this method presents a viable alternative approach to the retrieval of soil surface parameters when only single-acquisition single-configuration SAR data is available.     

Geostatistical analysis of soil moisture measurements and remotely sensed data at different spatial scales

The European remote sensing satellite (ERS-2) synthetic aperture radar (SAR) data was used for temporal monitoring of soil moisture at Sukhothai, Thailand. Higher correlations were found between the observed soil moisture and the radar backscattering coefficient. The soil moisture distribution shows great variation in space and time due to its stochastic nature. In order to obtain a better understanding of the nature and causes of spatial variation of soil moisture, the extensive soil moisture measurements observed in Thailand and also remotely sensed ERS-2 SAR data were used for geostatistical analysis. The observed soil moisture shows seasonal variations with mean varying from 3.33 %v/v (dry season) to 33.44 %v/v (wet season). The spatial geostatistical structure also shows clear seasonal variations in the geostatistical characteristics such as range and sill. The sills vary from 1.00 (%v/v)² for the driest day to 107.57 (%v/v)² for one of the wet days. The range or the correlation lengths varies between 46.5 and 149.8 m for the wettest and driest periods. The nugget effect does not show strong seasonal pattern or trend but the dry periods usually have a smaller nugget effect than the wet periods. The spherical variogram model fits the sample variograms very well in the case of soil moisture observations while the exponential model fits those of the remotely sensed data. The ranges observed from the observed soil moisture data and remotely sensed data at the same resolution are very similar. Resolution degradation affects the geostatistical structure of the data by reducing the sills, and increasing the ranges.     

热带人工林SAR散射组成及对遥感估测叶面积指数的影响

基于RADARSAT SAR数据,利用MIMICS（ Michigan Microwave Canopy Scattering）模型模拟森林组分（冠层、树干层、地表）雷达后向散射,模拟研究表明在稀疏的人工林地区,地表层与森林冠层的直接散射是影响森林总的后向散射中最重要的两个因素。在同样的地表条件与森林环境假设下,阔叶林的模拟结果与影像的一致性要优于针叶林,针叶林由于受到地形起伏的影响,难以利用模型模拟森林的散射情况。同时,研究发现,利用森林郁闭度可以定量的表示森林冠层直接散射与总散射的相关关系,因而在一定的条件下得到冠层直接散射。最后,对该方法进行了简单的验证。、     

空间尺度转换与跨尺度信息链接:区域生态水文模拟研究空间尺度转换方法综述

空间尺度转换是近年来区域生态水文研究领域的一个基本研究问题。其需要主要是源于模型的输入数据与所能提供的数据空间尺度不一致以及模型所代表的地表过程空间尺度与所观测的地表过程空间尺度不吻合。综述了目前区域生态水文模拟研究中常用的空间尺度转换研究方法,包括向上尺度转换和向下尺度转换。详细论述了2种向下尺度转换方法: 统计学经验模型和动态模型。前者是通过将GCM大尺度数据与长期的历史观测数据比较从而建立统计学相关模型, 然后利用这个统计学经验模型进行向下的空间尺度转换. 然而动态模型并不直接对GCM数据进行向下尺度的转换,而是对与GCM进行动态耦合的区域气候模型(RCM) 的输出数据进行空间尺度转换. 通常后者所获得的数据精度要比前者高,但是一个主要缺点就是并不是全球所有的研究区域都有对应的RCM。还详细论述了2种向上尺度转换方法: 统计学经验模型和斑块模型。前者是建立一个能代表小尺度信息在大尺度上分布的密度分布概率函数, 然后利用这个函数在所需的大尺度上进行积分而求得大尺度所需的信息。而后者是根据相似性最大化原则将大尺度划分为若干个可操作的小尺度斑块,然后将计算的每个小尺度斑块的信息平均化得到大尺度所需的信息。通常在计算这种斑块化的小尺度信息的时候,对每个小尺度也会采用统计学经验模型来计算代表整个斑块小尺度的信息。建议用斑块模型与统计学经验模型相集合的方法来实现向上的空间尺度转换     

Soil moisture from operational meteorological satellites

In recent years, unforeseen advances in monitoring soil moisture from operational satellite platforms have been made, mainly due to improved geophysical retrieval methods. In this study, four recently published soil-moisture datasets are compared with in-situ observations from the REMEDHUS monitoring network located in the semi-arid part of the Duero basin in Spain. The remotely sensed soil-moisture products are retrieved from (1) the Advanced Microwave Scanning Radiometer (AMSR-E), which is a passive microwave sensor on-board NASA’s Aqua satellite, (2) European Remote Sensing satellite (ERS) scatterometer, which is an active microwave sensor on-board the two ERS satellites and (3) visible and thermal images from the METEOSAT satellite. Statistical analysis indicates that three satellite datasets contribute effectively to the monitoring of trends in surface soil-moisture conditions, but not to the estimation of absolute soil-moisture values. These sensors, or rather their successors, will be flown on operational meteorological satellites in the near future. With further improvements in processing techniques, operational meteorological satellites will increasingly deliver high-quality soil-moisture data. This may be of particular interest for hydrogeological studies that investigate long-term processes such as groundwater recharge.     

统计降尺度法对未来区域气候变化情景预估的研究进展

由于迄今为止大部分的海气耦合气候模式（AOGCM）的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测,降尺度法已广泛用于弥补AOGCM在这方面的不足。简要介绍了3种常用的降尺度法：动力降尺度法、统计降尺度法和统计与动力相结合的降尺度法;系统论述了统计降尺度方法的理论和应用的研究进展,其中包括：统计降尺度法的基本假设,统计降尺度法的优缺点，以及常用的3种统计降尺度法；还论述了用统计降尺度法预估未来气候情景的一般步骤，以及方差放大技术在统计降尺度中的应用；同时还强调了统计降尺度方法和动力降尺度方法比较研究在统计降尺度研究中的重要性；最后指出统计与动力相结合的降尺度方法将成为降尺度技术的重要发展方向。