Plenty of leaf optical spectra datasets have been employed in the calibration and validation of leaf optical or empirical models. However, no experiment has been conducted that measures the true leaf reflectance and transmittance spectra while isolating chlorophyll fluorescence (ChlF) contributions, which results in a superimposed effect on measured reflectance or transmittance. As a result, to date, the simulated accuracy of leaf spectra in the red-edge domain remains limited due to the absence of true leaf spectra used in calibration of leaf models. This study aims to enhance the accuracy of simulating leaf spectral characteristics in the red-edge domain by combining an existing leaf physical model (PROSPECT-D) with the data-driven method that is typically used to establish leaf empirical models. A new leaf spectra dataset without ChlF contributions, including reflectance and transmittance data for 849 leaves, was first measured and replace the existing leaf spectra datasets in the modelling process. A coupled leaf optical properties model (PROSPECT-DP) was then established, in which a principal component analysis (PCA) approach was employed to model leaf spectra in red-edge domain using the spectral vectors derived from the training dataset, while the coefficients of spectral vectors were determined by leveraging the PROSPECT-D simulated leaf spectra except for the red-edge domain. Finally, validation of the PROSPECT-DP model with an independent validation dataset of 203 leaf samples showed that it performed much better than the PROSPECT-D model for spectral simulation in the red-edge domain. Furthermore, the PROSPECT-DP model exhibited better performance in leaf trait inversions with a closer relationship between measured and PROSPECT-DP-derived leaf chlorophyll a + b and carotenoid pigments compared to the PROSPECT-D model. Therefore, the novel coupled leaf model presented in this study will be highly beneficial for the integration of leaf models into canopy models and for applications in remote sensing to assess plant traits.

The continuous development of remote sensing techniques provides ample opportunities for high-resolution land-cover mapping. Although global 10 m land-cover products have made considerable progress over past few years, their simple classification system makes it difficult to meet the needs of diverse applications. In this work, we propose a hierarchical land-cover mapping framework to produce a novel global 10 m land-cover dataset with a fine classification system (called GLC_FCS10) using Sentinel-1 and Sentinel-2 time-series observations in 2023. First, the globally distributed training samples are hierarchically obtained from multisource prior products after applying a series of refinements. Then, a combination of hierarchical land-cover mapping, local adaptive modeling, and multisource features is used to produce land-cover maps for each 5x5 geographical tile. Next, using 56 121 globally distributed validation samples and a third-party validation dataset (LCMAP_Val), the GLC_FCS10 is assessed. The GLC_FCS10 achieves an overall accuracy of 83.16 % and a kappa coefficient of 0.789 globally and an overall accuracy of 85.09 % in the United States. Meanwhile, comparisons with five released 10 or 30 m land-cover products also demonstrate that GLC_FCS10 has higher accuracy and captures more diverse land-cover information than three of the released global 10 m land-cover products. In summary, the novel GLC_FCS10 land-cover maps can provide important support for high-resolution land-cover-related research and applications. The GLC_FCS10 can be freely accessed via https://doi.org/10.5281/zenodo.14729665 (Liu and Zhang, 2025).

Biomass and industrial fire points are crucial to forest fire prevention and industrial carbon emissions research. However, only a few investigations in the literature focus on fire point classification tasks using different spatial resolutions fire datasets in China. A comprehensive and accurate analysis of the spatial and temporal distribution characteristics and trends of fire points in China during the past decade is lacking. In this study, industrial heat source data, four fire point data, including VIIRS 750-m Nightfire (VNF), VIIRS 375-m Active Fire points (ACF), MODIS 1000-m fire points (MF) and Landsat-8 30 m fires data (LF), and land cover type data were integrated to more accurately classify fire points into biomass or industrial fire points. The result shows that our classification is more accurate than ACFu2019s results, and several conclusions were obtained from the spatial and temporal analysis of the total/biomass/industrial fire points across the four fire point datasets in China. (1) There was a high spatial and temporal correlation across all four datasets in China between 2012 and 2021. The annual numbers of fire points from the four fire point datasets all increased from 2012 to 2013, peaked in 2014, and have since declined. (2) The number of industrial fire points across the four datasets was static and persistent over the time series and had tight spatial aggregation, with little variation in their annual numbers and spatial distribution over time. In contrast, biomass fire points exhibited more significant changes in their spatial distribution, and the annual number declined after 2014. (3) The distribution of biomass fire points shifted northward over time, gradually moving from the Yangtze-Huai Plain and Yunnan Province in 2012 to northeastern China after 2018. These findings highlight the importance of considering temporal factors when analyzing fire point data, as well as the potential benefits of utilizing multiple datasets to achieve more accurate results.

Biomass and industrial fire points are crucial to forest fire prevention and industrial carbon emissions research. However, only a few investigations in the literature focus on fire point classification tasks using different spatial resolutions fire datasets in China. A comprehensive and accurate analysis of the spatial and temporal distribution characteristics and trends of fire points in China during the past decade is lacking. In this study, industrial heat source data, four fire point data, including VIIRS 750-m Nightfire (VNF), VIIRS 375-m Active Fire points (ACF), MODIS 1000-m fire points (MF) and Landsat-8 30 m fires data (LF), and land cover type data were integrated to more accurately classify fire points into biomass or industrial fire points. The result shows that our classification is more accurate than ACF’s results, and several conclusions were obtained from the spatial and temporal analysis of the total/biomass/industrial fire points across the four fire point datasets in China. (1) There was a high spatial and temporal correlation across all four datasets in China between 2012 and 2021. The annual numbers of fire points from the four fire point datasets all increased from 2012 to 2013, peaked in 2014, and have since declined. (2) The number of industrial fire points across the four datasets was static and persistent over the time series and had tight spatial aggregation, with little variation in their annual numbers and spatial distribution over time. In contrast, biomass fire points exhibited more significant changes in their spatial distribution, and the annual number declined after 2014. (3) The distribution of biomass fire points shifted northward over time, gradually moving from the Yangtze-Huai Plain and Yunnan Province in 2012 to northeastern China after 2018. These findings highlight the importance of considering temporal factors when analyzing fire point data, as well as the potential benefits of utilizing multiple datasets to achieve more accurate results.

Impervious surfaces are important indicators of human activity, and finding ways to quantify the gain and loss of impervious surfaces is important for sustainable urban development. However, most relevant studies assume that the transformation of natural surfaces to impervious surfaces is irreversible; thus, the losses of impervious surfaces are often ignored. Here, we propose a novel framework taking advantage of continuous change detection, multitemporal classification, and LandTrendr optimization to track the annual gains and losses in impervious surfaces. It may be the first study to focus on both loss and gain of impervious surfaces using time-series Landsat imagery. Specifically, we built dual continuous-change-detection models to pursue lower commission and omission errors for generating time-series training samples. Then, we adopted time-series classifications from multisource information and derived training samples to develop annual impervious-surface maps from 1985 to 2022 in Beijing. Afterwards, a novel optimization algorithm considering spatial heterogeneity and taking advantage of the LandTrendr algorithm was also proposed to optimize the spatiotemporal consistency of these impervious-surface maps. We further calculated accuracy metrics for the proposed method using time-series validation points, finding overall accuracies of 92.91 %+0.97 % and 93.17 %+1.26 % for gains and losses in impervious surfaces, respectively, using a one-year tolerance. Lastly, we revealed the gains and losses of impervious surfaces in Beijing during 1985-2022. The gained area of impervious surfaces was found to be 1996.21 km2 + 18.58 km2, and there was a rapid increase during 2000-2010; the total lost area of impervious surfaces was 898.60 km2 + 4.58 km2, of which 564.85 km2 + 2.21 km2 first increased and was then lost. Therefore, the proposed method provides a new way of tracking the gain and loss of impervious surfaces, and it offers new possibilities for monitoring urban regreening.

Satellite remote sensing is a promising approach for monitoring global CO2 emissions. However, existing satellite-based CO2 observations are too coarse to meet the requirements of fine-scale global mapping. We propose a novel data-driven method to estimate global anthropogenic CO2 emissions at a 0.1° scale, which integrates emissions inventories and satellite data while bypassing the inadequate accuracy of CO2 observations. Due to the co-emitted anthropogenic emissions of nitrogen oxides (NOx = NO + NO2) and CO2, high-resolution NO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) are employed to map the global anthropogenic emissions at a global 0.1° scale. We construct the driving features from NO2 data and also incorporate gridded CO2/NOx emission ratios and NOx/NO2 conversion ratios as driving data to describe co-emissions. Both ratios are predicted using a long short-term memory (LSTM) neural network (with an R2 of 0.984 for the CO2/NOx emission ratio and an R2 of 0.980 for the NOx/NO2 conversion ratio). The data-driven model for estimating anthropogenic CO2 emissions is implemented by random forest regression (RFR) and trained using the Emissions Database for Global Atmospheric Research (EDGAR). The satellite-based anthropogenic CO2 emission dataset at a global 0.1° scale agrees well with the national CO2 emission inventories (an R2 of 0.998 with Global Carbon Budget (GCB) and an R2 of 0.996 with EDGAR) and consistent with city-level emission estimates from Carbon Monitor Cities (CMC) with the R2 of 0.824. This data-driven method based on satellite-observed NO2 provides a new perspective for fine-resolution anthropogenic CO2 emissions estimation.

AbstractThe distribution of industrial heat sources (IHSs) is a crucial indicator for evaluating energy consumption and air pollution levels. However, there is a notable lack of IHS datasets in China that are frequently updated, span long periods, contain detailed characteristic information, have been individually validated and are publicly available. In this study, IHS datasets from China between 2012 and 2021 were constructed using the Visible Infrared Imaging Radiometer Suite (VIIRS) I Band 375 m NRT Active Fire/Hotspots (ACF) Product (VNP14IMGTDL_NRT) to monitor and analyse large‐scale IHSs. First, a density segmentation method based on an improved K‐means algorithm using ACF data and spatial topological correlation analysis was conducted to construct the IHS. Then, 4410 records covering China between 2012 and 2021, with 21 attributes, were obtained and verified, with an individual identification precision of 95.08% via manual verification based on high‐resolution remote‐sensing images and point of interest (POI) data. Finally, the trend of the spatiotemporal variation in IHSs was analysed using a long time series. The results showed that the spatial distribution of IHSs in China from 2012 to 2021 exhibited local aggregation and a gradual shift from east to west. In addition, the number of IHSs in China showed an initial increasing trend from 2012 to 2014, followed by a decrease since 2014, consistent with national energy reform‐related policies. The results of this study indicate the temporal variation in IHSs, enhance the precision of identifying fire location categories and demonstrate the potential for improving energy efficiency, reducing emissions and ensuring sustainable development in China.

Solar-induced chlorophyll fluorescence (SIF) provides a promising approach to monitoring plant photosynthesis. To this end, numerous retrieval algorithms have emerged and been developed to estimate ground, airborne, and satellite SIF; however, the accuracy of SIF retrieval methods in the red band is still somewhat limited. One potential obstacle that hinders the accuracy of retrieval of red SIF is the difficulty of modeling the true shape of the reflectance in the O-2-B band. To overcome this issue, herein, an improved spectral-fitting method (SFM) using principal component analysis (PCA) data-driven reflectance reconstruction method, SFM-PCA, is proposed based on a novel SIF-free reflectance dataset to improve the accuracy of SIF retrieval in the O-2-B band. In this work, the SFM-PCA method was validated using a field leaf dataset, canopy simulations, and tower-based canopy measurements. Compared to the true red SIF values at either the leaf or canopy levels, the SFM-PCA method was found to perform better than previous methods, with R-2 values of 0.97 and 0.999 for leaf measurements and canopy simulations, respectively, and corresponding normalized root-mean-square error (NRMSE) values of 6.98% and 2.335%. For the tower-based measurements, the red SIF retrieved using the SFM-PCA method was also more consistent with the O-2-A SIF. This indicates that it is feasible to make use of principal components (PCs) derived from SIF-free reflectance measurements to accurately model the true shape of the reflectance spectrum in the O-2-B band and to improve ground-based SIF retrieval in the red band. This also has the potential to be applied to the satellite-based measurements.

Solar-induced chlorophyll fluorescence (SIF) has been found to be a useful indicator of vegetation’s gross primary productivity (GPP). However, the directional SIF observations obtained from a canopy only represent a portion of the total fluorescence emitted by all the leaf photosystems because of scattering and reabsorption effects inside the leaves and canopy. Hence, it is crucial to downscale the SIF from canopy level to leaf level by modeling fluorescence escape probability (fesc) for improved comprehension of the relationship between SIF and GPP. Most methods for estimating fesc rely on the assumption of a “black soil background,” ignoring soil reflectance and the effect of scattering between soils and leaves, which creates significant uncertainties for sparse canopies. In this study, we added a correction factor considering soil reflectance, which was modeled using the Gaussian process regression algorithm, to the semi-empirical NIRv/FAPAR model and obtained the improved fesc model accounting for soil reflectance (called the fesc_GPR-SR model), which is suitable for near-infrared SIF downscaling. The evaluation results using two simulation datasets from the Soil–Canopy–Observation of Photosynthesis and the Energy Balance (SCOPE) model and the Discrete Anisotropic Radiative Transfer (DART) model showed that the fesc_GPR-SR model outperformed the NIRv/FAPAR model, especially for sparse vegetation, with higher accuracy for estimating fesc (R2 = 0.954 and RMSE = 0.012 for SCOPE simulations; R2 = 0.982 and RMSE = 0.026 for DART simulations) compared with the NIRv/FAPAR model (R2 = 0.866 and RMSE = 0.100 for SCOPE simulations; R2 = 0.984 and RMSE = 0.070 for DART simulations). The evaluation results using in situ observation data from multi-species canopies also suggested that the leaf-level SIF calculated by the fesc_GPR-SR model tracked better with photosynthetic active radiation absorbed by green components (APARgreen) for sparse vegetation (R2 = 0.937, RMSE = 0.656 mW/m2/nm) compared with the NIRv/FAPAR model (R2 = 0.921, RMSE = 0.904 mW/m2/nm). The leaf-level SIF calculated by the fesc_GPR-SR model was less sensitive to observation angles and differences in canopy structure among multiple species. These results emphasize the significance of accounting for soil reflectance in the estimation of fesc and demonstrate that the fesc_GPR-SR model can contribute to further exploring the physiological mechanism between SIF and GPP.

作物群体生物量是形成产量的物质基础,遥感技术是高效、客观监测作物地上生物量的重要手段,对农业生产管理具有重要意义.以安徽省龙亢农场为研究区,通过PROS AIL模拟光谱分析了 4个LAI相关的可见光-近红外植被指数、2个叶片干物质相关的短波红外植被指数和8个融合植被指数与冬小麦地上生物量的关系,并建立反演模型.模拟结果显示,干物质植被指数与作物生物量的相关性高于LAI相关的植被指数,两者融合的植被指数增强了常用植被指数冬小麦生物量的探测能力.利用实测冬小麦数据对生物量反演模型进行验证,结果显示:融合植被指数普遍提高了单一植被指数的地上生物量反演精度,其中MTVI2×NDMI精度最高(RMSE= 606.8 kg/hm2),并为作物地上生物量的高精度反演提供新的技术途径.

Solar-induced chlorophyll fluorescence (SIF) is closely linked to photosynthesis, and provides new opportunities for detecting global gross primary production (GPP). Instantaneous satellite SIF products are usually available only for clear-sky condition, which means that there is a temporal inconsistency in the direct link between these and continuous carbon flux records. In this study, we designed a method to upscale SIF from instantaneous clear-sky observations to all-sky sums, which adopted the absorbed photosynthetically active radiation (APAR) to correct for the effects of clouds on the SIF, and hereby derived a SIF product for all-sky conditions (ASSIF) from GOME-2 at 8-day and monthly intervals during 2007 and 2018. The advantage of ASSIF was evaluated using both tower-based experiments and satellite retrievals at different spatio-temporal scales, compared with the common clear-sky SIF upscaled using the cosine-based method (CSSIF). For time-series comparison, with tower-based experiments over a growing season, the all-sky upscaling method obviously corrected the overestimates of CSSIF made on cloudy days, and produced more accurate predictions with the retrieved SIF, and thus significantly reduced the variability in 8-day SIF-GPP correlations between sunny and cloudy days. It is the same case for the results of satellite products, but the improvements of 8-day SIF-GPP correlations were much weaker during longer time spans, for example several years, due to effects of time averaging and the dominance of seasonal growth dynamics. Notably, for spatial comparison, ASSIF products effectively reduced the remarkable overestimations in CSSIF for cloudy regions, with the largest reduction in the 12-year average being 31.15% at the fully humid and cool summer climate region (Dfc). This led to a decrease in the variability of the SIF-GPP slope over different climate regions, as well as a significant improvement for the correlation at yearly scale. The all-sky upscaling method presented here can eliminate the inconsistency between clear-sky satellite SIF and all-sky GPP, which is of importance to the understanding of SIF-GPP links and accurate estimation of GPP using satellite SIF.

在海量遥感数据背景下,传统的基于关键字/元数据数据服务模式,无法满足不同应用领域用户对多样化遥感变化信息数据的获取需求.将基于内容的图像检索技术应用到遥感图像变化信息数据获取中,提出了一种全新的基于内容的遥感图像变化信息检索概念模型.通过深入分析当前基于内容的图像检索的先进理论方法,构建基于内容的遥感图像变化信息检索模型框架,并对变化信息数据管理模型构建、多维特征提取和智能反馈模型创建等关键问题进行研究和算法实现,以中低分辨率遥感图像变化信息数据获取为例来进行模型验证与分析,建立原型系统.该方法作为一种新的遥感图像变化信息获取与服务方式,能有效利用遥感图像中底层特征,更准确地刻画了不同用户的遥感图像变化信息检索需求.同时,对影像的预处理要求较低,不受变化检测产品生产种类限制,具有较好普适性和自动化性,提高了遥感信息服务水平和效率.

The SCOPE (soil canopy observation of photochemistry and energy fluxes) model has been widely used to interpret solar-induced chlorophyll fluorescence (SIF) and investigate the SIF-photosynthesis links at different temporal and spatial scales in recent years. In the SCOPE model, the fluorescence quantum efficiency in dark-adapted conditions (FQE) for Photosystem II (fqe2) and Photosystem I (fqe1) were two key parameters of SIF emission, which have always been parameterized as fixed values derived from laboratory measurements. To date, only a few studies have focused on evaluating the SCOPE model for SIF interpretation, and the variation of FQE values in the field remains controversial. In this study, the accuracy of the SCOPE model to simulate the canopy SIF was investigated using diurnal experiments on winter wheat. First, ten diurnal experiments were conducted on winter wheat, and the canopy SIF emissions and the SCOPE model’s input parameters were directly measured or indirectly retrieved from the spectral radiances, gross primary productivity (GPP) data, and meteorological records. Second, the SCOPE-simulated SIF emissions with fixed FQE values were evaluated using the observed canopy SIF data. The results show that the SCOPE model can reliably interpret the diurnal cycles of SIF variation and provide acceptable results of SIF simulations at the O2-B (SIFB) and O2-A (SIFA) bands with RRMSEs of 24.35% and 23.67%, respectively. However, the SCOPE-simulated SIFB and SIFA still contained large systematical deviations at some growth stages of wheat, and the seasonal cycles of the ratio between SIFB and SIFA (SIFA/SIFB) cannot be credibly reproduced. Finally, the SCOPE-simulated SIF emissions with variable FQE values were evaluated using the observed canopy SIF data. The simulating accuracy of SIFB and SIFA can be improved greatly using variable FQE values, and the SCOPE simulations track well with the seasonal SIFA/SIFB values with an RRMSE of 20.63%. The results indicated a clear seasonal pattern of FQE values for unbiased SIF simulation: from the erecting to the flowering stage of wheat, the ratio of fqe1 to fqe2 (fqe1/fqe2) gradually increased from 0.05–0.1 to 0.3–0.5, while the fqe2 value decreased from 0.013 to 0.007. Our quantitative results of the model assessment and the FQE adjustment support the use of the SCOPE model as a powerful tool for interpreting the SIF emissions and can serve as a significant reference for future applications of the SCOPE model.

Recent studies have demonstrated that solar-induced chlorophyll fluorescence (SIF) can offer a new way for directly estimating the terrestrial gross primary production (GPP). The main objective of this study is to investigate whether the red or far-red SIF is a better indicator of GPP using both simulations by the SCOPE model (Soil Canopy Observation, Photochemistry and Energy fluxes) and the observations of winter wheat at the canopy level. The results showed that: (1) both far-red SIF and GPP increased with leaf area index (LAI), whereas the red SIF quickly reached its saturation with an LAI value of 2 due to the strong reabsorption effect; (2) the diurnal GPP could be robustly estimated from the SIF spectra for winter wheat at each growth stage, whereas the correlation weakened greatly at the red band if all the observations made at different growth stages or all the simulations with different LAI values were pooled together - a situation that did not occur at the far-red band; (3) the SIF-based GPP models derived from the 2016 observations were well validated using the data set from 2015, with a root mean square error (RMSE) value of 0.128 and 0.133 (mg m(-2) s(-1)) at the oxygen-A (O-2-A) band and oxygen-B (O-2-B) band, respectively. Therefore, the far-red SIF may be more reliable for mapping GPP for remote-sensing applications with heterogeneous and diverse vegetation growth conditions.

Accurate estimation of gross primary production (GPP) is of great importance to global change research and also food and fuel security. Previous studies have demonstrated that values of sun-induced chlorophyll fluorescence (SIF) retrieved from hyperspectral data provide a direct measure of ecosystem GPP. However, global analysis of the relationship between satellite SIF and model-based GPP indicates that the relationship between the two parameters is highly dependent on the plant functional type (PFT). The overarching goal of this study is to examine the potential of far-red SIF retrieved at 760nm (SIF760) to track the diurnal variations in GPP for C3 and C4 crops, and to investigate whether the GPP SIF relationship is dependent on the type of photosynthesis. GPP values are estimated from flux tower records and daily SIF760 data are derived from ground-based spectral measurements. The results show that GPP and SIF760 have similar diurnal patterns and are linearly correlated for C3 and C4 crops. However, the ratio of epsilon(P) to epsilon(F) (the slope of the linear SIF-based GPP model, GPP= epsilon(P)/epsilon(F) x SIF) for C3 wheat is about 46% of that for C4 maize. The findings from the diurnal variation experiments imply that theeF is weakly sensitive to the photosynthetic pathway type (PsP type) and that the large difference in sp between C3 and C4 crops leads to the difference in the slopes. Our studies confirm the capability of the remotely sensed SIF signals to act as a direct proxy for GPP and suggest that the PsP type should be considered when trying to accurately quantify the ecosystem productivity using the straightforward empirical approach. (C) 2016 Elsevier B.V. All rights reserved.

The papers in this proceeding are contributions written by participants at the 6th Digital Earth Summit held 7-8 July, 2016 in Beijing, China. Under the theme "Digital Earth in the Era of Big Data", the 6th Digital Earth Summit aims to provide an international platform to focus on the application and technologies of Digital Earth for new developments and advances in the context of big data. In total, over 184 abstracts were selected for presentations in "Digital Earth Application in the Context of Big Data", "Big Data and Mountain Surface Process", and "The Role of Open Standards in the Era of Big Data" at 13 scientific sessions and 2 poster sessions. The main topics of the Summit are well represented by the papers in this proceeding. The Co-Chairs of the Summit was Huadong GUO (Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences) and Alessandro ANNONI (Joint Research Center, European Commission). The Organizing Committee for the Summit was chaired by Yongwei LIU (Chinese National Committee of International Society for Digital Earth) and Xinyuan WANG (Key Laboratory of Digital Earth Sciences, Chinese Academy of Sciences).

Solar-induced chlorophyll fluorescence (SIF) is related to photosynthesis and can serve as a remote sensing proxy for estimating photosynthetic energy conversion and carbon uptake. In this paper, three key factors affecting the relationship between SIF and gross primary production (GPP) were investigated using both models and observations on winter wheat (C3 crop) and maize (C4 crop). Firstly, the bidirectional SIF emission was investigated by multi-angular spectral measurement, which was found to be similar to that of the canopy reflectance in the solar principal plane. Secondly, the wavelength dependent predictive power of SIF to estimate GPP was assessed using diurnal observations on winter wheat. As indicated by the preliminary studies, the far-red SIF may be more reliable for remote sensing of GPP than the red SIF due to the heterogeneous and diverse vegetation growth status at regional or global scale. Finally, the potential of far-red SIF to track the diurnal and seasonal variations in GPP for C3 and C4 crops was investigated, and the result show that the GPP SIF relationship is dependent on the type of photosynthesis.

æœ¬æ–‡ç ”ç©¶äº†LAIäº§å“çœŸå®žæ€§æ£€éªŒçš„æŒ‡æ ‡å’Œæ–¹æ³•,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3ä¸ªæ–¹é¢ã€‚ä»¥å† è’™å¤é”¡æž—æµ©ç‰¹è‰åŽŸä¸ºç ”ç©¶åŒº,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"æŒ‰ç §æœ¬æ–‡çš„æµç¨‹å¯¹MODIS LAI产品进行验证,åˆ†æžäº†ç ”ç©¶åŒºMODIS LAIäº§å“çœŸå®žæ€§æ£€éªŒçš„è¯¯å·®æ¥æºã€‚ç ”ç©¶è¡¨æ˜Ž,è¯¥ç ”ç©¶åŒºçš„MODIS LAI（MOD15A2）产品相对高估约25%ã€‚å„ä¸ªè¯¯å·®å› ç´ ä¸­,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 TMåœ°è¡¨åå°„çŽ‡æ•°æ®çš„å·®å¼‚é€ æˆäº†çº¦16.2%的低估;尺度效应的影响较小,é€ æˆçº¦3.1%的低估,å ¶ä¸­NDVI-LAI模型的尺度效应带来2.4%的低估,NDVIæ•°æ®çš„å°ºåº¦æ•ˆåº”é€ æˆçº¦0.7%的低估。