The accurate simulation of Bidirectional Reflectance Distribution Function (BRDF) across varied Crop Residue Cover (CRC) scenarios is pivotal for crop residue monitoring and management. Addressing the limitations of prior research in simulating BRDF for cropland with CRC, we have developed the novel Crop Residue-covered Bidirectional Reflectance (CRBR) model. This model couples Geometric Optical (GO) and Radiative Transfer (RT) model, which involves adding a clumping index and Crop Residue Tilt Angle (CRTA) distribution function through terrestrial laser scanning to parameterize the spatial distribution of covered crop residue. Validation of the CRBR model was conducted using corn residue cover data from Lishu County, Jilin Province, China, collected in April 2023. The results demonstrated strong alignment between the simulated and measured multi-angle bands reflectance (R² = 0.90, RMSE = 0.03, MAPE = 8.91%). Under various CRC scenarios, the CRBR model consistently outperformed linear mixed models (R² ≥ 0.99, RMSE ≤ 0.02, MAPE ≤ 4.14% vs R² ≥ 0.97, RMSE ≤ 0.05, MAPE ≤ 23.69%). Sensitivity analysis revealed the impact of key model parameters on reflectance simulation. Furthermore, we also examined the adaptability of our model under different moisture conditions and CRC scenarios, confirming its robustness and flexibility. The CRBR model not only helps our understanding of radiative transfer in crop residue-soil scenarios but also offers a promising approach for efficient and precise CRC estimation on a regional scale. Such advancements in the CRBR model hold significant implications for conservation tillage monitoring, biomass energy reserve estimation, and cropland carbon storage capacity assessment.

Leaf area index (LAI) is a key parameter for characterizing the dynamics of terrestrial ecosystems and is also one of the important structural parameters that can be retrieved from remote sensing (RS) data. LAI over mountainous areas, however, is still difficult to retrieve reliably due to the topographical variation that introduces significant uncertainties into observed reflectance. In this article, we proposed a new scheme to estimate topographic influence on ratio-based vegetation indices (VIs) from diffuse radiation, which is not yet adequately considered in existing topographic correction schemes. In our scheme, unmanned aerial vehicle (UAV) light detecting and ranging (LiDAR) data were first used to model the sky view factor (SVF) and terrain view factor of target pixels in a slope coordinate system. Based on these view factors, the total incident solar radiation on slope (ISRS) was corrected, specifically for the diffuse sky irradiance and adjacent terrain-reflected irradiance over complex terrains. we then recalculated the multispectral reflectance of UAV images and evaluated the topographic effects on the normalized difference vegetation index (NDVI) because the magnitudes of correction on red and near-infrared (NIR) reflectances are quite different. Finally, large-scale LAI distribution was retrieved by empirical models developed from the relationships between terrain-corrected NDVI and field-measured LAI. Our results show that the proposed topographic correction scheme can significantly improve the LAI retrievals over a growing season. Given that forests are widely distributed in complex terrains around the globe, this study would have significance in improving the mapping of global LAI that is essential for terrestrial carbon cycle studies.

The probability of viewing sunlit leaves (PT) is a crucial variable influencing observed canopy spectra. Proper determination of PT is necessary for the quantitative retrieval of vegetation parameters using remote sensing. This article describes a spectral index for estimating PT from satellite-observed canopy spectra. For this purpose, we propose a normalized spectral angle index (NSAI) at near-infrared (NIR) wavelengths, based on the spectral shapes of leaf and soil background. The performance of NSAI in estimating PT was evaluated using one ground-based high-resolution imaging dataset, one synthetic satellite dataset, and one satellite-ground synchronous observation dataset. The results demonstrate that NSAI is more suitable for estimating PT from satellite data than five commonly used spectral indices, including enhanced vegetation index (EVI), normalized difference spectral index (NDSI), normalized difference vegetation index (NDVI), simple ratio (SR) index, and photochemical reflectance index (PRI). NSAI exhibits a significant linear correlation with PT. The empirical model for estimating PT based on NSAI has the best transferability from simulated to in situ satellite data. For the fine spectral–spatial resolution (Hyperion) data, the normalized root-mean-square error (nRMSE) and adjusted$R^{2}$of estimated PT were 14.9% and 0.744, respectively. For MODIS images, PT was estimated with satisfactory accuracy, with an nRMSE of 18.71% and an adjusted$R^{2}$of 0.670. NSAI is potentially applicable to satellite images for direct estimation of PT to improve the inversion accuracy of vegetation parameters.

Topography distorts the angular distribution of the canopy gap fraction (GF). Path length (PL) correction is a simple and effective method to harmonize this distortion and improve canopy reflectance modelling andin situleaf area index measurements for vegetation, including both continuous (e.g., grass and crop) and discrete (e.g., forests) canopies, over sloping terrains. The rigorously theoretical derivation of PL correction for continuous canopies has been implemented. However, for discrete canopies, the PL show a serious heterogeneity, making it nearly impossible to be calculated. In this regard, there is still a need to develop theoretical derivation to evaluate and improve PL correction for forests over sloping terrains. In this study, (1) PL correction is proven to be equivalent to the correction of the canopy GF over sloping terrains, and our strategy concerns the canopy GF as a proxy of PL. (2) PL correction is first proven to be completely valid for forests with the Poisson trees distribution; yet it may produce uncertainty in certain directions for forests with tree distribution deviating from the Poisson model, especially for forests with regular tree distribution. (3) An improved model based on a Nilson and Peterson’s GF model for correcting PL for forests is given in this study. The results show that error produced by the PL correction for some forests can be effectively decreased by the improved model. The variation of directional tree distribution parameter cB(θ) with slope is the main cause of error produced by PL correction for forests. The study is of importance for better understanding and more accurate application of PL theory in topographic corrections andin situleaf area index measurements for forest canopies over sloping terrains.

Canopy clumping index (CI) characterizes the extent of the nonrandom spatial distribution of foliage elements within a canopy and is critical for determining the radiative transfer, photosynthesis, and transpiration processes in the canopy. It is widely perceived that CI increases with zenith angle (θ), because between-crown gaps decrease in size and number with increasing θ. In this study, we demonstrate that this is not always true. Analytical equations between CI and θ are first developed based on widely-used forest canopy gap fraction theories. The results show that the zenith angular variation of CI is closely related to crown projected area or crown shapes (i.e., the ratio of the crown height to its diameter, RHD): CI increases with θ for canopies with “tower” crowns (RHD > 1), but decreases with θ for “umbrella” crowns (RHDin-situmeasurements and multi-angular remote sensing.

Leaf chlorophyll content (LCC) is an important plant physiological trait and is critical for accurate modeling of vegetation photosynthesis over time and space. To date, there is still a lack of a global long time-series dataset of LCC. In this study, we developed an algorithm to retrieve global LCC from MODIS surface reflectance data from 2000–2020. An essential requirement for generating LCC time series is to capture its seasonal dynamics. This issue was addressed by using a matrix system with two pairs of vegetation indices to minimize the impacts of leaf area index and canopy non-photosynthetic material on LCC estimation in different seasons. The matrix system algorithm was applied to Landsat data and MODIS data, respectively. The validation based on Landsat data and ground measurements reveals the algorithm has the ability to catch the seasonal variations of LCC in different plant functional types, and the MODIS-derived LCC shows good agreement with Landsat-upscaled LCC (R2=0.77, RMSE=6.9 μg/cm2). The global 8-day LCC data at 500-m resolution in 2000–2020 was generated using the matrix system from MODIS and presented distinct temporal and spatial variations, which provides a new opportunity for analyzing vegetation physiological dynamics in climate change studies.

The PROSPECT model has been widely used to estimate leaf biochemical constituents, but retrieval of leaf mass per area (LMA) in fresh leaves has proved to be difficult due to the predominant water absorption in the infrared spectral region. At wavelengths where water absorption is low, both LMA absorption and light scattering are relatively high. Therefore, the uncertainty in scattering simulation at these wavelengths will lead to a relatively large error in LMA estimation. In this paper, we introduce a wavelength-independent factor to represent the first-order effect of anisotropic scattering in the elementary layer in the modified model PROSPECT-g, aiming at appropriately simulating leaf optical properties in spectral regions with high scattering and thus reducing the uncertainty in LMA estimation. In order to avoid introducing a new variable to be retrieved in model inversion, this factor is an intermediate variable derived from measured near infrared region spectral data and other existing model parameters. Results show that about 30%-40% of the tested samples are well simulated using PROSPECT-5, while for the rest of the samples simulation is greatly improved with PROSPECT-g. Leaf reflectance and transmittance reconstructions using PROSPECT-g are improved, especially at wavelengths with high scattering such as 750-1400 and 1500-1850 nm. LMA retrieval is significantly improved, with the average root-mean-square error decreasing from 38.7 (PROSPECT-5) to 16.6 g/m2(PROSPECT-g) for 628 leaves after considering anisotropic scattering in the elementary layer. Improvements are particularly noticeable for leaves with extremely high LMA contents.

The Sentinel-2A (S2A) Multi-Spectral Imager (MSI) is a new remote sensor launched on 23 June 2015 that provides unprecedented Earth observation with high spatial, spectral and temporal resolutions. It has high potential for chlorophyll content estimation. Chlorophyll content plays a crucial role in plant photosynthesis affecting the terrestrial carbon cycle. In this research, a physical retrieval algorithm is proposed for leaf chlorophyll content from the S2A MSI data based on 4-Scale and PROSPECT models. Satellite and ground data were collected and processed in a mixed temperate forest near Borden, Ontario, Canada from May to October 2016. Preliminary validation shows an agreement between the inverted and ground measured leaf chlorophyll contents, with r = 0.77 and RMSE = 8.82 μg/cm2, which is an improvement over those generated by the Sentinel Application Platform (SNAP). Further research is ongoing, and the algorithm will be improved in the future.

Geometric-optical (GO) model suitable for forest plantation (GOFP) is a GO model for forest plantations at the stand level developed in this study based on a four-scale GO model a Geometric-Optical Model for Sloping Terrains-II (GOST2), which simulates the bidirectional reflectance distribution function (BRDF) for natural forest canopies. In most previous GO models, tree distributions are often assumed to meet the Poisson or Neyman model in a forest; therefore, these models are suitable for simulating BRDF for natural forest canopies. However, in forest plantations, tree distributions are proven to meet the hypergeometric model rather than the Poisson or Neyman model at the stand level. GOFP, in which the tree distributions are described using the hypergeometric model, is proposed to simulate the bidirectional reflectance factor (BRF) of forest plantations at the stand level. The area ratios of the four scene components (sunlit foliage, sunlit ground, shaded foliage, and shaded ground) of GOFP compare well with those simulated by a 3-D canopy visualization technique. A comparison is also made against discrete anisotropic radiative transfer, showing that GOFP has the ability to simulate BRF of forest plantations. Another comparison is made against operational land imager and Moderate Resolution Imaging Spectroradiometer surface.

We investigate the magnitude of Sun-glint through modeling the Soil Moisture Active Passive (SMAP) brightness temperature (BT). Model results show that the specular reflection of Sun-glint in the L-band can spread over a wide range of view angles due to the roughness and undulation of the land surface and therefore affect SMAP radiometer observations. Due to SMAP's low incidence angle (40°), Sun-glint in the specular direction is never observed, and only the noncoherent component of Sun-glint has influence on SMAP observations. Sun-glint is particularly an issue over wet soil surfaces at low solar zenith angles (SZAs), and caution has to be taken for the terrain effect even for high SZAs, because the local solar incident angle can be significantly changed by the terrain slope and then the specular reflection of Sun-glint can be viewed by SMAP. Model results also show that BT in V-pol is less contaminated by Sun-glint than that in H-pol. During an intense solar radio burst, it was found that the land surface BT in H-pol increased by 50 K in the forward scattering direction from the SMAP observation. This is roughly equivalent to 1 K increment by every 100 solar flux units on a dry soil and/or dense vegetation. When the solar activity is quiet in 2015, the Sun-glint from both wet land and ocean surfaces can reach up to 10 K in the SMAP L1B BT product. This paper suggests that BT observations around the solar specular direction should be masked for soil moisture retrieval at the L-band.

Light use efficiency (LUE) models offer an effective way for regional gross primary productivity (GPP) estimation. However, LUE is not easily determined at the landscape level due to its complexity and dependence on various environmental factors. One possible strategy to avoid the requirement for assessing environmental stressors is using the photochemical reflectance index (PRI) to determine LUE via the epoxidation state of the xanthophyll cycle. Integration of such measurements into GPP models could lead to more realistic GPP estimates of landscape level. Conventional, “one-leaf” LUE models, however, seem less suitable for integration of such remote sensing observations, as optically derived estimates are dependent on the shadow fraction viewed at a given time. Here, we utilize the two-leaf LUE (TL-LUE) model to parameterize LUE from multiangle PRI observations and compare it with MOD17 approach. Significant relationships were found between LUE (LUE, LUEsun, and LUEshaded) and PRI (PRI, PRIsιn, and PRIshaded) over 8and 16-day time steps. Similarly, R values for the relationships between modeled GPP and observed GPP (EC derived measurements of GPP) were 0.87 (TL-LUE) and 0.81 (MOD17) at deciduous forest and 0.54 (TL-LUE) and 0.46 (MOD17) at evergreen forest for eight-day periods, as well as 0.84 (TL-LUE) and 0.74 (MOD17) at deciduous forest and 0.49 (TL-LUE) and 0.46 (MOD17) at evergreen forest for 16-day periods. Our results are relevant when planning potential future satellite missions to help constrain existing GPP models using remotely sensed data, as such observations will likely be affected by canopy shading effects at the time of observation.

Photosynthesis in vegetation is arguably the most important and variable part of the terrestrial carbon cycle. In terrestrial biospheric models, the photosynthesis rate of plant leaves is generally simulated based on the maximum carboxylation rate at an optimum temperature (often 25°C), which is often denoted as Vcmax25. In regional and global photosynthesis modeling, Vcmax25 is usually prescribed as constants for different plant functional types (PDF) based on ground measurements in order to capture the first order spatial variability associated with PTF distribution. However, experimental data show that Vcmax25 can vary by 2-3 factors for the same PFT, and it is also not a constant in different growing seasons because of leaf physiological change with season. It is therefore highly desirable to be able to map this critical parameter to address the issues of its spatial and temporal variabilities. Both terrestrial carbon and water cycle simulations can be greatly improved if we can achieve the global mapping of this parameter.

Proper determination of light use efficiency (LUE) is a prerequisite for LUE models to simulate gross primary productivity (GPP). This study was devoted to apply the photochemical reflectance index (PRI) to accurately track LUE variations for a sub-tropical coniferous forest using tower-based PRI and GPP measurements. To improve the ability of PRI to track LUE, a simple two-leaf approach is used to process the remote sensing and flux data. The results showed: both PRI and LUE decreased with increases of bioclimatic factors. PRI is able to capture diurnal and seasonal changes in LUE. And the two-leaf approach significantly enhanced the correlation between PRI and LUE at either half-hourly or daily time steps.

A new geometric optical (GO)-radiative transfer (RT) model with a multiple scattering scheme suitable for sloping forest canopies is developed in this study. It is based on a Geometrical-Optical model for Sloping Terrains and an RT method. This new model overcomes the difficulty to prescribe bidirectional reflectance factors (BRFs) of shaded components (shaded foliage and background) in GO modeling through simulating radiation multiple scattering within a sloping forest. A case study shows that multiply scattered radiation depends on topographic factors and leaf area index. The contributions of the shaded components to stand-level BRF are less than 3% in the red band and can reach up to 40% in the near-infrared (NIR) band. The “multiangle” Moderate Resolution Imaging Spectroradiometer (MODIS) data over sloping pixels are selected to validate the modeled forest BRF. Considering the multiple scattering schemes and topographic factors, the modeled BRF is closer to the MODIS surface reflectance (BRF product) (red band: R2= 0.8614, rRMSE = 0.1339; NIR band: R2= 0.7573, rRMSE = 0.0850) than the modeled BRF (red band: R2= 0.7771, rRMSE=0.1839; NIR band: R2=0.5176, rRMSE = 0.1155) without topographic consideration. It is also shown that the MODIS surface reflectance of sloping forests at multiple angles can be simulated well using the newly developed model.

GOST is a geometric-optical (GO) model for sloping terrains developed in this study based on the four-scale GO model, which simulates the bidirectional reflectance distribution function (BRDF) of forest canopies on flat surfaces. The four-scale GO model considers four scales of canopy architecture: tree groups, tree crowns, branches, and shoots. In order to make this model suitable for sloping terrains, the mathematical description for the projection of tree crowns on the ground has been modified to consider the fact that trees grow vertically rather than perpendicularly to sloping grounds. The simulated canopy gap fraction and the area ratios of the four scene components (sunlit foliage, sunlit background, shaded foliage, and shaded background) by GOST compare well with those simulated by 3-D virtual canopy computer modeling techniques for a hypothetical forest. GOST simulations show that the differences in area ratios of the four scene components between flat and sloping terrains can reach up to 50%-60% in the principal plane and about 30% in the perpendicular plane. Two case studies are conducted to compare modeled canopy reflectance with observations. One comparison is made against Landsat-5 Thematic Mapper (TM) reflectance, demonstrating the ability of GOST to model canopy reflectance variations with slope and aspect of the terrain. Another comparison is made against MODIS surface reflectance, showing that GOST with topographic consideration outperforms that without topographic consideration. These comparisons confirm the ability of GOST to model canopy reflectance on sloping terrains over a large range of view angles.

Leaf area index (LAI) is one of the essential biogeophysical variables related to terrestrial carbon and biogeochemical cycles. The University of Toronto (UofT) LAI product is developed in order to support the European Space Agency GLOBCARBON project for global and climate change assessments. The climate and global change communities have recently requested for a daily 250-m LAI product in order to improve the spatial and temporal patterns of carbon pools and fluxes knowledge. In light of these considerations, we carry out further improvements on the UofT LAI algorithm, including enhanced spatial resolution (250 m) by considering an improved land cover map, local topography, clumping index, and background reflectance variations in order to produce canopy LAI time series. Here, we present the methodological framework and an evaluation of 250-m UofTv2 LAI estimates in forest stands of the Canadian Carbon Program fluxnet sites. The LAI distributions over Canada and the comparison with ground measurements show an improved LAI estimates from the UofT v2 LAI algorithm as compared with the UofT v1 LAI algorithm. One of the key differences between v1 and v2 UofT LAI product is that the former produces total LAI whereas the latter produces overstorey LAI in forest and total LAI in other vegetated land cover types. A daily LAI product can further be extracted from the 10-day UofT v2 LAI time series by fitting various curve fitting algorithms. Although, we have shown the LAI product only over Canada, the algorithm can also be extended for a global 250-m LAI product.

Global and regional vegetation assessment strategies often rely on the combined use of multisensor satellite data. Variations in spectral response function (SRF) which characterizes the sensitivity of each spectral band have been recognized as one of the most important sources of uncertainty for the use of multisensor data. This paper presents the SRF differences among 21 Earth observation satellite sensors and their cross-sensor corrections for red, near infrared (NIR), and shortwave infrared (SWIR) reflectances, and normalized difference vegetation index (NDVI) aimed at global vegetation monitoring. The training data set to derive the SRF cross-sensor correction coefficients were generated from the state-of-the-art radiative transfer models. The results indicate that reflectances and NDVI from different satellite sensors cannot be regarded as directly equivalent. Our approach includes a polynomial regression and spectral curve information generated from a training data set representing a wide dynamics of vegetation distributions to minimize land cover specific SRF cross-sensor correction coefficient variations. The absolute mean SRF caused differences were reduced from 33.9% (20.1%) to 9.4 % (6%) for red, from 3.2 % (8.9%) to 1% (1.1% ) for NIR, from 2.9% (3.6 %) to 1.9% (1.6%) for SWIR, and from 7.1 % (9%) to 1.8% (1.7% ) for NDVI, after applying the SRF cross-sensor correction coefficients on independent top of canopy (top of atmosphere) data for all-embraced-sensor comparisons. Variations in processing strategies, non spectral differences, and algorithm preferences among sensor systems and data streams hinder cross-sensor spectra and NDVI comparability and continuity. The SRF cross-sensor correction approach provided here, however, can be used for studies aiming at large-scale vegetation monitoring with acceptable accuracy.

The Multi-Angle Imaging Spectroradiometer (MISR) is a powerful sensor for leaf area index (LAI) mapping with its simultaneous multi-angle observations. However, the LAI product derived from MISR observations has low temporal resolution, which is unsatisfactory for many applications. This paper presents an algorithm that expands the MISR LAI product to high temporal resolution with the aid of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The algorithm establishes relationships between the MISR LAI and the MODIS red/near-infrared band ratio (simple ratio (SR)) pixel by pixel using coincident data of these two sensors for the past nine years. Using these pixel-based SR-LAI relationships, a new LAI product with the merits of the original MISR product and high temporal resolution is obtained from MODIS surface reflectance. The expanded LAI series was compared with the original MISR and MODIS LAI products, as well as field LAI measurements made at the Baohe and Maoershan forest sites and the Hulunbeier grassland site, to assess the algorithm's performance. The results show that the temporal coverage of the MISR LAI improved from 15.5% to 65.2% in an 8-day composite, and the mean root-mean-square error is 0.74 for the vegetated pixels. This LAI product has similar temporal consistency and seasonal dynamics to the existing MODIS LAI product generated from the main algorithm, but is more robust against the low quality of reflectance inputs. The expanded LAI product differs with field measurements by about 11.5%, with agreement to field observations at all three sites within an accuracy of 0.8 LAI.

The precise estimation of the baseline is a crucial procedure in repeat-pass interferometric synthetic aperture radar (InSAR) applications. Using the ephemeris of the satellite, a polynomial regression algorithm can fit the satellite orbit at the third or higher order with a main shortcoming that the mutual constraints among the three dimensions defining the orbit are missed. In this paper, a new approach is presented to fit the satellite orbit based on the assumption that the satellite orbit is a 3-D ellipse, which retains the relations among the three dimensions. Considering the complexity of 3-D ellipse parameters estimation, the 3-D orbit is first transformed into three 2-D ellipses. Then, the parameters of these 2-D ellipses are estimated with a direct least-square ellipse fitting method (DLS-EFM). These two orbit fitting algorithms are tested with ten sets of advanced land observation satellite phased array L-band SAR data, which were acquired in north Toronto, Ontario, Canada, from September, 2008 to January, 2009. Moreover, two of them acquired with an adjacent period were chosen to form a repeat-pass InSAR, and the corresponding baseline is calculated with the proposed method as an example. The experimental results show that the error of the satellite position using DLS-EFM is at a submetric level, which is less than one-tenth of that of the polynomial regression algorithm. Consequently, the proposed method is appropriate for the baseline estimation in spaceborne InSAR applications.

The three-dimentional plant canopy architecture is often characterized using the foliage clumping index useful for ecological and land surface modeling. In this paper, an algorithm is developed to retrieve the foliage clumping index with the Moderate Resolution Imaging Spectroradiometer bidirectional reflectance distribution function (BRDF) parameter product (MCD43A1), which is generated using the RossThick-LiSparse Reciprocal (Ross-Li) model. First, the Ross-Li model is modified to improve the simulation of the reflectance at hotspot using the Polarization and Directionality of Earth Reflectance measurements as benchmarks to determine BRDF parameters. Then, the modified model (Ross-Li-H) is used to simulate the reflectance at hotspot and darkspot, which is used to calculate the normalized difference between hotspot and darkspot (NDHD). With the relationship between clumping index and NDHD simulated by the 4-Scale geometrical model, the clumping index over China's landmass at 500-m resolution is retrieved every 8 days during the period from 2003 to 2008. Finally, The effect of topography on the retrieved clumping index is corrected using a topographic compensation function calculated from the digital elevation model at 90-m resolution. The topographically corrected clumping index values correlate well with field measurements at five sites over China, indicating the feasibility of the algorithm for retrieving the clumping index from the MCD43A1 product.

In this study, we show for the first time a global Clumping Index (CI) map at 500 m resolution derived using the Bidirectional Reflectance Distribution Function (BRDF) product from Moderate Resolution Imaging Spectroradiometer (MODIS). We found that the hotspot calculated from the MODIS BRDF product is underestimated in comparison with POLarization and Directionality of the Earth's Reflectances (POLDER) measurements very near the hotspot, so the MODIS-derived CI could be overestimated without correcting the bias. We developed an approach to correct the MODIS hotspot magnitude against synchronous co-registered POLDER-3 data. Field measured CIs by the Tracing Radiation and Architecture of Canopies instrument from 38 sites were used to evaluate the global CI map. Validation indicates that a strong correlation between MODIS-derived NDHD and CI indeed exists, and red band is better than near infrared band in effectively capturing the structural differences among cover types.

Forest background, consisting of understory, moss, litter, and soil, contributes significantly to optical remote sensing signals from forests in the boreal region. In this paper, we present results of background reflectance retrieval from multiangle high-resolution Compact Airborne Spectrographic Imager sensor data over a boreal forest area near Sudbury, ON, Canada. Modifications of the background by white and black plastic sheets at two sites provide two extreme limits for the development and testing of an algorithm for retrieving the background information from multiangle data. Measured background reflectances in red and near-infrared bands at six sites in the vicinity of these modified sites are used to validate the algorithm. We also explore the effect of uncertainties in the input forest structural parameters on this retrieval. The results document: 1) capability of the algorithm to retrieve meaningful background reflectance values for various forest stand conditions, particularly in the low to intermediate canopy density range; 2) the effect of background bidirectional reflectance distribution function on retrieved values; 3) performance of the algorithm using data with different cross angle values; and 4) verification of the internal consistency of the geometric-optical 4-Scale model used. The results provide an important platform for the operational estimation of the vegetation background reflectance from the bidirectional reflections observed by the Multiangle Imaging Spectroradiometer instrument.

Measurements at more than one angle capture the directional anisotropy of solar radiance reflected from vegetated surfaces. According to our recent research, we propose that the best two view angles for vegetation structural mapping are the following: 1) the hotspot, where the Sun and view directions coincide, and 2) the darkspot, where the sensor sees the maximum amount of vegetation structural shadows. The Normalized Difference between Hotspot and Darkspot (NDHD), an angular index generated from Compact Airborne Spectrographic Imager (CASI) data, is found to be highly correlated with the field-measured foliage clumping index. The foliage clumping index characterizes the nonrandomness in the spatial distribution pattern of leaves. It is of comparable importance as the leaf area index (LAI) for quantifying radiation interception and distribution in plant canopies, and it also affects estimated LAI mapping using remote sensing data. As the clumping index can vary considerably within a cover type, it is highly desirable to map its spatial distribution for various ecological applications. We have generated clumping index maps based on the previous algorithms and empirical relationships between field-measured ¿ and CASI-derived NDHD. Through intensive validation using field data, we demonstrate that the combination of the hotspot and darkspot reflectances has the strongest response to changes in vegetation structure. Two crown structural characteristics, namely, crown height and within-crown density, are major factors that impact the NDHD and clumping index difference between the mature and young (regrowth) coniferous forests. The study area is located near Sudbury in the northern Ontario, Canada.

The concept of combining multi-angle and hyperspectral remote sensing has been developed and utilized in an existing satellite observation system CHRIS (Compact High-Resolution Imaging Spectrometer) onboard the Project for On-Board Autonomy (PROBA) platform developed by the European Space Agency (ESA). We attempt to refine this measurement concept by testing a new one: a system that acquires hyperspectral signals only in the nadir direction but measures in two additional directions in two spectral bands, red and NIR. Through model experiments, we demonstrate that the combination of the hotspot and darkspot reflectances has the strongest signals about the vegetation structure quantified using the foliage clumping index. The results suggest that the multi-angle hyperspectral data exhibit much redundancy, and that the multi-spectral measurements at off-nadir angles in addition to the nadir hyperspectral data would be sufficient to reconstruct hyperspectral signatures at off-nadir angles.

Process-based terrestrial ecosystem models inevitably need model initialization and parameters specification. In this study, remotely sensed surface soil moisture derived from near infrared and shortwave infrared bands was assimilated in BEPS (Boreal Ecosystem Production Simulator) to initialize soil moisture in BEPS and fine-tune BEPS key parameters which are closely related to soil moisture estimation including maximum stomotal conductance, leaf area index (LAI) and root density. An Ensemble Kalman Filter is used to perform data assimilation and parameter adjustment. The result shows that using the optimized parameters, the performance of model predictions of 0-10 cm soil moisture was greatly improved compared with the surface soil moisture fields derived from remote sensing data. It is demonstrated that the method of assimilating remotely sensed soil moisture in the BEPS model can help improve the soil moisture results of the BEPS model in the arid and semiarid area and provide a feasible way to monitor drought and to assess its influence on agriculture.

Both surface topography and vegetation heterogeneity are important factors introducing biases in regional ecological modeling, especially when the modeling is made at large grids. Several studies have demonstrated that gridding the land surface into coarse homogeneous pixels may cause important biases on ecosystem model estimations of carbon budget components at local, regional and global scales. These biases result from overlooking sub-pixel variability of land surface characteristics. This study suggests a simple algorithm that uses sub-pixel information on the spatial variability of vegetation and surface topography to correct net primary productivity (NPP) estimates, made at coarse spatial resolutions where the land surface is considered as homogeneous within each pixel. A spatial scaling algorithm is developed to correct biases in coarse-resolution NPP estimation. This algorithm considers the effect of sub-pixel heterogeneities of land cover, leaf area index (LAI), slope and elevation. Its application to a carbon-hydrology coupled model estimates made at a 1-km resolution over a watershed (named Baohe River Basin) located in the southwestern part of Qinling Mountains, in China, Shaanxi Province, China, improved estimates of average NPP as well as its temporal and spatial variability.

In this paper, we report on a process-based approach to estimate leaf chlorophyll content from hyperspectral remote sensing imagery. Extensive field and laboratory measurements were conducted for ten sites in black spruce (Picea mariana) forests near Sudbury, Ontario, Canada in 2003 and 2004. Leaf optical spectra and chlorophyll content, leaf and canopy biophysical parameters, and forest background optical properties were collected. Hyperspectral remote sensing images were acquired by the Compact Airborne Spectrographic Imager (CASI) over the study sites within one week of ground measurements. Using measured data as inputs, a geometricaloptical model 4-Scale was investigated to estimate forest canopy reflectance. The simulated canopy reflectance agrees well with the CASI measured reflectance. A look-up-table approach was developed to provide the probabilities of viewing sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up-tables, leaf reflectance spectra were inverted from hyperspectral remote sensing imagery. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified leaf-level PROSPECT inversion model.

Satellite-derived vegetation indices and their resulting surface parameters, such as the leaf area index (LAI), are inevitably affected by the atmosphere. Errors in the atmospheric corrections can often be easily identified in a seasonal trajectory of a surface parameter because the atmospheric effect generally causes erratic reductions in vegetation indices. A locally adjusted cubic-spline capping (LACC) method is developed here to screen affected data points in a pixel and to replace them through temporal interpolation. In LACC, a variable local smoothing parameter, which controls the local smoothness of the fitted curve, is automatically determined according to the local curvature of the original seasonal variation pattern. An iteration procedure is designed to produce a seasonal capping curve by progressively replacing abnormally low values with fitted values. This method has two advantages over existing methods based on harmonics, namely: 1) cubic splines are flexible for simulating a wide range of seasonal variation patterns and 2) a variable local smoothing parameter allows the fitted capping curve to mimic either rapid or slow variation patterns in various seasons. The capping curve is also mathematically differentiable for further applications. The effectiveness of this method is demonstrated through case studies for several cover types in China and processing a series of Moderate Resolution Imaging Spectroradiometer LAI images of China in 2001

Leaf area index (LAI) is one of the most important Earth surface parameters in modeling ecosystems and their interaction with climate. Based on a geometrical optical model (Four-Scale) and LAI algorithms previously derived for Canada-wide applications, this paper presents a new algorithm for the global retrieval of LAI where the bidirectional reflectance distribution function (BRDF) is considered explicitly in the algorithm and hence removing the need of doing BRDF corrections and normalizations to the input images. The core problem of integrating BRDF into the LAI algorithm is that nonlinear BRDF kernels that are used to relate spectral reflectances to LAI are also LAI dependent, and no analytical solution is found to derive directly LAI from reflectance data. This problem is solved through developing a simple iteration procedure. The relationships between LAI and reflectances of various spectral bands (red, near infrared, and shortwave infrared) are simulated with Four-Scale with a multiple scattering scheme. Based on the model simulations, the key coefficients in the BRDF kernels are fitted with Chebyshev polynomials of the second kind. Spectral indices - the simple ratio and the reduced simple ratio - are used to effectively combine the spectral bands for LAI retrieval. Example regional and global LAI maps are produced. Accuracy assessment on a Canada-wide LAI map is made in comparison with a previously validated 1998 LAI map and ground measurements made in seven Landsat scenes

Leaf area index (LAI) retrieval from remote sensing is a very active research field. In heterogeneous canopies, simulations with the canopy reflectance model five-scale show that nadir view vegetation indices, such as NDVI and simple ratio (SR), based on near infrared and red reflectance are more closely related to the gap fraction at the solar zenith angle than the LAI. The gap fraction is important in canopy light interception. At the solar zenith angle, it can be used to estimate the amount of sunlit LAI. But the knowledge or LAI and foliage heterogeneity are both needed to estimate shaded leaves that are also important in the carbon cycle. In our previous studies, we developed a methodology to retrieve the foliage heterogeneity, represented by a clumping index, from remote sensing. The retrieval is accomplished with an anisotropy index using broadband directional reflectance at the hotspot and at the darkspot. The combination of nadir, hotspot, and darkspot views allows the LAI retrieval for a given cover type. However, directional measurements are not usually acquired with the same sun-target-sensor geometry, so the anisotropy kernel-based four-scale linear model for anisotropy reflectance (FLAIR) is used to interpolate the directional reflectance from ADEOS-POLDER data to acquire hotspot and darkspot reflectance at a common geometry in the near infrared band. A landcover map at 1-km based on SPOT-VGT data acquired in 1998 and directional POLDER data acquired over Canada in June 1997, are used to map the clumping index. The results show consistent clumping index values compared to in-situ values, and that SR and the anisotropy index are not correlated to each other, indicating that both indices are related to different canopy properties.

In-situ estimations of leaf area index (LAI), leaf clumping, and leaf angular distribution are often performed from canopy gap fraction measurements with optical sensors. Two new procedures are used in this study to improve the estimation of gap fraction from digital camera photographs,: 1) the digital number (DN) of mixed sky-canopy pixels is used to estimate the within pixel gap fraction instead of the usual threshold used to separate a pixel in gap or a foliage pixel, and 2), the within pixel gap fraction is calculated at different view zenith and azimuth angles to take into account multiple scattering effects. To estimate foliage clumping, a gap size distribution is calculated from a narrow view zenith angle range (less than 1/spl deg/). The clumping index is then extracted using 3 methods: 1) a refined gap size distribution theory developed for the TRAC instruments; 2) the Lang and Xiang (1986) logarithm gap fraction averaging and 3) a combination of 1) and 2). Clumping index variations with view zenith angle in the range from 15/spl deg/ to 70/spl deg/ are derived using the individual and combined methods. Analysis of the digital hemispherical photographs shows that 1) the three methods give different clumping estimates, but the angular variation patterns are similar, and 2) canopies with significant angular variation in clumping can induce large errors in the inverted leaf angle distribution when the clumping angular variation is not included in the retrieval. The practical implication of these findings is that LAI, clumping index, and foliage orientation can all be reliably retrieved using digital hemispherical photographs, considerably reducing the number and cost of instruments needed in fieldwork.

Spatial scaling is of particular importance in remote sensing applications to terrestrial ecosystems where spatial heterogeneity is the norm. Surface parameters derived at different resolutions can be considerably different even though they are derived using the same algorithms or models. This article addresses issues related to spatial scaling of net primary productivity (NPP). The main objective is to develop algorithms for spatial scaling of NPP using subpixel information. NPP calculations at 30 m and 1km resolutions were performed using the Boreal Ecosystem Productivity Simulator (BEPS). The area of interest is near Fraserdale, Ontario. It is found from this investigation that lumped (coarse resolution) calculations can be considerably biased (up to 64 %) from distributed (fine resolution) case, suggesting that global and regional NPP maps can be biased by the same amount if surface heterogeneity within the mapping resolution is ignored. The bias is negative when conifer-labeled pixels contain considerable deciduous forests. Due to relatively high and variable NPP values of open land areas with growing grasses, the bias is negative when deciduous-labeled pixels are mixed with open land. There is no trend between the biasness and open land fractions within conifer-labeled pixels. Based on these results, algorithms for removing these biases in lumped NPP are developed using subpixel land cover information.

For pt.I see ibid., vol.39, no.5, p.1072-83 (2001). To address the need for a flexible model of the bidirectional reflectance distribution function (BRDF) that is also suitable for inversion, the FLAIR Model (Four-Scale Linear Model for AnIsotropic Reflectance) has been developed H. P. White et al. (2001). Based on the more detailed Four-Scale Model J. M. Chen et al. (1997), FLAIR is a linear kernel-like model, developed with the aim of not being limited to specific canopy characteristics or view/illumination geometry, while maintaining a direct relationship between canopy architectural properties and model coefficients. Having been previously demonstrated to have the ability to capture the bi-directional patterns in both forward and inverse modes of calculation, this paper examines the FLAIR model in describing the boreal canopy by applying FLAIR to multiangular data sets obtained by various sensors during BOREAS 1994. Effects of sensor field of view, ranges of view/solar illumination geometry, and multiple sensor use on BRDF derivation and inversion for canopy parameter retrieval are considered.

Geometrical optical (GO) models have been widely used in remote sensing applications because of their simplicity and ability to simulate angular variation of remote sensing signals from the Earth's surface. GO models are generally accurate in the visible part of the solar spectrum, but less accurate in near-infrared (NIR) part in which multiple scattering in plant canopies is the strongest. Although turbid-media radiative transfer (RT) methods have been introduced to GO models to cope with the second-order and higher order scattering, the problem of canopy geometrical effects on multiple scattering still remains and becomes the main obstacle in GO model applications. In this paper, we propose and test a multiple scattering scheme to simulate angular variation in multiply scattered radiation in plant canopies. This scheme is based on various view factors between sunlit and shaded components (both foliage and background) in the canopy and allows the geometrical effects to propagate to the second-order and higher order scattering simulations. As the view factors depend on the canopy geometry, the scheme is particularly useful in GO models. This new scheme is implemented in the 4-Scale Model, which previously used band-specific multiple scattering factors. After the use of the scheme, these factors are removed and the multiple scattering at a given wavelength and angle of observation can be automatically computed. Improvements made with this scheme are shown in comparison with the top-of-canopy (i.e., PARABOLA) and airborne (i.e., POLDER) measurements with modeled results with and without the scheme. Examples of canopy-level hyperspectral signatures simulated using the scheme are also shown.

As optical remote sensing techniques provide increasingly detailed canopy reflectance data at a variety of illumination/view geometries, direct quantitative comparisons between data sets require a flexible model of the bidirectional reflectance distribution function (BRDF) suitable for inversion. Typically, such derivations rely on: 1) complex and computationally expensive empirical canopy descriptions, or 2) simplifications for specific canopy types, conditions, or view geometry. More practical would be one general model not requiring significant computing resources, but that provides information on canopy architecture when utilized as an inverse model. The Four-Scale Model, developed by Chen and Leblanc (1997), describes canopy reflectance considering four levels of architecture, distributions of tree crowns, branches, shoots, and leaves. A linear kernel-like model has been developed from this Four-Scale Linear Model for Anisotropic Reflectance (FLAIR). While simplifications are performed, an effort has been made not to limit FLAIR to specific canopy characteristics, while maintaining relationships between modeled coefficients and canopy architecture. Comparisons between Four-Scale and FLAIR, and use of FLAIR in the forward mode on multi-angular data sets obtained during BOREAS 1994, allow examination of the suitability, capabilities, and limitations of this model in describing canopy reflectance. As partial validation, this paper compares FLAIR functions to aspects of the Four-Scale Model from which they are developed. Examination of how this model reacts to inversion of simulated reflectance data sets demonstrates its ability to simulate and reproduce canopy reflectance. leading toward the retrieval of reasonable LAI.

Airborne Polarization and Directional Earth Radiation (POLDER) data acquired during the boreal ecosystem-atmosphere study (BOREAS) and the four-scale model of Chen and Leblanc (1997) are used to investigate radiative transfer in boreal forest. The four-scale model is based on forest canopy architecture at different scales. New aspects are incorporated into the model to improve the physical representation of each canopy, as follows: 1) Elaborate branch architecture is added. 2) Different crown shapes are used for conifer and deciduous forests. 3) Bilayer version of the model is introduced for forest canopies with an important understory. 4) Natural repulsion effect is considered in the tree distribution statistics. Ground measurements from BOREAS sites are used as input parameters by the model to simulate measurements of bidirectional reflectance distribution function (BRDF) from four forest canopies (old black spruce, old aspen, and old and young jack pine) acquired by the POLDER instrument from May-July 1994. The model is able to reproduce with great accuracy the BRDF of the four forests. The importance of the branch architecture and the self-shadowing of the foliage is emphasized.

Open boreal forests present a challenge in understanding remote sensing signals acquired with various solar and view geometries. Much research is needed to improve our ability to model the bidirectional reflectance distribution (BRD) for retrieving the surface information using measurements at a few angles. The geometric-optical bidirectional reflectance model presented in this paper considers four scales of canopy architecture: tree groups, tree crowns, branches and shoots. It differs from the Li-Strahler's model in the following respects: 1) the assumption of random spatial distribution of trees is replated by the Neyman distribution which is able to model the patchiness or clumpiness of a forest stand; 2) the multiple mutual shadowing effect between tree crowns is considered using a negative binomial and the Neyman distribution theory; 3) the effect of the sunlit background is modeled using a canopy gap size distribution function that affects the magnitude and width of the hotspot; 4) the branch architecture affecting the directional reflectance is simulated using a simple angular radiation penetration function; and 5) the tree crown surface is treated as a complex surface with microscale structures which themselves generate mutual shadows and a hotspot. All these scales of canopy architecture are shown to have effects on the directional distribution of the reflected radiance from conifer forests. The model results compare well with a data set from a boreal spruce forest.

Measurements of the fraction of photosynthetically active radiation (FPAR) absorbed by the forest overstory were made at 20 sites in black spruce (Picea mariana) and jack pine (Pinus banksiana) boreal forests located in Saskatchewan and Manitoba, Canada. Canopies of both species have similar vertical tree crown structure but different branch and shoot architecture. Intensive investigation was made on the effect of these canopy architecture on the penetration of total visible radiation into the canopy at various solar zenith angles /spl theta/, quantified using the projection coefficient G/sub t/(/spl theta/). Based on experimental evidence, constant values of G/sub t/(/spl theta/) and the above- and below-canopy PAR reflectivities are suggested for these two species for the calculation of daily green FPAR. The calculation then requires only a single stand parameter: the effective green leaf area index (LAI) L/sub eg/, which is similar to the effective LAI L/sub e/ measured using optical instruments but reduced by a small fraction to remove the contribution of woody material to the total above-ground plant area. Daily green FPAR of the sites was correlated with the Simple Ratio (SR) and the Normalized Difference Vegetation Index (NDVI) obtained from Landsat 5 TM images. The correlation was better in late-spring than in mid-summer, suggesting spring images are more useful for obtaining FPAR of the overstory. Comparisons of the present with previous results suggest that the background (understory and ground cover) signal and the tree crown shadows are important in satellite measurements of FPAR.

In recent years, the methodology in ground-based optical measurements of leaf area index (LAI) of plant canopies has been substantially improved after the introduction of canopy gap size analysis methods. In this paper, the two methods by Chen and Black (1992) and Chen and Cihlar (in press) are compared for four boreal conifer stands located near Prince Albert, Saskachewan, and Thompson, Manitoba, Canada. The data used in the analysis were obtained from a new sunfleck-LAI instrument, the TRAC (Tracing Radiation and Architecture of Canopies), which measures the photosynthetic photon flux density along transects beneath the overstory at a rate of 100 samples per meter. It is confirmed in this study that the needle shoots of conifer trees can be treated as the basic foliage units (elements) for radiation interception considerations. The effect of foliage clumping at scales larger than the shoots is quantified using an element clumping index. This is necessary for indirect measurements of LAI based on the gap fraction principle using optical instruments such as the LI-COR LAI-2000. The values of element clumping index derived from these two methods agree within 17% for all stands investigated. However, the values obtained using Chen and Black's method are consistently smaller than those calculated using Chen and Cihlar's method. The difference results from a negative bias introduced in the method of Chen and Black which requires the assumption for a random spatial distribution of foliage clumps (tree crowns). The method of Chen and Cihlar makes no assumption of foliage distribution patterns and is therefore more reliable. Yet, Chen and Black's method allows the derivation of several canopy architectural parameters which are useful for modeling radiative regimes in forest canopies. It is concluded that for remote sensing and other studies, a large quantity of ground truth LAI data can be acquired quickly and accurately using a combination of indirect optical measurements by the LAI-2000 for the foliage angular distribution and the TRAC for the foliage spatial distribution.>