锡林浩特草原区域MODIS LAI产品真实性检验与误差分析

本文研究了LAI产品真实性检验的指标和方法,建立了LAI产品真实性检验的流程,将遥感产品真实性检验误差分解为模型误差、数据定量化差异和尺度效应3个方面。以内蒙古锡林浩特草原为研究区,结合实测数据和Landsat TM数据建立NDVI-LAI模型,得到LAI验证参考"真值",据此"真值"按照本文的流程对MODIS LAI产品进行验证,分析了研究区MODIS LAI产品真实性检验的误差来源。研究表明,该研究区的MODIS LAI(MOD15A2)产品相对高估约25%。各个误差因素中,LAI遥感模型差异对于结果影响最大,MODIS LAI模型高估了该区域草地LAI(高估约44.2%);数据定量差异的影响也比较大,MODIS地表反射率数据与Landsat TM地表反射率数据的差异造成了约16.2%的低估;尺度效应的影响较小,造成约3.1%的低估,其中NDVI-LAI模型的尺度效应带来2.4%的低估,NDVI数据的尺度效应造成约0.7%的低估。     

Estimating soil erosion using MODIS and TM images based on support vector machine and à trous wavelet

To date, there is little work concerning the application of fusing images with significantly different spectral and spatial resolutions. In this paper, a novel method based on support vector machine (SVM) is proposed to quickly estimate soil erosion using the fused results produced from fusing such multisensor images by à trous wavelet transform (AWT). In the proposed method, the AWT is used to derive the high-resolution vegetation coverage image (HVCI) while the SVM overlays the HVCI and the slope image to derive the soil erosion map. By taking MODIS and TM images as an example, the potential of the proposed method is evaluated both quantitatively and qualitatively. The results show that it is feasible to perform the fusion of MODIS and TM images and the soil erosion map produced from the fused images by the proposed method can be achieved with an accuracy level comparable to that solely from the TM images. The merging of MODIS and TM images partly solves the constrains associated with the TM data availability which is caused by the lower revisit frequency and narrower spatial coverage.     

MODIS V006和V005全球反照率产品精度对比分析

反照率作为一种非常重要的地表能量平衡、全球变化研究的参数,在众多研究领域中得到了广泛的应用,到目前为止已经有多种全球范围的反照率产品进行业务化生产和发布,针对不同反照率产品质量评价的研究也变得愈加重要。MODLAND团队在MODIS V005反照率产品反演算法的基础上通过改进16天周期内观测数据加权的方法生产出新版本的反照率产品MODIS V006。本文针对MODIS两个版本V005及V006的反照率产品,利用FLUXNET地面站点数据,比较验证两个版本反照率的总体精度以及在不同地表类型条件下的精度差异,同时通过交叉验证的方法分析二者的差异及稳定性。验证结果表明,MODIS V006反照率产品虽然在全反演高质量的数据比例上较V005有所下降,但是在同等条件下V006在提高时间分辨率的同时其精度也有所提高,在不同的地表类型条件下精度也优于V005,且在时间序列分布上具有稳定比例的高质量数据,可以满足大多数应用的精度需求。     

基于波谱知识库的MODIS叶面积指数反演及验证

目前用物理模型反演叶面积指数普遍存在缺少先验知识的状况，如何获得准确的先验知识是遥感走向应用的一个关键环节。中国典型地物标准波谱数据库就是结合国家重大行业中的应用需求，研究制定地物波谱获取与分析的技术规范和数据标准，建立典型地物标准波谱数据库。从波谱数据库提取模型反演所需要的先验知识，实现了基于SAIL模型的MODIS数据（经过几何纠正与大气纠正）叶面积指数的反演。另外，基于TM数据，对MODIS混合像元进行了分解，用纯像元的叶面积指数与实测数据进行对比验证，同时，反演结果与NASA的LAI产品也进行了对比，结果表明基于波谱库的先验知识可以有效的提高叶面积指数的反演精度。     

基于GPS的MODIS近红外可降水量季节性模型建立

在考虑可降水量季节性变化的基础上,提出利用GPS数据建立MODIS近红外可降水量季节性模型。首先对比分析2014年北京房山(BJFS)站的GPS可降水量和相应时间的MODIS近红外可降水量数据,发现两者之间的变化趋势基本一致,存在显著线性相关性;然后以GPS可降水量为标准值,利用回归分析建立GPS和MODIS可降水量之间的季节和全年校正模型。经检验,GPS可降水量与四个季节模型校正的MODIS近红外可降水量的均方根误差均小于3mm,最大误差不超过6mm,季节校正模型的精度都要高于全年校正模型。     

基于多源遥感数据的玛纳斯河流域荒漠化监测

利用遥感手段对玛纳斯河流域绿洲-荒漠带的荒漠化状况进行监测。利用不同时期TM数据,在小尺度上分析该区域2000~2008年荒漠化土地的分布特征;基于MODIS陆地遥感产品(NDVI、Albedo及LST),利用多源信息复合法计算荒漠化程度指数;基于TM荒漠化监测结果对该指数进行分级处理,初步建立并验证基于MODIS数据的荒漠化监测评价模型。结果表明:近10 a来,绿洲外围荒漠化土地变化较小,大部分变化发生在绿洲内部,主要为局部荒漠化被开发或因盐碱化过重导致的撂荒;荒漠化监测评价模型对研究区荒漠化程度定量评价精度可达91.25%。     

An approach for developing Landsat-5 TM-based retrieval models of suspended particulate matter concentration with the assistance of MODIS

It is challenging to develop Landsat-5 TM (TM5) image-based retrieval models for estimating the suspended particulate matter concentration (C_SPM) in water when missing coincident ground C_SPM measurements. This study, with the Poyang Lake in China as a case study, proposed an approach for developing TM5-based C_SPM retrieval models with the assistance of moderate resolution imaging spectroradiometer (MODIS) images. After validation with an independent dataset, a cubic C_SPM retrieval model of 250 m MODIS red band was used to estimate the C_SPM values at 100 sampling points from the MODIS images (MODIS-based C_SPM) captured at three time periods. The MODIS-based C_SPM values at the time period with the largest C_SPM variation were combined with their coincident TM5 image reflectance for TM5-based model calibrations. The linear, quadratic, cubic, power and exponential models of MODIS-based C_SPM against TM5 single bands and their combinations were calibrated, respectively. Four best-fitting TM5-based C_SPM models were selected to retrieve the C_SPM values at 100 sampling points from the TM5 images (TM5-based C_SPM) at the other two time periods, and the coincident MODIS- and TM5-based C_SPM values were compared to assess TM5-based model performances. Model calibration results showed that the cubic and exponential models of TM5 red band (band 3) and red subtracting mid-infrared band (band 5) obtained the best fitting for estimating C_SPM from the TM5 image on 12 August 2005, and they explained 94–97% of the variation of MODIS-based C_SPM values with an estimated standard error of 6.617–8.457 mg/l. Model validations indicated that the exponential model of TM5 red band got the best result for estimating C_SPM from TM5 images when the MODIS-based C_SPM values were assumed as ground truths (correlation coefficient between MODIS- and TM5-based C_SPM values = 0.96, root mean square error = 4.60 mg/l). We concluded that the TM5-based C_SPM retrieval models could be developed with the assistance of MODIS, and the approach proposed in this study will be helpful for other researchers who also want to retrieve C_SPM from TM5 image archive but without coincident ground C_SPM measurements.     

Evaluating MODIS snow products for modelling snowmelt runoff: Case study of the Rio Grande headwaters

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM) and in other models for simulating discharge from snowmelt. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM + ) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and may not always provide sufficient cloud-free dates. The coarser spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) offers better temporal resolution and in cloudy years, MODIS data offer the best alternative for mapping snow cover when finer spatial resolution data are unavailable. However, MODIS’ coarse spatial resolution (500 m) can obscure fine spatial patterning in snow cover and some MODIS products are not sensitive to end-of-season snow cover. In this study, we aimed to test MODIS snow products for use in simulating snowmelt runoff from smaller headwater basins by a) comparing maps of TM and MODIS-based SCA and b) determining how SRM streamflow simulations are changed by the different estimates of seasonal snow depletion. We compared gridded MODIS snow products (Collection 5 MOD10A1 fractional and binary SCA; SCA derived from Collection 6 MOD10A1 Normalised Difference Snow Index (NDSI) Snow Cover), and the MODIS Snow Covered-Area and Grain size retrieval (MODSCAG) canopy-corrected fractional SCA (SCA_MG), with reference SCA maps (SCA_REF) generated from binary classification of TM imagery. SCA_MG showed strong agreement with SCA_REF; excluding true negatives (where both methods agreed no snow was present) the median percent difference between SCA_REF and SCA_MG ranged between −2.4% and 4.7%. We simulated runoff for each of the four study years using SRM populated with and calibrated for snow depletion curves derived from SCA_REF. We then substituted in each of the MODIS-derived depletion curves. With efficiency coefficients ranging between 0.73 and 0.93, SRM simulation results from the SCA_MG runs yielded the best results of all the MODIS products and only slightly underestimated discharge volume (between 7 and 11% of measured annual discharge). SRM simulations that used SCA derived from Collection 6 NDSI Snow Cover also yielded promising results, with efficiency coefficients ranging between 0.73 and 0.91.In conclusion, we recommend that when simulating snowmelt runoff from small basins (<4000 km²) with SRM, we recommend that users select either canopy-corrected MODSCAG or create their own site-specific products from the Collection 6 MOD10A1 NDSI.     

Consistency of vegetation index seasonality across the Amazon rainforest

Vegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.     

多时相MODIS影像水田信息提取研究

水稻种植及其分布信息是土地覆被变化、作物估产、甲烷排放、粮食安全和水资源管理分析的重要数据源。基于遥感的水田利用监测中,通常采用时序NDVI植被指数法和影像分类法分别进行AVHRR和TM影像的水田信息获取。针对8天合成MODIS陆地表面反射比数据的特点和水稻生长特征,选取水稻种植前的休耕期、秧苗移植期、秧苗生长期和成熟期等多时相MODIS地表反射率影像数据,通过归一化植被指数、增强植被指数及利用对土壤湿度和植被水分含量较敏感的短波红外波段计算得到的陆表水指数进行水田信息获取。将提取结果与基于ETM＋影像的国土资源调查水田数据,通过网格化计算处理并进行对比分析,结果表明,利用MODIS影像的8天合成地表反射率数据,进行区域甚至全国的水田利用监测是可行的。     

地表覆盖分类数据对区域森林叶面积指数反演的影响

以江西省吉安市为研究区,将5种全球地表覆盖分类数据(包括美国地质调查局(USGS)、马里兰大学(UMD)和波士顿大学(BU)生成的3套数据和欧洲生成的2套数据)以及由TM影像生成的区域地表覆盖分类数据,分别与MODIS1km反射率资料结合,利用基于4尺度几何光学模型的LAI反演方法生成研究区的LAI。在1km和4km两种尺度上将反演的LAI与TM资料生成的LAI进行比较,评价地表覆盖分类数据对LAI反演结果的影响。结果表明,TM和欧洲太空局的GLOBCOVER地表覆盖分类数据用于反演LAI的结果较好,在1km尺度上,反演的LAI与统计模型估算的TMLAI相关的R2分别为0.44和0.40,在4km尺度上的R2分别为0.57和0.54;其次为波士顿大学的MODIS地表覆盖分类数据,据其反演的LAI与TMLAI相关的R2在1km和4km尺度上分别为0.38和0.51;而马里兰大学的UMD和欧洲的GLC2000地表覆盖分类数据会导致反演的LAI存在较大误差,据其反演的LAI与TMLAI之间的一致性较差,在1km和4km两种尺度上平均偏低20%左右;LAI的反演结果对聚集度系数具有强的敏感性。该研究表明,为了提高区域/全球LAI反演精度,需要有高质量的地表覆盖分类数据。     

Accuracy assessment of four cloud-free snow cover products over the Qinghai-Tibetan Plateau

Four up-to-date daily cloud-free snow products – IMS (Interactive Multisensor Snow products), MOD-SSM/I (combination of the MODIS and SSM/I snow products), MOD-B (Blending method basing on the MODIS snow cover products) and TAI (Terra–Aqua–IMS) – with high-resolutions over the Qinghai-Tibetan Plateau (QTP) were comprehensively assessed. Comparisons of the IMS, MOD-SSM/I, MOD-B and TAI cloud-free snow products against meteorological stations observations over 10 snow seasons (2004–2013) over the QTP indicated overall accuracies of 76.0%, 89.3%, 92.0% and 92.0%, respectively. The K_hat values of the IMS, MOD-SSM/I, MOD-B and TAI products were 0.084, 0.463, 0.428 and 0.526, respectively. The TAI products appear to have the best cloud-removal ability among the four snow products over the QTP. Based on the assessment, an I-TAI (Improvement of Terra–Aqua–IMS) snow product was proposed, which can improve the accuracy to some extent. However, the algorithms of the MODIS series products show instability when identifying wet snow and snow under forest cover over the QTP. The snow misclassification is an important limitation of MODIS snow cover products and requires additional improvements.     

A radiance-based split-window algorithm for land surface temperature retrieval: Theory and application to MODIS data

The split-window algorithm is the most commonly used method for land surface temperature (LST) retrieval from satellite data. Simplification of the Planck’s function, as an important step in developing the SWA, allows us to directly relate the radiance to the temperature toward solving the radiative transfer equation (RTE) set. In this study, Planck’s radiance relationship between two adjacent thermal infrared channels was modeled to solve the RTE set instead of simplification of the Planck’s function. A radiance-based split-window algorithm (RBSWA) was developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data. The performance of the RBSWA was assessed and compared with three most common brightness temperature-based split-window algorithms (BTBSWAs) by using the simulated data and satellite measurements. Simulation analysis showed that the LST retrieval using RBSWA had a Root Mean Square Error (RMSE) of 0.5 K and achieved an improvement of 0.3 K compared with three BTBSWAs, and the LST retrieval accuracy using RBSWA was better than 1.5 K considering uncertainties in input parameters based on the sensitivity analysis. For application of RBSWA to MODIS data, the results showed that: 1) comparison between LST from MODIS LST product and LST retrieved using RBSWA showed a mean RMSE of 1.33 K for 108 groups of MODIS image covering continental US, which indicates RBSWA is reliable and robust; 2) when using the measurements from US surface radiation budget network as real values the RMSE of the RBSWA algorithm was 2.55 K and was slightly better than MODIS LST product; and 3) through the cross validation using Advanced Spaceborne Thermal Emission and Reflection Radiometer LST product, the RMSE of the RBSWA algorithm was 2.23 K and was 0.28 K less than that of MODIS LST product. We conclude that the RBSWA for LST retrieval from MODIS data can attain a better accuracy than the BTBSWA.     

The assessment of multi-sensor image fusion using wavelet transforms for mapping the Brazilian Savanna

Multi-sensor image fusion using the wavelet approach provides a conceptual framework for the improvement of the spatial resolution with minimal distortion of the spectral content of the source image. This paper assesses whether images with a large ratio of spatial resolution can be fused, and evaluates the potential of using such fused images for mapping the Brazilian Savanna. Three types of wavelet transforms were used to perform the fusion between MODIS and Landsat TM images. Five quality measures were defined to assess the quality of the fused images. The results showed that it was possible to perform the fusion of MODIS and TM images and the pyramidal in Fourier space wavelet transform provided the best quality measures for the fused images. Classification results showed that fused images could be used for mapping the Brazilian Savanna with an accuracy level comparable to the Landsat TM image.     

FY-3C VIRR大气可降水产品生成和检验

风云三号C星(FY-3C)可见光红外扫描辐射计(VIRR)两个红外分裂窗通道数据生成的晴空大气可降水(TPW)产品已投入业务使用。本文介绍了该产品的生成方法,并从产品精度和稳定性两个方面评价产品质量。与MODIS Terra TPW的月平均数据对比,FY-3C VIRR TPW能正确反应大气可降水的全球分布。与2015年3月—4月的全球探空数据对比,FY-3C VIRR TPW均方根误差为5.36 mm,相对误差在水汽值大于30 mm时在20%以内,并且夜间产品精度优于白天。相比于MODIS红外TPW产品与探空数据的误差,FY-3C TPW精度略好。计算2015年1月至2016年7月FY-3C VIRR TPW产品相对探空数据的月均方根误差,19个月均方根误差的标准差是0.54 mm,小于同期MODIS Terra TPW均方根误差的标准差,说明FY-3C VIRR TPW产品在检验时期内更稳定。FY-3C VIRR TPW产品精度较高且质量稳定,具备广泛应用能力。     

Evaluation of global land cover maps for cropland area estimation in the conterminous United States

Global land cover data could provide continuously updated cropland acreage and distribution information, which is essential to a wide range of applications over large geographical regions. Cropland area estimates were evaluated in the conterminous USA from four recent global land cover products: MODIS land cover (MODISLC) at 500-m resolution in 2010, GlobCover at 300-m resolution in 2009, FROM-GLC and FROM-GLC-agg at 30-m resolution based on Landsat imagery circa 2010 against the US Department of Agriculture survey data. Ratio estimators derived from the 30-m resolution Cropland Data Layer were applied to MODIS and GlobCover land cover products, which greatly improved the estimation accuracy of MODISLC by enhancing the correlation and decreasing mean deviation (MDev) and RMSE, but were less effective on GlobCover product. We found that, in the USA, the CDL adjusted MODISLC was more suitable for applications that concern about the aggregated county cropland acreage, while FROM-GLC-agg gave the least deviation from the survey at the state level. Correlation between land cover map estimates and survey estimates is significant, but stronger at the state level than at the county level. In regions where most mismatches happen at the county level, MODIS tends to underestimate, whereas MERIS and Landsat images incline to overestimate. Those uncertainties should be taken into consideration in relevant applications. Excluding interannual and seasonal effects, R² of the FROM-GLC regression model increased from 0.1 to 0.4, and the slope is much closer to one. Our analysis shows that images acquired in growing season are most suitable for Landsat-based cropland mapping in the conterminous USA.     

基于TM/ETM+和MODIS的漓江流域生态环境遥感动态监测

为调查漓江流域近30 a来的生态环境变化，开展了基于TM/ETM+和MODIS遥感长、短周期结合的动态监测研究。通过对3期 Landsat-5/7 TM/ETM+遥感影像的信息提取、模式识别和实际调查，发现造成漓江上游生态环境退化的主要原因与漓江水源林遭人 为破坏、城市化扩展和水质污染等因素有关。通过对MODIS-NDVI和水体反射波谱分析，初步探明了流域区植被总量的季节变化规律 和漓江水体污染的遥感反射波谱特征，确定了水质遥感监测的最佳MODIS波段，得到了研究区的植被覆盖、城市扩展变化及河流水 质变化信息。     

利用BRDF原型和单方向反射率数据估算地表反照率

地表反照率是影响地表能量收支平衡的决定性参数之一,精确反演地表反照率需要考虑地表各向异性反射特征。本文尝试以双向反射分布函数BRDF原型为地表各向异性反射的先验知识,通过单方向反射率反演地表反照率。首先根据地面实测及MODIS多角度反射率数据对反演方法进行分析和精度评价,然后借助MODIS BRDF产品统计出研究区的主导BRDF原型,并联合环境一号卫星(HJ-1)单方向反射率数据反演30 m地表反照率,最终将结果与地表实测数据进行比较。结果表明:BRDF原型对BRDF的变化进行了约束,且能够适用于几十米尺度的遥感数据反照率的反演;不同级别的各向异性反射特征的分布是不均一的,借助于主导BRDF原型能够使大部分样本的地表反照率满足精度要求;利用研究区MODIS BRDF产品统计得到的主导BRDF原型为先验知识,通过HJ-1数据反演得到的地表反照率与地表实测反照率有较高的一致性,而朗伯假定条件下的反照率高于实测结果。本文算法简单高效,可为产生全国范围的中高分辨卫星反照率产品提供有价值的算法参考。     

MODIS LAI产品真实性检验与偏差分析

当前对MODIS LAI产品的真实性检验工作中,更多的是关注遥感产品在数值与趋势上与地表真值的一致性程度,很少工作能够全面分析遥感LAI产品偏差来源以及不同来源的偏差对全局偏差的贡献率。本文在对MODIS LAI产品进行真实性检验基础之上,进一步分析了MODIS LAI产品偏差来源。将遥感产品真实性检验偏差来源分解为反演模型,反射率数据和冠层聚集效应3个方面,并定量分析各个偏差源对真实性检验结果的影响。以河北省怀来玉米为研究对象,结合实测LAI数据和Landsat 8 OLI(Operational Land Imager)数据建立NDVI LAI半经验模型,得到LAI参考数据,据此对MODIS LAI产品进行真实性检验及偏差分析。研究表明,该区域MODIS LAI产品存在明显的低估现象,参考数据和MODIS LAI数据均值分别为3.53 m2/m2和2.33 m2/m2,MODIS产品低估为34.14%。在各个偏差因素中,反射率数据的差异对结果影响最大,即MODIS地表反射率数据与Landsat 8 OLI地表反射率数据的差异造成的偏差占总偏差的57.50%;聚集效应的影响次之,占总偏差的28.33%;模型差异对结果的影响最小,占总偏差的14.17%。本研究对遥感产品真实性检验及其不确定性分析具有一定的借鉴意义。     

一种拓展的半物理时空融合算法及其初步应用

Landsat 5卫星较低的时间分辨率(16天)使得其很难获得大区域的、时相一致的清晰影像数据集。本文发展了一种基于半物理模型的时空融合算法-即乘性调制融合算法,并借助多时序的MODIS反射率数据来生成多时相的Landsat TM/ETM+反射率合成影像,经镶嵌后得到区域尺度的高时空分辨率地表反射率数据集(Landsat TM/ETM+)。本文利用吉林省2006年—2011年的Landsat 5 TM地表反射率数据以及500 m的MOD09A1反射率产品来生成3个时相的Landsat 5 TM反射率合成数据,从而获得研究区在上述时相下地表反射率数据的镶嵌图。初步分析表明,所生成的Landsat 5 TM反射率数据的光谱分布特征与MOD09A1反射率数据较为一致,且图像在整体上光谱特征的连续性较好。