中国西南山区GEOV1、GLASS和MODIS LAI产品的对比分析

叶面积指数(Leaf Area Index,LAI)是表征植被生物物理变化和冠层结构特征的关键参数,目前存在多个全球范围、长时间序列LAI产品,对其进行验证是LAI产品应用的重要前提,然而目前山区的验证工作尤其少见。在我国西南山区选取6个典型样区,考虑山区复杂地形特征,从产品时空完整性以及对山区植被时空特征表征能力等方面对GEOV1、GLASS和MODIS LAI产品进行对比分析。研究结果表明:(1)相比于地形平坦地区,在山区随海拔和地形起伏度的增加,LAI产品时空完整性呈递减的趋势,其中,GEOV1LAI表现最差,MODIS LAI次之,GLASS LAI表现最好;(2)GLASS LAI和GEOV1LAI的空间分布合理且具有较好的一致性,MODIS LAI的空间分布和二者存在差异,3种LAI产品均难以准确反映山区植被垂直带谱的变化特征;(3)草地类型LAI产品间差值较小,林地和农作物GLASS LAI和GEOV1LAI产品一致性较好,MODIS LAI产品和二者存在较大的差异;(4)GLASS LAI时间序列曲线平滑且连续,GEOV1LAI存在时间不连续现象,MODIS LAI季相变化中的波动现象比较严重;各产品不仅难以准确反映冬季的常绿针叶林LAI,而且难以准确表征样区内农田作物轮作的物候信息。对比分析有助于发现LAI产品在山区存在的问题,并为今后LAI产品的算法改进提供帮助和参考。     

内蒙古草原叶面积指数时空格局与水热影响

叶面积指数(Leaf Area Index,LAI)是表征植被状况的重要参数,与植被的生长和变化状况密切相关.探究内蒙古草原LAI长时间序列时空格局特征及水热条件对LAI的影响,可为准确掌握内蒙古草原分布与生长状况差异提供数据支撑,对了解内蒙古草原生产能力的空间分布特征具有参考价值.基于2000～2019年GEOV2 ...     

西南地区不同山地环境梯度叶面积指数遥感反演

叶面积指数(LAI)遥感估算是植被定量遥感研究的热点之一,监测植被LAI时空变化对于研究陆地生态系统碳循环及全球变化等具有非常重要的意义。在我国西南山区设置10个50km×50km的观测样区作为研究区,其中包括5个森林生态系统样区、3个农田生态系统样区和2个草地生态系统样区。分别获取不同优势植被类型LAI地面实测数据,结合同期获取的遥感数据,考虑地形因素影响,基于偏最小二乘原理分别构建各样区LAI遥感估算模型,并采用交叉验证的方式对模型精度进行评价。结果表明:考虑了海拔、坡度和坡向等地形因子的森林LAI遥感反演模型与未考虑地形变量的模型相比,其验证精度有所提高,R2由0.30~0.75提高至0.50~0.80,RMSE由0.52~0.93m2/m2降低至0.48~0.89m2/m2;所有样区优势植被类型LAI反演模型验证R2在0.40~0.80之间,RMSE在0.22~0.89m2/m2之间。发展的LAI遥感估算方法有助于认知山地植被LAI反演的地形效应问题,可为进一步的山地植被长势监测提供科学依据。     

巴音河流域高时空分辨率叶面积指数数据集

叶面积指数（Leaf Area Index，LAI）是表征地表特征变化的重要指标之一，也是陆表、水文等模型的重要参数。本数据集是基于增强型时空自适应反射率融合模型（ESTARFM），将全球陆地表层卫星（GLASS）LAI（8d/500m）、中分辨率成像光谱仪（MODIS）MOD13A1和MYD13A1、陆地卫星Landsat-7 ETM+和Landsat-8 OLI数据，进行融合，得到8 d/30 m分辨率的LAI，通过分段线性内插最终得到巴音河流域高时空分辨率LAI（1 d/30 m）。对比高时空分辨率LAI（1 d/30 m）与GLASS LAI产品的时空特征，验证数据集精度。结果表明：与原始GLASS LAI相比，本数据集在空间上具有与GLASS LAI一致的分布特征，且轮廓与纹理更为清晰。在时间上，二者具有相同的月际变化特征，且由1 d/30 m LAI估算的区域月平均LAI和区域8日平均LAI与原始GLASS LAI存在显著正相关性，R²分别为0.95、0.94，Pearson积矩相关系数均为0.97，P值均小于0.01。此数据集可为陆表过程、水文循环等模拟提供重要的数据支持，为监测植被-陆表-大气循环的变化提供重要依据。     

全球典型植被叶片光谱特征及其对LAI反演的影响分析

在全球范围长时间序列LAI遥感产品反演算法中,植被冠层反射率模型仅使用少量叶片光谱特征代表全球植被全年的典型植被光谱特征,叶片光谱的不确定性导致LAI遥感产品存在一定的误差。目前全球已经构建了多个典型植被叶片波谱数据集,这些数据集包含多个植被物种、不同空间地域及多时相叶片光谱数据,为定量分析叶片光谱特征提供了数据支持。主要利用LOPEX’93、ANGERS’03、中国典型地物波谱数据库和野外实测的叶片光谱数据,以黄边参数、红边参数和叶片光谱指数作为分析指标,探讨不同植被物种、不同气候区和不同物候期的叶片光谱特征差异,及其对植被冠层反射率、LAI反演的影响,为发展考虑现实叶片光谱差异的LAI反演算法提供研究基础。结果表明:植被叶片光谱存在多样性,叶片光谱特征差异主要影响MODIS传感器近红外波段和绿波段反射率值,其中,绿波段反射率值对叶片光谱变化最为敏感;在LAI反演算法中,如果只考虑植被类型而不考虑物种叶片光谱差异,可能会给LAI反演带来大于3的误差。     

国产叶面积指数产品在中国区域的一致性检验北大核心CSCD

叶面积指数是研究全球和区域碳循环、水文循环、气候变化区域响应的重要参数之一,研究不同LAI产品的时空一致性可为该地区LAI产品的使用提供建议和参考。本研究基于流域、DEM和土地利用类型,对GLOBMAP、GLOBALBNU、GLASS的LAI产品从平均值、频率以及差值频率等的变化进行统计,分析3种国产LAI产品在中国区域的一致性。主要结论为:①3种产品均可捕捉中国地区LAI的空间分布和月及年的时间变化特征,GLOBMAP在2001年更换数据源后年平均值开始下降。②3种产品在九大流域、不同DEM、不同地表利用类型分类下均存在差异。在海河流域、黄河流域和内陆河流域,3种产品的相关性较好,但是在长江流域、东南诸河流域以及珠江流域内产品间差值大于2.00的范围较多。2000~4000 m区域内3种产品的年均变化趋势区别存在明显不同。和其他产品相比,在草地区域GLASS较低,在城乡工业用地区域GLOBMAP较低,在林地区域GLOBALBNU较高。定量分析了3套国产LAI产品的时空差异,结果可为国产LAI产品在中国的应用提供科学参考。     

基于机载LVIS和星载GLAS波形LiDAR数据反演森林LAI

波形激光雷达（Light Detection And Ranging, LiDAR）已经大量用于森林叶面积指数（Leaf Area Index, LAI）估算,但是波形LiDAR数据估算森林LAI易受地形影响。地形坡度引起的波形展宽使得地面回波和植被冠层回波信息混合在一起,难以得到准确的地面回波和冠层回波,进而影响到LAI估算精度。为了估算不同地形坡度条件下的LAI,本文采用一种坡度自适应的方法处理机载LVIS和星载GLAS波形数据。通过坡度自适应的方法得到地面波峰位置,基于高度阈值来区分地面回波和冠层回波,进而得到能量比值用于LAI估算。基于LVIS和GLAS数据,估算了不同森林站点的LAI,并利用实测LAI数据进行检验。结果表明：利用波形LiDAR数据可以估算森林LAI,坡度自适应方法可以改善地形的影响,提高LAI估算精度。对于机载LVIS,估算新英格兰森林LAI精度为R²=0.77和RMSE=0.21;对于星载GLAS,估算塞罕坝森林LAI精度为R²=0.81和RMSE=0.28。无论机载还是星载数据,该方法都有着较高的精度,对于复杂地形估算LAI具有一定潜力。     

基于时空层级查询的指代视频目标分割

本文针对当前指代视频目标分割方法缺乏目标时空一致性建模和目标时空表征学习不足等问题,进行了深入的研究,提出了基于时空层级查询的指代视频目标分割方法 (STHQ).本文将指代视频目标分割看作基于查询的序列预测问题,并提出两级查询机制进行目标的时空一致性建模和时空特征学习.在第1阶段,本文提出了帧级空间信息提取模块,该模块使用语言特征作为查询独立地和视频序列中的每一帧在空间维度进行信息交互,生成包含目标空间信息的实例嵌入;在第2阶段,本文提出时空信息聚合模块,该模块使用视频级的可学习查询嵌入和第1阶段生成的实例嵌入在时空维度进行信息交互,生成具有时空表征信息的视频级实例嵌入;最后,视频级实例嵌入线性变换为条件卷积参数,并和视频序列中的每一帧进行卷积操作,生成目标的掩码预测序列.在该领域的3个基准数据集上的实验结果表明,本文提出的STHQ方法超越了现有的方法,实现了最佳的性能.     

面向遥感叶面积指数产品的地形校正研究

地形校正是提高复杂地形区地表参数遥感定量化反演精度的重要手段。当前广泛应用的遥感叶面积指数产品（Leaf Area Index, LAI）多具有一定的地形误差,减少地形影响、提升其产品精度有着非常重要的意义。以我国江西省千烟洲地区为研究区域,利用地面实测LAI数据、LandsatTM数据和高程数据等,基于高程标准差和GLOBMAP LAI产品值的关系,建立面向叶面积指数产品的地形校正模型,利用这一模型对GLOBMAP LAI产品进行地形校正。结果表明：校正后的LAI与地面实测数据更为接近,LAI产品与地面测量值的RMSE由2.11下降到2.04;校正后LAI产品的标准差由2.08下降至1.69,LAI产品的地形误差得到了较好的改正。该方法较好地完成了LAI产品的地形校正,进一步提高了产品精度,具有一定的实用价值。     

黑河及汉江流域MODIS叶面积指数产品质量评价

叶面积指数（LAI）是MODIS地面队伍生产的一系列标准产品之一，对其进行独立的质量评价有助于用户了解数据的适用性。本文用近同时相的Landsat影像及野外实测LAI数据获得了黑河及汉江两个研究区高分辨率的Landsat LAl分布图。基于此，对MODIS LAI数据进行了质量评价，评价指标包括统计特征和空间特征。分析结果表明，就统计特征而言，MODIS LAI数据值一般低于Landsat的LAI值，在植被覆盖较好的汉江区低估约10％，在植被覆盖稀疏的黑河区，LAI值低估达58％；就空间特征而言，两个研究区的结果都表明MODIS LAI数据无法很好地体现植被空间分布信息，在黑河区存在大量低槽被覆盖像元被归类为非植被覆盖区的情况。     

内蒙古锡林浩特草原GLASS LAI产品的真实性检验

结合内蒙古锡林浩特草原区域的实地测量数据和Landsat TM高分辨率遥感数据,对两景GLASS LAI产品进行真实性检验,同时以MODIS LAI产品为比照。结果表明:①GLASS LAI产品和MODIS LAI产品均高估,其中7月中旬GLASS LAI产品高估约11%,其高估的程度明显小于MODIS LAI(高估约36%),而8月末两者高估的程度相似;②GLASS LAI产品与Landsat TM反演LAI产品有较好的一致性,两期数据的决定系数R²分别达到0.72和0.58,优于MODIS LAI产品(0.61和0.27);③GLASS LAI和MODIS LAI两个产品的误差主要来自于模型的准确性,真实性检验中数据定量化误差小于5%。结论表明:GLASS LAI产品数据在锡林浩特草原区域的观测精度和一致性都优于MODIS LAI产品数据,更适用于相关研究。     

SPOT-VEGETATION GEOV1 biophysical parameters in semi-arid agro-ecosystems

The VEGETATION system, which has been delivering global observations of the surface on a daily basis since 1998, provides key information for regional to global climate, environmental and natural resource management applications. Just recently, VEGETATION-derived GEOV1 biophysical products (LAI, FAPAR, and FCOVER) became available for the scientific community and were evaluated in this study for semi-arid forests in the Dry Chaco ecoregion, Argentina. Indirect validation with the MODIS-derived biophysical products (MOD15A2) shows a very good temporal consistency between both products for the period 2000–2011, with a remarkably smooth behaviour of the GEOV1 products. A good relationship between both products was found in the regression analysis with an R² of 0.826 and 0.724 for LAI and FAPAR, respectively. Using direct validation with digital hemispherical photography (DHP) and ceptometer ground measurements, a relatively small RMSE (RMSE_LAI ≈ 0.31 and RMSE_FAPAR ≈ 0.11) was found. The novel PASTIS-57 technique, which can derive continuous plant area index (PAI) estimates from light transmittance measurements, shows a similar temporal profile to the GEOV1 LAI product with a relatively high but constant offset for the dry forest study sites and a nearly identical profile for the deforested site (R² = 0.86). Overall, PASTIS-57, in combination with satellite-based observations, shows potentials in LAI/PAI research and ecosystem carbon studies in general, but more ground measurements taken over multiple growing seasons and vegetation types are required to confirm these findings.     

HJ-1A NDVI与MODIS NDVI时间序列提取植被物候特征对比研究

以HJ-1A和MODIS为数据源,通过动态阈值法提取物候特征参数,对HJ-1A NDVI和MODIS NDVI时间序列进行植被物候特征提取进行定性和定量比较,通过比较结果,提出HJ-1A NDVI数据在该应用中存在的问题,促进国产中空间高时间分辨率影像数据在植被物候信息提取研究中的应用,提高其在生态系统研究中的应用价值。结果表明:在SOS、EOS和LOS以及TOMS几个主要的物候时间点上,MODIS NDVI时间序列的标准差较小,所得物候数据更为集中,偏离度较小,所得物候数据较稳定;而HJ-1A NDVI时间序列所得物候数据的标准差较大,数据偏离程度较大,而在POS、BOS和AOS等表征植被生命周期中生长幅度数据上,其标准差较小,离散程度小。     

Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products

This paper evaluates the performances of a neural network approach to estimate LAI from CYCLOPES and MODIS nadir normalized reflectance and LAI products. A data base was generated from these products over the BELMANIP sites during the 2001-2003 period. Data were aggregated at 3 km × 3 km, resampled at 1/16 days temporal frequency and filtered to reject outliers. VEGETATION and MODIS reflectances show very consistent values in the red, near infrared and short wave infrared bands. Neural networks were trained over part of this data base for each of the 6 MODIS biome classes to retrieve both MODIS and CYCLOPES LAI products.Results show very good performances of neural networks to estimate the original LAI products with an overall root mean square error (RMSE) around 0.5 for MODIS LAI from both MODIS and CYCLOPES normalized reflectances and a RMSE ranging between 0.12 (CYCLOPES reflectances) and 0.29 (MODIS reflectances) for CYCLOPES LAI. A drop of 15% of performance was found by training MODIS biome dependant algorithm by a single network over all the classes at the same time. More detailed analyses show that CYCLOPES and MODIS LAI values are very consistent for grasses and crops. Conversely, other biomes including shrubs, savanna, needleleaf and broadleaf forests show significant discrepancies, mainly due to differences between LAI definitions used between CYCLOPES (closer to effective LAI) and MODIS (closer to true LAI). However, products derived from the original CYCLOPES LAI products show a better agreement with both effective and true LAI ground measurements values. MODIS LAI products show more instability, partly because of the slightly shorter temporal resolution as compared to CYCLOPES.These results confirm the interest and versatility of neural networks for operational algorithms. This approach could be extended to other products or sensors, and may constitute a step forward for the fusion of data from several sensors, hence contributing to develop ‘virtual constellations’.     

Analysis of leaf area index products from combination of MODIS Terra and Aqua data

A prototype product suite, containing the Terra 8-day, Aqua 8-day, Terra-Aqua combined 8- and 4-day products, was generated as part of testing for the next version (Collection 5) of the MODerate resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) products. These products were analyzed for consistency between Terra and Aqua retrievals over the following data subsets in North America: single 8-day composite over the whole continent and annual time series over three selected MODIS tiles (1200 × 1200 km). The potential for combining retrievals from the two sensors to derive improved products by reducing the impact of environmental conditions and temporal compositing period was also explored. The results suggest no significant discrepancies between large area (from continent to MODIS tile) averages of the Terra and Aqua 8-day LAI and surface reflectances products. The differences over smaller regions, however, can be large due to the random nature of residual atmospheric effects. High quality retrievals from the radiative transfer based algorithm can be expected in 90-95% of the pixels with mostly herbaceous cover and about 50-75% of the pixels with woody vegetation during the growing season. The quality of retrievals during the growing season is mostly restricted by aerosol contamination of the MODIS data. The Terra-Aqua combined 8-day product helps to minimize this effect and increases the number of high quality retrievals by 10-20% over woody vegetation. The combined 8-day product does not improve the number of high quality retrievals during the winter period because the extent of snow contamination of Terra and Aqua observations is similar. Likewise, cloud contamination in the single-sensor and combined products is also similar. The LAI magnitudes, seasonal profiles and retrieval quality in the combined 4-day product are comparable to those in the single-sensor 8-day products. Thus, the combined 4-day product doubles the temporal resolution of the seasonal cycle, which facilitates phenology monitoring in application studies during vegetation transition periods. Both Terra and Aqua LAI products show anomalous seasonality in boreal needle leaf forests, due to limitations of the radiative transfer algorithm to model seasonal variations of MODIS surface reflectance data with respect to solar zenith angle. Finally, this study suggests that further improvement of the MODIS LAI products is mainly restricted by the accuracy of the MODIS observations.     

基于被动微波与时空联合算法的云下像元LST重建

地表温度作为衡量地球表面水热平衡的关键参数,具有两大时空分布特征:第一,空间分布一致性,即属性相近的像元地表温度与其地表亮温间的相关关系相对稳定;第二,时间序列周期性,且同一地区时间越接近地表温度值越相似。基于这两大特征将空间统计模型与时间序列滤波相结合,提出了用于云下像元地表温度重建的时空联合算法。以2008年MODIS地表温度产品为研究对象,采用Landsat TM数据和AMSR_E地表亮温数据重建中国9个省份的地表温度值,并与基于MODIS地表分类产品的多通道统计模型重建结果进行对比。实验结果表明,所提算法实用性强,能有效实现大面积复杂下垫面区域的地表温度重建;平均重建误差约为1.2 K,相较于基于下垫面分类的多通道统计模型下降了76%,算法精度明显提高。
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呼伦贝尔草甸草原MODIS/LAI产品验证

以内蒙古呼伦贝尔草甸草原为研究区域,利用2013年6期地面实测数据,结合HJ-1A/B CCD高分辨率影像,经过辐射校正与模型建立,对研究区域草原生长季的MODIS/LAI产品进行验证。结果表明:在时间上,MODIS/LAI产品能够较好地反映草原的长势与物候变化。在空间上,由于MODIS/LAI产品输入数据的不确定性,MODIS/LAI产品与地面情况存在一定偏差(ΔLAI=0.59m2/m2),在呼伦贝尔草甸草原草场整个生长季都存在高估现象,平均相对误差为40%。在生长初期和末期,较大的地表异质性使MODIS/LAI产品高估现象较严重;生长中期高估现象减小,相对误差在30%以内。研究结果对了解该地区的MODIS/LAI产品精度与使用该地区MODIS/LAI产品具有重要指导意义。     

A multisensor fusion approach to improve LAI time series

High-quality and gap-free satellite time series are required for reliable terrestrial monitoring. Moderate resolution sensors provide continuous observations at global scale for monitoring spatial and temporal variations of land surface characteristics. However, the full potential of remote sensing systems is often hampered by poor quality or missing data caused by clouds, aerosols, snow cover, algorithms and instrumentation problems. A multisensor fusion approach is here proposed to improve the spatio-temporal continuity, consistency and accuracy of current satellite products. It is based on the use of neural networks, gap filling and temporal smoothing techniques. It is applicable to any optical sensor and satellite product. In this study, the potential of this technique was demonstrated for leaf area index (LAI) product based on MODIS and VEGETATION reflectance data. The FUSION product showed an overall good agreement with the original MODIS LAI product but exhibited a reduction of 90% of the missing LAI values with an improved monitoring of vegetation dynamics, temporal smoothness, and better agreement with ground measurements.     

Variational retrieval of leaf area index from MODIS time series data: examples from the Heihe river basin,north-west China

Leaf area index (LAI) products retrieved from observations acquired on one occasion have obvious discontinuity in the time series owing to cloud coverage and other factors, and the accuracy may not meet the needs of many applications. Effectively utilizing data assimilation techniques to retrieve biophysical parameters from the time series of remote-sensing data has attracted special interest. The data assimilation technique is based on a reasonable consideration of dynamic change rules of biophysical parameters and time series observational quantities, thereby improving the quality of retrieved profiles. In this article, a variational assimilation procedure for retrieving LAI from the time series of remote-sensing data is developed. The procedure is based on the formulation of an objective function. A dynamic model is constructed based on the climatology from multi-year Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data to evolve LAI in time, and a radiative transfer model is coupled with the dynamic model to simulate a time series of surface reflectances. A shuffled complex evolution method (developed at the University of Arizona; SCE-UA) optimization algorithm is then used to minimize the objective function and estimate the dynamic model states and the parameters of the coupled model from the MODIS reflectance data with a higher quality in a given time window. The variational assimilation method is applied to the MODIS surface reflectance data for the whole of 2008 at the Heihe river basin to produce regional LAI mapping results. The ground LAI data measured in situ are used to develop algorithms to estimate LAI from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) surface reflectance, and ASTER LAI maps are produced for each ASTER scene using the algorithms developed. Then the ASTER LAI maps are aggregated to compare with the new LAI products. It is found that the variational assimilation method is able to produce temporal continuous LAI products and that accuracy has been improved over the MODIS LAI standard product.