荷兰农业水土资源利用研究与遥感技术的应用—赴荷兰访问考察团

根据中荷两国学者互访协议,中国科学院沙漠所派我们两人在1985年10月10日至11月6日对荷兰进行了为期四周的考察访问。在荷期间,我们受到荷方学者热情友好的接待,首后访问了国际农业中心(IAC—     

SMOS与SMAP过境时段表层土壤水分的稳定性研究

SMOS和SMAP都是为获取全球土壤水分信息而设计的专题卫星,均搭载了L波段辐射计。进行二者的横向对比是构建具有一致性的全球土壤水分数据集的关键基础。虽然SMAP、SMOS名义上的过境时刻是固定的,但二者的实际过境时刻随时间和空间发生变化,它们与地面实测数据三者之间难以匹配形成时序上严格统一的样本对,从而给土壤水分反演结果的精度评定带来困难。针对这一问题,以美国大陆地区为研究区,首先对2016~2017年SMOS、SMAP土壤水分数据的时间戳进行统计,判定二者过境的交叠时段;进而利用高观测频率、大空间尺度的实测数据,研究表层土壤水分在此时段内的自然变化特征。结果显示,按照全部、无降水、有降水3种条件,在样本量分别为98.14%、99.51%和88.49%的绝大多数情况下,表层土壤水分的变化量为0.007 m3/m3、0.007 m3/m3和0.012 m3/m3, 远小于SMOS、SMAP的目标精度（0.04 m3/m3）。初步证实： ①SMOS与SMAP的土壤水分反演结果（L2数据）可进行直接比对;②过境时刻差异对验证误差的影响可不计。     

Evaluation of the SMOS L-MEB passive microwave soil moisture retrieval algorithm

Soil moisture will be mapped globally by the European Soil Moisture and Ocean Salinity (SMOS) mission to be launched in 2009. The expected soil moisture accuracy will be 4.0 %v/v. The core component of the SMOS soil moisture retrieval algorithm is the L-band Microwave Emission of the Biosphere (L-MEB) model which simulates the microwave emission at L-band from the soil-vegetation layer. The model parameters have been calibrated with data acquired by tower mounted radiometer studies in Europe and the United States, with a typical footprint size of approximately 10 m. In this study, aircraft L-band data acquired during the National Airborne Field Experiment (NAFE) intensive campaign held in South-eastern Australia in 2005 are used to perform the first evaluation of the L-MEB model and its proposed parameterization when applied to coarser footprints (62.5 m). The model could be evaluated across large areas including a wide range of land surface conditions, typical of the Australian environment. Soil moisture was retrieved from the aircraft brightness temperatures using L-MEB and ground measured ancillary data (soil temperature, soil texture, vegetation water content and surface roughness) and subsequently evaluated against ground measurements of soil moisture. The retrieval accuracy when using the L-MEB ‘default’ set of model parameters was found to be better than 4.0 %v/v only over grassland covered sites. Over crops the model was found to underestimate soil moisture by up to 32 %v/v. After site specific calibration of the vegetation and roughness parameters, the retrieval accuracy was found to be equal or better than 4.8 %v/v for crops and grasslands at 62.5-m resolution. It is suggested that the proposed value of roughness parameter H_R for crops is too low, and that variability of H_R with soil moisture must be taken into consideration to obtain accurate retrievals at these scales. The analysis presented here is a crucial step towards validating the application of L-MEB for soil moisture retrieval from satellite observations in an operational context.     

Soil moisture mapping and AMSR-E validation using the PSR in SMEX02

Field experiments (SMEX02) were conducted to evaluate the effects of dense agricultural crop conditions on soil moisture retrieval using passive microwave remote sensing. Aircraft observations were collected using a new version of the Polarimetric Scanning Radiometer (PSR) that provided four C band and four X band frequencies. Observations were also available from the Aqua satellite Advanced Microwave Scanning Radiometer (AMSR-E) at these same frequencies. SMEX02 was conducted over a three-week period during the summer near Ames, Iowa. Corn and soybeans dominate the region. During the study period the corn was approaching its peak water content state and the soybeans were at the mid point of the growth cycle. Aircraft observations are compared to ground observations. Subsequently models are developed to describe the effects of corn and soybeans on soil moisture retrieval. Multiple altitude aircraft brightness temperatures were compared to AMSR-E observations to understand brightness temperature scaling and provide validation. The X-band observations from the two sensors were in reasonable agreement. The AMSR-E C-band observations were contaminated with anthropogenic RFI, which made comparison to the PSR invalid. Aircraft data along with ancillary data were used in a retrieval algorithm to map soil moisture. The PSR estimated soil moisture retrievals on a field-by-field comparison had a standard error of estimate (SEE) of 5.5%. The error reduced when high altitude soil moisture estimates were aggregated to 25 km resolution (same as AMSR-E EASE grid product resolution) (SEE ∼ 2.85%). These soil moisture products provide a validation of the AMSR retrievals. PSR/CX soil moisture images show spatial and temporal patterns consistent with meteorological and soil conditions. The dynamic range of the PSR/CX observations indicates that reasonable soil moisture estimates can be obtained from AMSR, even in areas of high vegetation biomass content (∼ 4-8 kg/m2).     

利用遥感和GIS监测旱情的方法研究

主要以山东,河南两省为例,探讨了利用ＮＯＡＡ／ＡＶＨＲＲ图像资料监测大面积表的几种研究方法,并对每种方法的优缺点进行了评估。结果表明,几种方法结合使用并借助地理信息系统,进行大面积的旱情监测是可行的,另外还进行了利用机载雷达图像监测河北省土壤水分的方法尝试研究,分析表明微波遥感监测土壤水分具有广阔的应用前景,可以利用星载ＳＡＲ图像资料作进一步的分析研究,从而获取微波监测土壤水分的最佳波段和最佳极化     

微波遥感土壤湿度研究进展

发展实用的微波遥感土壤湿度卫星反演算法,以提供区域尺度上的土壤湿度信息,对水文学、气象学以及农业科学研究与应用至关重要。简要分析了主动微波遥感土壤湿度的研究进展情况,重点对被动微波遥感土壤湿度的原理、算法发展及研究趋势进行了详细论述。主动微波具有卫星数据空间分辨率高的特点,随着携载主动微波器的一系列卫星的发射,主动微波遥感土壤湿度将受到重视。被动微波遥感研究历史长、反演算法特别是卫星遥感算比较成熟,是今后区域尺度乃至全球尺度监测土壤湿度的重要手段。     

The SMOS Mediterranean Ecosystem L-Band characterisation EXperiment (MELBEX-I) over natural shrubs

In the framework of ESA's SMOS mission (Soil Moisture and Ocean Salinity), many studies have been carried out over different land surface types to model their microwave emission at L-band (1.4 GHz). Results of these studies have been integrated in the emission model L-MEB (L-Band Microwave Emission of the Biosphere), which is the core of the SMOS Level 2 soil moisture retrieval algorithm. The Mediterranean Ecosystem L-Band characterisation EXperiment (MELBEX-I) was carried out at the SMOS validation site near Valencia in autumn 2005. The main objective of MELBEX-I was to calibrate L-MEB over Mediterranean shrub land, as no data were available over this biome. For that purpose, multi-angular and dual polarimetric measurements (H, V) were obtained by the EMIRAD L-band radiometer from a 14-m tower. Results of this study indicate a small and constant impact of vegetation on the microwave emission of shrub land, and L-MEB parameters for shrub land were obtained. In addition, the study highlights the need for calibrating microwave soil roughness, which was found to be constant at the site. Depending on the number of retrieved parameters, soil moisture (SM) near the surface could be estimated with errors between 0.035 m³ m^− 3 (if only SM was retrieved) and 0.057 m³ m^− 3 (if SM, optical depth and a roughness parameter were simultaneously retrieved). Finally, no modelling improvements were observed when coarse estimates of the fraction of exposed rocks were accounted for in the model.     

微波遥感监测土壤水分的研究初探

在GPS定位的基础上,同步测量土攘水分、土壤后向散射系数,和同步获取的X波段、HH机化SAR图像进行了土攘水分监N.]的徽波遥感试验研究。结果表明,X波段SAR图像的灰度与表层土壤(0~10cm)水分有较好的相关性,35OHH极化的土峨后向散射系数与SAR图像灰度和土攘水分也有较好的相关性,由SAR图像及土攘的后向散射系数估算的土峨水分精度相近,相对误差均为12%左右,因而利用X波段、HH极化的机载SAR图像监浏土壤水分是可行的。雷达图像的穿透力一般在10cm以内,因此探讨了由表层土壤水分推求剖面土壤水分的可能性,并提出以土攘水分计法在浏童精度和速度上改进传统土壤水分测量的方法。     

土壤湿度微波遥感中的植被散射模型进展

植被是影响土壤湿度微波遥感的主要因子之一,土壤湿度微波遥感的主要任务是建立含有地表土壤信息的植被散射模型。植被散射模型的建立可以加深我们对植被和土壤散射机理的理解,定量分析微波后向散射系数对于各散射因子的敏感性,进一步达到从微波信息中反演土壤湿度的目的。植被散射模型可以分为经验模型、理论模型和半经验模型,各种模型都具有自身的优势和局限性。经验模型的建立比较简单,但一般只适用于特定的研究条件;理论模型是建立在一定的理论基础之上,对于散射因子的考虑相对详尽,但一般模型比较复杂,反演相对困难;半经验模型是前两者的折中,它以植被的宏观物理参量为模型参数,模型的建立和反演比理论模型要简单,但同时也具有一定的理论依据,适用性也较经验模型广。     

基于微波和可见光遥感的冬小麦土壤墒情反演

土壤墒情是水文学、气象学及农业科学研究领域中的一个重要指标参数,对气候、农业、旱情监测都具有极为重要的意义。以河南省广利灌区冬小麦为研究对象,以水云模型为基础,利用Landsat-8和Sentinel-1 A数据计算双层衰减因子、植被和土壤的后向散射系数。采用RBF神经网络拟合土壤的后向散射系数与土壤含水量的关系,通过实测监测点对反演的结果进行精度验证,结果表明:相关系数的平方为0.754 5,均方根误差为0.022,结果良好,可为灌区冬小麦土壤墒情的评估提供参考。     

A first assessment of the SMOS data in southwestern France using in situ and airborne soil moisture estimates: The CAROLS airborne campaign

The Soil Moisture and Ocean Salinity (SMOS) satellite mission, based on an aperture synthesis L-band radiometer was successfully launched in November 2009. In the context of a validation campaign for the SMOS mission, intensive airborne and in situ observations were performed in southwestern France for the SMOS CAL/VAL, from April to May 2009 and from April to July 2010. The CAROLS (Cooperative Airborne Radiometer for Ocean and Land Studies) bi-angular (34°-0°) and dual-polarized (V and H) L-band radiometer was designed, built and installed on board the French ATR-42 research aircraft. During springs of 2009 and 2010, soil moisture observations from the SMOSMANIA (Soil Moisture Observing System-Meteorological Automatic Network Integrated Application) network of Météo-France were complemented by airborne observations of the CAROLS L-band radiometer, following an Atlantic-Mediterranean transect in southwestern France. Additionally to the 12 stations of the SMOSMANIA soil moisture network, in situ measurements were collected in three specific sites within an area representative of a SMOS pixel. Microwave radiometer observations, acquired over southwestern France by the CAROLS instrument were analyzed in order to assess their sensitivity to surface soil moisture (w_g). A combination of microwave brightness temperature (T_b) at either two polarizations or two contrasting incidence angles was used to retrieve w_g through regressed empirical logarithmic equations with good results, depending on the chosen configuration. The regressions derived from the CAROLS measurements were applied to the SMOS T_b and their retrieval performance was evaluated. The retrievals of w_g showed significant correlation (p-value < 0.05) with surface measurements for most of the SMOSMANIA stations (8 of 12 stations) and with additional field measurements at two specific sites, also. Root mean square errors varied from 0.03 to 0.09 m³ m^− 3 (0.06 m³ m^− 3 on average).     

基于Triple-Collocation方法的微波遥感土壤水分产品不确定性分析及数据融合

微波遥感可以获取大范围的地表土壤水分信息,以及由此得到全球尺度的土壤水分产品。但由于传感器观测配置和反演方法等诸多因素的影响,使得不同的土壤水分产品在精度和可靠性方面存在差异。基于Triple-Collocation（TC）方法,在青藏高原那曲地区的0.25°×0.25°和1.0°×1.0°两个空间尺度上对AMSR2、SMAP和SMOS 3种土壤水分遥感产品进行不确定性分析,开展基于随机误差的数据融合算法研究。研究结果表明:不同遥感产品间的随机误差在空间分布上存在显著的不一致性,使得应用传统的算术平均方法进行数据融合不具有普适性。基于此不确定性,对3种产品配赋相应的权重进行融合,相比于3种土壤水分原始数据集,融合产品不仅具有更丰富的数据量,也会对数据精度有所改善。当遥感产品间的随机误差接近时,等权重和优化权重的融合结果非常接近;当遥感产品间的随机误差差异较大时,基于不确定性的数据融合方法相比等权重方法可以明显的提高融合数据的精度。     

Soil moisture variations monitoring by AMSU-based soil wetness indices: A long-term inter-comparison with ground measurements

Soil moisture controls the partitioning of rainfall into runoff and infiltration and, consequently, the runoff generation. On the catchment scale its routine monitoring can be performed through remote sensing technologies. Within this framework, the purpose of this study is to investigate the potential of the Advanced Microwave Sounding Unit (AMSU), radiometer on board the NOAA (National Oceanic and Atmospheric Administration) satellites and operating since 1998, for the assessment of soil wetness conditions by comparing soil moisture data with both those measured in situ and provided by a continuous rainfall-runoff model applied to four catchments located in the Upper Tiber River (Central Italy). In particular, in order to perform a robust analysis an extensive and long-term period (nine years) of data was investigated. In detail, the Soil Wetness Variation Index, derived from the AMSU data modified in order to take account of the difference between the soil layer investigated by the satellite sensor and that used as a benchmark, was found to be correlated both with the in-situ and modeled soil moisture variations showing correlation coefficients in the range of 0.42-0.49 and 0.33-0.48, respectively. As far as the soil moisture temporal pattern is concerned, higher correlations were obtained (0.59-0.84 for the in-situ data and 0.82-0.87 for the modeled data set) partly due to the soil moisture seasonal pattern that enhances the correlation. Overall, the root mean square error was found to be less than 0.05 m³/m³ for both the comparisons, thus assessing the potential of the AMSU sensor to quantitatively retrieve soil moisture temporal patterns. Moreover, the AMSU sensor can be considered as a useful tool to provide a reliable and frequently updated global soil moisture data set, considering its higher temporal resolution now available (about 4 passes per day) thanks to the presence of the sensor aboard different satellites.     

基于Triple-Collocation方法的微波遥感土壤水分产品不确定性分析及数据融合

微波遥感可以获取大范围的地表土壤水分信息,以及由此得到全球尺度的土壤水分产品。但由于传感器观测配置和反演方法等诸多因素的影响,使得不同的土壤水分产品在精度和可靠性方面存在差异。基于Triple-Collocation（TC）方法,在青藏高原那曲地区的0.25°×0.25°和1.0°×1.0°两个空间尺度上对AMSR2、SMAP和SMOS 3种土壤水分遥感产品进行不确定性分析,开展基于随机误差的数据融合算法研究。研究结果表明:不同遥感产品间的随机误差在空间分布上存在显著的不一致性,使得应用传统的算术平均方法进行数据融合不具有普适性。基于此不确定性,对3种产品配赋相应的权重进行融合,相比于3种土壤水分原始数据集,融合产品不仅具有更丰富的数据量,也会对数据精度有所改善。当遥感产品间的随机误差接近时,等权重和优化权重的融合结果非常接近;当遥感产品间的随机误差差异较大时,基于不确定性的数据融合方法相比等权重方法可以明显的提高融合数据的精度。     

基于CCI土壤水分产品的干旱指数精度评价及其对东北地区粮食产量的影响

土壤水分是监测作物旱情的基本因子,以欧空局1978~2014年微波遥感土壤水分产品、中国经济与社会发展统计数据库以及气象数据为基础,结合土壤水分亏缺指数（Soil Water Deficit Index, SWDI）分析东北地区的干旱程度与玉米亩产的关系。结果表明：①东北三省干旱程度空间上呈现自东北向西南逐渐加重的空间分布模式;②基于CCI (Climate Change Initiative)土壤水分产品计算的SWDI干旱指数与降雨量和气温有良好的相关关系,可用于评估干旱发生的严重程度;③玉米生长季关键需水期——7月的SWDI与玉米产量的相关性最好,二者在黑龙江、吉林和辽宁省的R2分别为0.43、0.78和0.38,非常适合用于评估干旱对玉米单产的影响。该结论对于研究大范围土壤水分含量对农作物产量的影响以及相关农业决策具有重要指导意义。     

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.     

A proposed extension to the soil moisture and ocean salinity level 2 algorithm for mixed forest and moderate vegetation pixels

The Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009, provides global maps of soil moisture and ocean salinity by measuring the L-band (1.4 GHz) emission of the Earth's surface with a spatial resolution of 40-50 km. Uncertainty in the retrieval of soil moisture over large heterogeneous areas such as SMOS pixels is expected, due to the non-linearity of the relationship between soil moisture and the microwave emission. The current baseline soil moisture retrieval algorithm adopted by SMOS and implemented in the SMOS Level 2 (SMOS L2) processor partially accounts for the sub-pixel heterogeneity of the land surface, by modelling the individual contributions of different pixel fractions to the overall pixel emission. This retrieval approach is tested in this study using airborne L-band data over an area the size of a SMOS pixel characterised by a mix Eucalypt forest and moderate vegetation types (grassland and crops), with the objective of assessing its ability to correct for the soil moisture retrieval error induced by the land surface heterogeneity. A preliminary analysis using a traditional uniform pixel retrieval approach shows that the sub-pixel heterogeneity of land cover type causes significant errors in soil moisture retrieval (7.7%v/v RMSE, 2%v/v bias) in pixels characterised by a significant amount of forest (40-60%). Although the retrieval approach adopted by SMOS partially reduces this error, it is affected by errors beyond the SMOS target accuracy, presenting in particular a strong dry bias when a fraction of the pixel is occupied by forest (4.1%v/v RMSE, −3.1%v/v bias). An extension to the SMOS approach is proposed that accounts for the heterogeneity of vegetation optical depth within the SMOS pixel. The proposed approach is shown to significantly reduce the error in retrieved soil moisture (2.8%v/v RMSE, −0.3%v/v bias) in pixels characterised by a critical amount of forest (40-60%), at the limited cost of only a crude estimate of the optical depth of the forested area (better than 35% uncertainty). This study makes use of an unprecedented data set of airborne L-band observations and ground supporting data from the National Airborne Field Experiment 2005 (NAFE'05), which allowed accurate characterisation of the land surface heterogeneity over an area equivalent in size to a SMOS pixel.     

干旱区地表L波段微波辐射特性分析穿透深度的影响

利用２０１２年黑河中游HiWATER实验区戈壁和大满超级站的土壤水分和温度廓线数据,基于LＧMEB微波辐射传输模型,分析了L波段微波穿透深度随土壤水分的变化.在此基础上,分析了不同深度土壤水分和温度作为LＧMEB模型输入,对L波段微波发射率和亮度温度的影响.研究结果表明:L波段微波穿透深度随土壤水分、温度的变化,呈现明显的差异,在本研究区L波段微波穿透深度在４~２２cm 之间;在土壤水分较高的超级站,计算的L波段亮度温度与０~４cm土壤层的亮度温度更为接近,而在土壤水分较低的戈壁站,计算的L波段亮度温度与０~４０cm土壤层的亮度温度更为接近.因此,在利用L波段微波数据反演土壤水分时,应考虑穿透深度的影响,以提高反演精度.     

被动微波遥感指数及其应用

分别概述了微波极化指数、散射指数以及土壤湿度指数等被动微波遥感指数的发展及其应用。37GHz的微波极化差指数△T37（△T37=TB37V—TB37H）和极化比指数（MPDI=C＊（TB37V—TB37H）／（TB37V＋TB37H））被认为是监测植被状况的微波植被指数,利用GAME—Tibet1998IOP数据计算和分析了青藏高原中部5个试验站点6～9月的平均△T37值和MPDI值的变化情况。结果表明：ANDUO和MS3608的平均值在15K左右,表现出裸土的微波辐射特征;总体上5个站点的MPDI随时间的变化不大,也即在1998年6～9月间,各个站点的植被状况变化不大;而站间的差别比较大,也即各个站点的植被状况有较大的差别;ANDUO的MPDI表现出规律性的变化,即在6至9月的变化中,8月份的MPDI最小,对应植被最好的月份;对研究区的MPDI和相应时间的MSAVI（可见／近红外数据得到的修改型土壤调整植被指数）的空间分布图进行了比较,二者基本吻合。