The Normalized Difference Vegetation Index (NDVI) is widely used for vegetation monitoring, phenological analysis, and ecosystem assessment. However, obtaining continuous daily high-resolution NDVI time series remains challenging because high-resolution satellite observations are often affected by long revisit intervals and cloud contamination. Existing reconstruction methods generally rely on dense observations or auxiliary datasets, limiting their applicability in data-sparse regions. In this study, we propose a method by extracting phenological archetypes from tower observations to reconstruct GF NDVI time series. The proposed method successfully reconstructed daily NDVI for 2024 over a forested area. The results show that the reconstructed NDVI achieves an RMSE of 0.100 at 16 m resolution (R2=0.942) and 0.158 at 1.5 m resolution (R2=0.789), compared with tower observations. The method enhances spatial detail by more than five times relative to the original GF NDVI while maintaining temporal consistency, requiring only tower observations and limited satellite data. These results demonstrate the potential of the proposed approach for high-resolution daily NDVI reconstruction in data-sparse regions.

Fractional vegetation cover(FVC)serves as a crucial indicator for assessing the health status of terrestrial vegetation ecosystems.Currently,most global FVC products derived from satellite remote sensing have a temporal resolution of 8-10 days,which proves insufficient for capturing vegetation dynamics during rapid growth periods.Based on the gap probability theory,this study utilized the global GLASS leaf area index(LAI)product with a spatial resolution of 5 km and a temporal resolution of 8 days,along with a priori clumping index information,to generate a global FVC product at 5 km/8-day resolution from 1981 to 2000.Furthermore,the global MuSyQ LAI product with a spatial resolution of 500 m and a temporal resolution of 4 days,together with clumping index products,was used to generate a global FVC product at 500 m/4-day from 2001 to 2020.Validation using ground-based measurement from the VALERI and IMAGINES projects showed that the product achieved a root mean square error(RMSE)of 0.15 and a mean relative error of 12.56%.This product provides data support for long-term global vegetation dynamics monitoring.

Characterizing forest canopy height is fundamental to understanding forest structural complexity and supports a wide range of applications, including aboveground biomass estimation, carbon stock quantification, and ecosystem monitoring. However, optical remote sensing remains limited in forest canopy height retrieval because conventional vegetation indices primarily respond to canopy biochemical properties and often saturate in structurally complex forests. Although bidirectional reflectance distribution function (BRDF) observations contain abundant structural information, the potential of hotspot signals for characterizing forest vertical structure has not been fully explored. Here, we demonstrate that BRDF hotspot information provides a robust optical proxy for forest canopy height through the development of a hotspot canopy structure index (HCSI) derived from MODIS observations. Firstly, the response of the HCSI to variations in forest canopy height was investigated through three-dimensional radiative transfer simulations implemented in the LESS model. Subsequently, we validated using airborne LiDAR measurements. Results showed that the HCSI exhibited strong sensitivity to forest canopy height and maintained responsiveness in forests exceeding 50 m in height without evident saturation. Spatial comparisons involving more than 17,000 pixels revealed that the HCSI structural patterns closely matched LiDAR-observed forest canopy height distributions, demonstrating that the HCSI effectively reflects landscape-scale differences in canopy structural characteristics. The HCSI explained forest canopy height variability with an R² of 0.69, while incorporating pixel-level heterogeneity metrics further improved retrieval accuracy to R² = 0.73 and RMSE = 5.12 m. Moreover, the HCSI reconstructed from different BRDF models remained highly consistent (R² = 0.87–0.94). These findings indicate that BRDF hotspot signals encode fundamental information on forest three-dimensional structure and provide a physically meaningful pathway for large-scale forest structural characterization using optical remote sensing observations.

Global Aboveground forest Biomass(AGB)products have become increasingly abundant in recent years,providing valuable data for assessing carbon stocks and fluxes.However,substantial spatiotemporal inconsistencies among these products have led to large uncertainties in the estimation of global carbon storage and carbon sink strength.This study aims to evaluate systematically the interannual consistency of major global AGB products and identify their strengths and limitations for long-term biomass monitoring and carbon accounting.We integrated multi-source forest biomass data of AGB data,including satellite-derived products,e.g.,European Space Agency Climate Change Initiative(CCI),NASA Jet Propulsion Laboratory(JPL),Dynamic Global Vegetation Model(DGVM)simulations(net biome poductivity,carbon in vegetation),and ground-based validation datasets.A multidimensional evaluation framework was designed from three perspectives:(1)spatial consistency,assessed using correlation coefficients and spatial agreement metrics among products;(2)interannual variability,analyzed through temporal correlation and trend consistency;(3)ground validation,performed using field observations to quantify product accuracy in regions of biomass increase and decrease.The results show the following:(1)different remote sensing products exhibit pronounced differences in spatial consistency.Single-epoch products based on Global Ecosystem Dynamics Investigation(GEDI)and Ice,Cloud,and Land Elevation Satellite-2(ICESat-2)show relatively high spatial consistency(ρc>0.7),with the most significant consistency found in tropical forest regions of South America and Africa.Long-timeseries remote sensing products(CCI and JPL)demonstrate higher consistency in interannual variability compared with other products.(2)For long-timeseries biomass products,interannual consistency improves to a certain extent as time span increases.However,CCI and JPL show fewer regions of consistency in high-latitude areas(40°—60° N/S).JPL exhibits higher interannual variability values in Asia compared with in other regions.By contrast,DGVM data indicate substantially higher interannual variability in tropical regions(20°S—20°N),with an overall tendency toward carbon sink estimates(proportion of pixels with increasing trends>80%).Nevertheless,in high-latitude regions,interannual variability estimated by DGVM diverges strongly from that of remote sensing products.(3)Ground validation indicates that CCI performs better in regions of biomass increase(r=0.36,RMSE(root mean square error)=8.54 Mg/hm2),but performs poorly in regions of biomass decrease(r<0.15,RMSE>14 Mg/hm2).Both result show some degree of underestimation,though the bias is smaller than that of DGVM simulations.This study provides a comprehensive,multidimensional assessment of global AGB product consistency from spatial,interannual variations,and ground-based perspectives.The results highlight that although GEDI-based and ICESat-2-based products ensure reliable spatial distribution,long-timeseries products,such as CCI and JPL,offer better interannual stability for tracking biomass dynamics.The DGVM outputs complement remote sensing data in capturing large-scale carbon flux trends but require further calibration in high-latitude regions.Overall,the findings provide a scientific basis for selecting,integrating,and applying multisource AGB datasets to improve the accuracy and reliability of carbon monitoring and ecological assessment at the global scale.

A soil radiative transfer model (RTM) capable of simultaneously characterizing the spectral and directional reflectance of soil is essential for the accurate retrieval of soil-related parameters. However, most existing soil RTMs exhibit inherent limitations in jointly simulating spectral-directional behavior. For instance, the Hapke model primarily focuses on directional reflectance and lacks an explicit spectral formulation, while the MARMIT-2 model is designed for spectral modeling but does not explicitly account for directional effects. These limitations restrict the applicability of current models under complex surface conditions, particularly in the presence of variations in soil moisture and texture. To address these issues, we propose an unified soil RTM framework capable of modeling soil reflectance across both dry and wet conditions. Firstly, we develop the dry soil model (i. e., Hapke-BSM, HB) by coupling the Hapke model and dry soil components of the brightness-shape-moisture (BSM) model. Then, we propose a wet soil model (i.e., Hapke-BSM-improved MARMIT-2, HBiM2), which integrates the HB model with an improved multilayer RTM of soil reflectance (MARMIT) model framework that explicitly accounts for directional effects. Finally, the proposed soil models are validated using various datasets covering 10 soil textures, 14 particle sizes, multiple moisture levels, and a wide range of observational geometries over the 0.4-2.4 mu m spectral range. The HB model demonstrates high accuracy across diverse conditions (R-2 > 0.98, RMSE <0.02), while the HBiM2 model exhibits consistently robust performance under varying moisture and angular conditions (R-2 > 0.95, RMSE <0.02). These results indicate strong agreement between simulated and measured reflectance under controlled conditions, supporting the proposed HBiM2 model framework, in which the HB component effectively characterizes dry soil reflectance.

Surface reflectance varies with changes in solar and viewing angles, exhibiting distinct anisotropic reflectance characteristics, which can be characterized using the bidirectional reflectance distribution function (BRDF). Normalizing reflectance from various directions to a common solar-viewing geometry can eliminate the effects of reflectance anisotropy, thereby enhancing the accuracy of surface parameter retrieval from reflectance data. In previous studies, the reflectance angular normalization typically relies on prior knowledge extracted from coarse-resolution BRDF products. In this study, we developed a universal BRDF archetype database and explored the applicability of each BRDF archetype across various solar and viewing angles. The results indicate that the following: 1) The 3 x 3 universal BRDF archetype database is sufficient to represent the surface anisotropy of different spectral bands. 2) The optimal BRDF archetype in the lookup table (LUT) undergoes notable variations under different solar and viewing geometries, and specific prior BRDF knowledge is applicable only to certain solar and viewing geometries. 3) Validations based on simulated MODerate resolution imaging spectroradiometer reflectance suggests that in the red and near-infrared (NIR) bands, when the observation zenith angle is 60 degrees, the percentages of directions with the root mean square error (RMSE) values below 0.05 and 0.06 constitute 82.74% and 75.70% of the entire viewing hemisphere, respectively. And validations based on polarization and directionality of the Earth's reflectance observations indicate that after normalization with the BRDF archetype LUT, the RMSE decreases from 0.0291 to 0.0249. The BRDF archetype LUT is applicable to the vast majority of solar and viewing geometries, even for the large solar zenith angles. Additionally, the LUT demonstrates good noise resistance and high applicability in observations at different scales, particularly in the NIR band. This study provides a new approach for reflectance angular normalization, offering the potential to provide a more reliable data foundation for future scientific research and practical applications.

In recent years, the UAV has become a convenient platform to obtain multiangle reflectance observations and study bi-directional reflectance distribution function (BRDF) characteristics at a higher spatial resolution than satellite. However, only a few vegetation types were concerned in previous studies, leading to a lack of BRDF knowledge for various objects and preventing further recognition application. In this study, UAV-based multiangle observations were collected for six typical natural and artificial targets including larch forest, grass, artificial turf, asphalt road, cement hut, and model plane. First, fitting accuracy of directional reflectance was calculated, and then we analyzed the variance patterns of spectral and anisotropic indices along with spatial resolutions (i.e., 1-100 m). The results show that: 1) The RTLSR_C kernel-driven model is still applicable for UAV with fitting RMSEs of reflectance less than 0.05, showing multiscale adaptability for both UAV and satellite; 2) for grass and artificial turf, the normalized difference vegetation index (NDVI) decreases as spatial resolution increases, and a significant change with view zenith angle can be observed with the minimum at the hotspot; 3) The anisotropic flat index (AFX) varies with ground types, light, shadows, and sample spatial homogeneity. Notably, there is a sudden change in AFX for larch forest at 5 m near canopy width. Similar NDVI and AFX values are found between grass and artificial turf. This study further reveals BRDF patterns for new target types at varying spatial scales, providing evidence for the applicability of the kernel-driven model at high spatial resolution and target camouflage.

Leaf chlorophyll content (LCC) is a crucial biochemical parameter for monitoring the plant's nutritional status and photosynthetic capacity. However, retrieving LCC from canopy reflectance is challenging due to the coupling influence of LCC and canopy structure, particularly leaf area index (LAI). The isolation of leaf-scale information from canopy signals is therefore essential to improve the LCC estimation. This study proposed an approach for deriving the leaf-scale chlorophyll index (CIleaf) from the canopy bidirectional reflectance factor (BRF) based on the spectral invariant theory (p-theory). Six widely used canopy-scale chlorophyll indices (CIcanopy) were selected to derive the corresponding CIleaf. The CIleaf is expressed as the product of its original CIcanopy and a scale conversion factor (SCF) (CIleaf = CIcanopy x SCF). The SCF is determined by two spectral invariants of p-theory (recollision probability p and directional area scattering factor DASF), as well as canopy BRFs at specific wavelengths, and it corrects for the contribution of canopy multiple scattering to CIcanopy. The analysis through radiative transfer model simulations showed that CIleaf exhibited more unified relationships with LCC across LAI conditions than the original CIcanopy and substantially eliminated the influence of LAI on the CI-based model. Validation results demonstrated that CIleaf improved the accuracy of LCC estimation compared to CIcanopy. The leaf-scale MERIS terrestrial chlorophyll index (MTCIleaf) exhibited the most prominent improvements, reducing the root-mean-square error (RMSE) by 6.68 mu g/cm2 for ground spectra and 2.33-4.21 mu g/cm2 for Sentinel-2 images with multi-ecosystem datasets. Additionally, the influence of vegetation types on the CI-based model was mitigated by CIleaf. MTCIleaf reduced the RMSE values by 3.8 %-34.0 % for different plant functional types, giving more consistent accuracies across species than MTCIcanopy. Our results show that the proposed CIleaf combines the robustness of the physically-based method with the simplicity of the CI-based method, thus providing a practical approach for large-scale high-resolution LCC mapping. Moreover, the method holds promise for designing leaf-scale vegetation indices sensitive to various leaf biochemical parameters beyond LCC, extending its utility to broader leaf-scale remote sensing retrieval (e.g., leaf carotenoid content and leaf dry mass).

Abstract. Leaf chlorophyll content (LCC) is an essential biochemical parameter reflecting vegetation's photosynthetic activity. In the past five years, some global LCC remote sensing products have been generated, and play an important role in vegetation growth monitoring and terrestrial carbon cycle modeling. However, the resolution of current global LCC products ranges from 300 m to 500 m, and the existing 30m-resolution product, Multi-source data Synergized Quantitative remote sensing production system LCC (MuSyQ LCC), is only available in China, resulting in a lack of global high-resolution LCC products. This study used an empirical relationship method based on the chlorophyll sensitive index (CSI) to produce a 10 m resolution global LCC product (MuSyQ Global LCC) with the Google Earth Engine (GEE) platform. A web application was developed, allowing users to independently select regions of interest, time ranges, and spatial-temporal resolutions. The validation results show the MuSyQ Global LCC consists well with the current global MODIS LCC, and MuSyQ Global LCC’s (RMSE = 14.16 μg/cm2, bias = 1.68 μg/cm2) accuracy is slightly higher than that of MODIS LCC (RMSE = 14.74 μg/cm2, bias = -2.65 μg/cm2). The 10m-resolution LCC product has an RMSE of 15.33 μg/cm2, R2 of 0.27, and the accuracy of the vegetation types-specific regression model is stable in different sites across the world. The high-resolution LCC product can show more details of spatial distribution and reasonable temporal profiles than the existing low-resolution product, indicating its ability in precision agriculture, forestry monitoring, and related research.

The clumping index (CI) describes the level of foliage grouping relative to the random distribution within the canopy. It plays a vital role in the derivation of other important parameters (e.g., the leaf area index, (LAI)) that are usually employed in hydrological, ecological and climatological modeling. In recent years, several satellite-based CI products have been developed using multi-angle reflectance data. However, these products have been validated through the use of a “point-to-point” comparison, which rarely involves a quantitative analysis of spatial representativeness for field-measured CIs in most cases. In this study, we developed a methodological framework to validate the MODIS CI at three different data scales on the basis of intense field measurements, high-resolution unmanned aerial vehicle (UAV) observations and Landsat 8 data. This framework was used to understand the impacts of the scale issue and subpixel variance of the CI in the validation of the MODIS CI for a case study of 12 gridded 500 m pixels in Saihanba National Forest Park, Hebei, China. The results revealed that the MODIS CIs in the study area were in good agreement with the upscaled field CIs (R = 0.75, RMSE = 0.05, bias = 0.02) and UAV CIs. Through a comparison of the observed CIs along the 30 m transects with the 500 m MODIS CIs, we gained insight into the uncertainty caused by the direct “point-to-pixel” evaluation method, which ranged from −0.21~+0.27 for the 10th and 90th percentiles of the observed-MODIS CI error distribution for the twelve pixels. Moreover, semivariogram analysis revealed that the representativeness assessments based on high-resolution albedo and CI maps could reflect the spatial heterogeneity within pixels, whereas the CI map provided more information on the variation in vegetation structures. The average observational footprint needed for a spatially representative sample is approximately 209 m according to an analysis of the high-resolution CI map. The uncertainty of mismatched MODIS land cover types can lead to a deviation of 0.33 in CI estimates, and compared with the CLX method, the scaled-up CI method based on simple arithmetic averages tends to overestimate CIs. In summary, various validation efforts in this case study reveal that the accuracy of the MODIS CIs is generally reliable and in good agreement with that of the upscaled field CIs and UAV CIs; however, with the development of surface process modeling and remote sensing technology, substantial measurements of field CIs in conjunction with high-resolution remotely sensed CI maps derived from single-angle advanced methods are urgently needed for further validation and potential applications. Certainly, such a validation effort will help to improve the understanding of MODIS CI products, which, in turn, will further support the methods and applications of global geospatial information.

Leaf chlorophyll content (LCC) serves as a critical indicator for quantifying photosynthetic carbon assimilation, providing fundamental data for terrestrial carbon cycle estimation. In the past five years, some global LCC remote sensing products have been generated, but their resolution ranges from 300 m to 500 m. This study employed an empirical relationship method based on the Chlorophyll Sensitive Index (CSI) to produce the Multi-source data Synergized Quantitative Global LCC product (MuSyQ Global LCC) with a resolution from 100 m to 10 m using the Google Earth Engine (GEE) platform. Validation results demonstrate that the 10m-resolution MuSyQ Global LCC product has an RMSE of 13.69 μg/cm2, R2 of 0.37, and the RMSE is between 11.28 μg/cm2 and 15.22 μg/cm2 for different vegetation types. Its finer resolution reveals more spatial details compared with the existing global products. When upscaled to 500 m, it demonstrates high consistency with the MODIS LCC product, and MuSyQ Global LCC (RMSE = 14.16 μg/cm2) exhibits higher accuracy than MODIS LCC (RMSE = 14.74 μg/cm2).

Surface albedo measures the proportion of incoming solar radiation reflected by the Earth’s surface. Accurate albedo retrieval from remote sensing data usually requires sufficient multi-angular observations to account for the surface reflectance anisotropy. However, most middle and high-resolution remote sensing satellites lack the capability to acquire sufficient multi-angular observations. Existing algorithms for retrieving surface albedo from single-direction reflectance typically rely on land cover types and vegetation indices to extract the corresponding prior knowledge of surface anisotropic reflectance from coarse-resolution Bidirectional Reflectance Distribution Function (BRDF) products. This study introduces an algorithm for retrieving albedo from directional reflectance based on a 3 × 3 BRDF archetype database established using the 2015 global time-series Moderate Resolution Imaging Spectro-radiometer (MODIS) BRDF product. For different directions, BRDF archetypes are applied to the simulated MODIS directional reflectance to retrieve albedo. By comparing the retrieved albedos with the MODIS albedo, the BRDF archetype that yields the smallest Root Mean Squared Error (RMSE) is selected as the prior BRDF for the direction. A lookup table (LUT) that contains the optimal BRDF archetypes for albedo retrieval under various observational geometries is established. The impact of the number of BRDF archetypes on the accuracy of albedo is analyzed according to the 2020 MODIS BRDF. The LUT is applied to the MODIS BRDF within specific BRDF archetype classes to validate its applicability under different anisotropic reflectance characteristics. The applicability of the LUT across different data types is further evaluated using simulated reflectance or real multi-angular measurements. The results indicate that (1) for any direction, a specific BRDF archetype can retrieve a high-accuracy albedo from directional reflectance. The optimal BRDF archetype varies with the observation direction. (2) Compared to the prior BRDF knowledge obtained through averaging method, the BRDF archetype LUT based on the 3 × 3 BRDF archetype database can more accurately retrieve the surface albedo. (3) The BRDF archetype LUT effectively eliminates the influence of surface anisotropic reflectance characteristics in albedo retrieval across different scales and types of data.

Multi-angular remote sensing observation contains crucial information on forest structure parameters. Here, our goal is to examine the ability of multi-angular indices, which are constructed by the typical-angular reflectances in red and NIR bands from MODIS observations, for the retrieval of forest biomass based on the field-measured above-ground biomass (AGB) data. Specifically, we employed the updated version of the MCD43A1 BRDF parameter product as an input for BRDF models to reconstruct the MODIS typical-angular reflectances. Furthermore, we evaluated the effects of different configurations of BRDF models and solar zenith angles (SZA) on forest AGB estimation using our developed multi-angular indices. The semivariogram analysis strategy combined with Landsat ground-surface reflectance data was employed to determine the MODIS pixel heterogeneity; the survey data from field sites of homogeneous pixels was used in our analysis and validation. The results show that our developed multi-angular indices based on a hot-revised BRDF model, under a SZA of 45°, when combined with forest cover information, can account for up to 72% of the variation forest AGB, with an RMSE = 45 Mg/ha. We also found that different kernels for the BRDF models influenced the weight parameters of the biomass inversion equation but did not significantly affect the estimated AGB. In conclusion, our method can enable the better usage of MODIS multi-angular observations for forest AGB estimation.

Leaf chlorophyll content (LCC) is crucial in plant physiology and ecological research. Although several LCC products have recently been generated at a regional or global scale, understanding their accuracy is still a concern in the scientific community. We intercompared and analyzed five existing LCC products (MuSyQ LCC, MODIS LCC, MERIS LCC, GLCC, and GLOBMAP MERIS LCC) over China in terms of spatial continuity and spatiotemporal consistency over seven plant functional types. The products of 2011 and 2019 over China were used in this study. Research findings indicate (1) the 30 m-resolution MuSyQ LCC has the highest accuracy compared to field LCC of cropland and grassland types, with an RMSE of 19.5 μg/cm2, while MODIS LCC product demonstrates a more robust fit to the measured LCC, with an R2 of 0.341. (2) Interpolation of products with lower spatial resolution e.g. MODIS LCC, MERIS LCC, and GLOBMAP MERIS LCC, generally improves spatial continuity. The non-interpolated 30 m MuSyQ LCC exhibits good regional continuity due to its high spatial resolution. The lowest spatial continuity is found over shrubs for all products. (3) MODIS LCC and MuSyQ LCC of 2019 demonstrate high overall spatial consistency and exhibit the highest correlation over cropland sites. MODIS LCC and GLOBMAP MERIS LCC of 2011 demonstrate high temporal consistency over deciduous forests, evergreen forests, grasslands, and shrubs sites. The most robust overall temporal consistency is exhibited among all products in the deciduous needleleaved forest, followed by evergreen needleleaved forest and grassland. The findings of this research are essential for improving leaf chlorophyll content inversion algorithms and for understanding and better use of LCC products in land surface models.

Chlorophyll is a vital indicator of vegetation growth; exploring its relationship with external influencing factors is essential for studies such as chlorophyll remote sensing retrieval and vegetation growth monitoring. However, there has been limited in-depth exploration of the spatial distribution of leaf chlorophyll content (LCC) and its influencing factors across large-scale areas with varying climates and terrains. To investigate the primary influencing factors and degrees of various environmental factors on LCC, this study employed the Geodetector Model (GDM) and the LCC satellite products in Sichuan Province in 2020 to investigate the impact of relationships between nine environmental factors (meteorology, topography, and vegetation types) and the ecosystem LCC at a regional scale. The results indicated the following: (1) Elevation (q-value = 49.31%) is the primary factor determining photosynthesis in Sichuan Province, followed by temperature (46.10%) and vegetation types (40.73%). The impact of topographical factors on LCC distribution is higher than that of meteorological factors and vegetation types in terrain with complex topography. The elevation effectively distinguishes the variations in climate factors and vegetation types. (2) Combining the influencing factors pairwise increased the combined q-values. The combination of elevation with other factors yielded the highest combined q-value. (3) The q-values for all influencing factors are higher in winter and spring and lowest in summer. Different influencing factors exhibited more substantial constraints on vegetation photosynthesis during winter and spring, significantly reducing influence during summer. (4) The different primary factors drive or constrain vegetation photosynthesis in different climate zones due to their distinct temperature and humidity characteristics. The findings of this study provide a basis for future research on vegetation change analysis and dynamic monitoring of vegetation LCC in different terrains.

The foliage Clumping Index(CI)is an important structural parameter of vegetation canopies.The CI influences radiation interception within canopies and plays an important role in the study of global carbon and water cycles.Currently,the widely used method for deriving satellite-borne CI products is based on a linear model constructed on the basis of the CI and the Normalized Difference between the Hotspot and Dark spot(NDHD)angular indices.As coniferous and broadleaf forests exhibit aggregate differences at the leaf scale,the CI inversion model can be applied to a variety of coefficients to generate different CI-NDHD models.Modelers typically use CI inversion coefficients of broadleaf forests to estimate the CI of coniferous-broadleaf mixed forests for medium-coarse resolution pixels,but this approach can theoretically cause a CI overestimation for this landcover type.Thus,in this study,we propose a novel coniferous-broadleaf Mixed Forest CI(MFCI)estimation method to dynamically select the endmember CIs of mixed forests pixel by pixel.The proposed method was successfully applied to satellite-borne MODIS data.The MFCI of the tree-farm study area on Saihanba was estimated,and the accuracy of the results was validated using ground-measured CIs. The MFCI was estimated by considering land cover classes and the Anisotropy Flatness Index(AFX),which describes the basic Bidirectional Reflectance Distribution Function(BRDF)variation.First,the prior values of the endmember NDHD were extracted pixel by pixel by imposing double constraints on the landcover type of the International Geosphere-Biosphere Program and the surface AFX,which characterize the shape of the BRDF.Then,the high-resolution land cover classification data were used to obtain the proportions of the endmembers in the coniferous-broadleaf mixed forest pixels.An optimization factor f was introduced to eliminate the differences between the NDHD of mixed forest pixels and the NDHD prior values of different vegetation cover types based on the NDHD linear mixing assumption.Then,the endmember CIs were calculated.Finally,the endmember CIs,combined with endmember abundance,were used to estimate the MFCIs based on Beer's law. First,the existing MODIS CI product algorithm does not consider coniferous-broadleaf mixed forest pixels within mixed forest pixels,which leads to overestimation of coniferous-broadleaf mixed forest CIs.When the proportion of coniferous species reaches 60％in a mixed forest pixel,the overestimation of the CI can exceed 35％.Second,the proposed MFCI estimation method based on the CI-NDHD algorithm can significantly improve the CI estimation accuracy of coniferous-broadleaf mixed forest pixels.When the proportion of coniferous forest in the mixed forest pixels reached 60％,the accuracy improved by 28.03％.The root mean-square error and bias for the enhanced results were reduced by approximately 84％and 175％,respectively.Third,the MFCI method is more sensitive than the current MODIS CI products to changes in coniferous and broadleaf forest structures in mixed forest pixels. The current satellite CI products for mixed forest pixels typically use the modeled coefficients of broadleaf forests in the CI-NDHD model,which theoretically implies increased uncertainty in CI products.In this study,the proposed MFCI estimation method was used for coniferous-broadleaf forest mixed pixels.The CI endmembers were dynamically adjusted.The validation based on ground-measured CIs showed that the proposed method was significantly more accurate than the current MODIS CI products in terms of estimating the CI of mixed coniferous and broadleaved forests.In summary,the MFCI estimation method improved the estimation accuracy of mixed forest CI products in the selected study area.The proposed method is a promising technique for further improving the accuracy of global CI products.