Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates

Forest leaf area index (LAI), is an important variable in carbon balance models. However, understory vegetation is a recognized problem that limits the accuracy of satellite-estimated forest LAI. A canopy reflectance model was used to investigate the impact of the understory vegetation on LAI estimated from reflectance values estimated from satellite sensor data. Reflectance spectra were produced by the model using detailed field data as input, i.e. forest LAI, tree structural parameters, and the composition, distribution and reflectance of the forest floor. Common deciduous and coniferous forest types in southern Sweden were investigated. A negative linear relationship (r² = 0.6) was observed between field estimated LAI and the degree of understory vegetation, and the results indicated better agreement when coniferous and deciduous stands were analysed separately. The simulated spectra verified that the impact of the understory on the reflected signal from the top of the canopy is important; the reflectance values varying by up to ± 18% in the red and up to ± 10% in the near infra-red region of the spectra due to the understory. In order to predict the variation in LAI due to the understory vegetation, model inversions were performed where the input spectra were changed between the minimum, average and maximum reflectance values obtained from the forward runs. The resulting variation in LAI was found to be 1.6 units on average. The LAI of the understory could be predicted indirectly from simple stand data on forest characteristics, i.e. from allometric estimates, as an initial step in the process of estimating LAI. It is suggested here that compensation for the effect of the understory would improve the accuracy in the estimates of canopy LAI considerably.     

Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics

Leaf area index (LAI) is a key variable for the understanding of several eco-physiological processes within a vegetation canopy. The LAI could thus provide vital information for the management of the environment and agricultural practices when estimated continuously over time and space thanks to remote sensing sensors.This study proposed a method to estimate LAI spatial and temporal variation based on multi-temporal remote sensing observations processed using a simple semi-mechanistic canopy structure dynamic model (CSDM) coupled with a radiative transfer model (RTM). The CSDM described the temporal evolution of the LAI as function of the accumulated daily air temperature as measured from classical ground meteorological stations.The retrieval performances were evaluated for two different data sets: first, a data set simulated by the RTM but taking into account realistic measurement conditions and uncertainties resulting from different error sources; second, an experimental data set acquired over maize crops the Blue Earth City area (USA) in 1998. Results showed that the proposed approach improved significantly the retrieval performances for LAI mainly by smoothing the residual errors associated to each individual observation. In addition it provides a way to describe in a continuous manner the LAI time course from a limited number of observations during the growth cycle.     

The RAMI On-line Model Checker (ROMC): A web-based benchmarking facility for canopy reflectance models

The exploitation of global Earth Observation data hinges increasingly on physically-based radiative transfer (RT) models. These models simulate the interactions of solar radiation within a given medium (e.g., clouds, plant canopies) and are used to generate look-up-tables that are embedded into quantitative retrieval algorithms, such as those delivering the operational surface products for MODIS, MISR and MERIS. An assessment of the quality of canopy RT models thus appears essential if accurate and reliable information is to be derived from them. Until recently such an undertaking was a time consuming and labour intensive process that was made even more challenging by the general lack of absolute reference standards. Several years of benchmarking activities in the frame of the RAdiation transfer Model Intercomparison (RAMI) exercise have now led to the development of the RAMI On-line Model Checker (ROMC). The ROMC is a web-based tool allowing model developers and users to autonomously assess the performance of canopy RT models (http://romc.jrc.ec.europa.eu/). Access to the ROMC is free and enables users to obtain both statistical and graphical indications as to the performance of their canopy RT model. In addition to providing an overall indication of the skill of a given model to correctly match the reference data, the ROMC allows also for interactive comparison/evaluations of different model versions/submissions of a given user. All ROMC graphs can be downloaded in PostScript format and come with a reference number for easy usage in presentations and publications. It is hoped that the ROMC will prove useful for the RT modeling community as a whole, not only by providing a convenient means to evaluate models outside the triennial phases of RAMI but also to attract participation in future RAMI activities.     

Application of photon recollision probability in coniferous canopy reflectance simulations

A new semi-physical forest reflectance model, PARAS, is presented in the paper. PARAS is a simple parameterization model for taking into account the effect of within-shoot scattering on coniferous canopy reflectance. Multiple scattering at the small scale represented by a shoot is a conifer-specific characteristic which causes the spectral signature of coniferous forests to differ from that of broadleaved forests. This has for long led to problems in remote sensing of canopy structural variables in coniferous dominated regions. The PARAS model uses a relationship between photon recollision probability and leaf area index (LAI) for simulating forest reflectance. The recollision probability is a measurable, wavelength independent variable which is defined as the probability with which a photon scattered in the canopy interacts with a phytoelement again. In this study, we present application results using PARAS in simulating reflectance of coniferous forests for approximately 800 Scots pine and Norway spruce dominated stands. The results of this study clearly indicate that a major improvement in simulating canopy reflectance in near-infrared (NIR) is achieved by simply accounting for the within-shoot scattering. In other words, the low NIR reflectance observed in coniferous areas is mainly due to within-shoot scattering. In the red wavelength the effect of within-shoot scattering was not pronounced due to the high level of needle absorption in the red range. To conclude the paper, further application possibilities of the presented parameterization model are discussed.     

Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations

The leaf area index (LAI) of fast-growing Eucalyptus plantations is highly dynamic both seasonally and inter-annually, and is spatially variable depending on pedo-climatic conditions. LAI is very important in determining the carbon and water balance of a stand, but is difficult to measure during a complete stand rotation and at large scales. Remote-sensing methods allowing the retrieval of LAI time series with accuracy and precision are therefore necessary. Here, we tested two methods for LAI estimation from MODIS 250m resolution red and near-infrared (NIR) reflectance time series. The first method involved the inversion of a coupled model of leaf reflectance and transmittance (PROSPECT4), soil reflectance (SOILSPECT) and canopy radiative transfer (4SAIL2). Model parameters other than the LAI were either fixed to measured constant values, or allowed to vary seasonally and/or with stand age according to trends observed in field measurements. The LAI was assumed to vary throughout the rotation following a series of alternately increasing and decreasing sigmoid curves. The parameters of each sigmoid curve that allowed the best fit of simulated canopy reflectance to MODIS red and NIR reflectance data were obtained by minimization techniques. The second method was based on a linear relationship between the LAI and values of the GEneralized Soil Adjusted Vegetation Index (GESAVI), which was calibrated using destructive LAI measurements made at two seasons, on Eucalyptus stands of different ages and productivity levels. The ability of each approach to reproduce field-measured LAI values was assessed, and uncertainty on results and parameter sensitivities were examined. Both methods offered a good fit between measured and estimated LAI (R² = 0.80 and R² = 0.62 for model inversion and GESAVI-based methods, respectively), but the GESAVI-based method overestimated the LAI at young ages.     

A coupled 1-D atmosphere and 3-D canopy radiative transfer model for canopy reflectance, light environment, and photosynthesis simulation in a heterogeneous landscape

Detailed knowledge of light interactions between the atmosphere and vegetation, and within vegetation are of particular interest for terrestrial carbon cycle studies and optical remote sensing. This study describes a model for 3-D canopy radiative transfer that is directly coupled with an atmospheric radiative transfer model (Forest Light Environmental Simulator, FLiES). The model was developed based on the Monte Carlo ray-tracing method using some existing modeling frameworks. To integrate the canopy radiative transfer model with atmosphere, the same numerical method, sampling technique, and variance reduction technique were employed in both the atmospheric and the canopy modules. Farquhar's leaf photosynthesis model was combined to calculate the canopy level photosynthesis from the light environmental parameters obtained by the radiative transfer calculation. In order to document the quality of the coupled model, we first compared the atmospheric radiative transfer module to well known 1-D atmospheric radiative transfer models, and then evaluated the 3-D canopy radiative transfer module against a series of test cases provided by the RAMI On-line Model Checker (ROMC). We used the model to show the impact of atmospheric properties and 3-D canopy structure on the directionality of downward photosynthetically active radiation (PAR) at the top of canopy, the 3-D distribution of absorbed PAR (APAR), and overall canopy photosynthesis. The results indicate the importance to consider angular geometry of incident light at TOC and 3-D canopy structure.     

Neural network method to solve inverse problems for canopy radiative transfer models

Vegetation parameter retrieval is considered as the inverse of modeling canopy radiative transfer. To solve this problem, a new computationally efficient method based on mixture density networks (MDNs) is proposed to estimate the errors of retrieved parameters for each given set of reflectances. The properties of neural networks of traditional architecture and MDNs are considered. The method is tested using a simple model and the PROSPECT leaf radiative transfer model and is validated against real data. The paper is supported by the joint project of the Science and Technology Center in Ukraine (STCU) and National Academy of Sciences of Ukraine (NASU), “Grid Technologies for Multi-Source Data Integration” (No. 4928). Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 159–172, May–June 2009. Original article submitted January 29, 2009.     

Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale

This paper presents a physically-based approach for estimating critical variables describing land surface vegetation canopies, relying on remotely sensed data that can be acquired from operational satellite sensors. The REGularized canopy reFLECtance (REGFLEC) modeling tool couples leaf optics (PROSPECT), canopy reflectance (ACRM), and atmospheric radiative transfer (6SV1) model components, facilitating the direct use of at-sensor radiances in green, red and near-infrared wavelengths for the inverse retrieval of leaf chlorophyll content (C_ab) and total one-sided leaf area per unit ground area (LAI). The inversion of the canopy reflectance model is constrained by assuming limited variability of leaf structure, vegetation clumping, and leaf inclination angle within a given crop field and by exploiting the added radiometric information content of pixels belonging to the same field. A look-up-table with a suite of pre-computed spectral reflectance relationships, each a function of canopy characteristics, soil background effects and external conditions, is accessed for fast pixel-wise biophysical parameter retrievals. Using 1 m resolution aircraft and 10 m resolution SPOT-5 imagery, REGFLEC effectuated robust biophysical parameter retrievals for a corn field characterized by a wide range in leaf chlorophyll levels and intermixed green and senescent leaf material. Validation against in-situ observations yielded relative root-mean-square deviations (RMSD) on the order of 10% for the 1 m resolution LAI (RMSD = 0.25) and C_ab (RMSD = 4.4 μg cm^− 2) estimates, due in part to an efficient correction for background influences. LAI and C_ab retrieval accuracies at the SPOT 10 m resolution were characterized by relative RMSDs of 13% (0.3) and 17% (7.1 μg cm^− 2), respectively, and the overall intra-field pattern in LAI and C_ab was well established at this resolution. The developed method has utility in agricultural fields characterized by widely varying distributions of model variables and holds promise as a valuable operational tool for precision crop management. Work is currently in progress to extend REGFLEC to regional scales.     

Optimal interpolation analysis of leaf area index using MODIS data

A simple data analysis technique for vegetation leaf area index (LAI) using Moderate Resolution Imaging Spectroradiometer (MODIS) data is presented. The objective is to generate LAI data that is appropriate for numerical weather prediction. A series of techniques and procedures which includes data quality control, time-series data smoothing, and simple data analysis is applied. The LAI analysis is an optimal combination of the MODIS observations and derived climatology, depending on their associated errors σ_o and σ_c. The “best estimate” LAI is derived from a simple three-point smoothing technique combined with a selection of maximum LAI (after data quality control) values to ensure a higher quality. The LAI climatology is a time smoothed mean value of the “best estimate” LAI during the years of 2002-2004. The observation error is obtained by comparing the MODIS observed LAI with the “best estimate” of the LAI, and the climatological error is obtained by comparing the “best estimate” of LAI with the climatological LAI value. The LAI analysis is the result of a weighting between these two errors. Demonstration of the method described in this paper is presented for the 15-km grid of Meteorological Service of Canada (MSC)'s regional version of the numerical weather prediction model. The final LAI analyses have a relatively smooth temporal evolution, which makes them more appropriate for environmental prediction than the original MODIS LAI observation data. They are also more realistic than the LAI data currently used operationally at the MSC which is based on land-cover databases.     

地表通量对模型参数的不确定性和敏感性分析

基于2007年12月22日~2009年12月31日黑河流域阿柔冻融观测站的气象驱动数据,利用通用陆面模型(Common Land Model,CoLM)模拟的地表通量结果,研究地表通量对模型参数(叶面积指数、地表反照率和植被覆盖度)的不确定性与敏感性。结果表明,叶面积指数、地表反照率和植被覆盖度对地表感热和潜热通量不同组分的影响存在较大的差异。其中,植被层的感热和潜热通量对叶面积指数的敏感性程度较高,敏感系数均达到0.7以上;与潜热通量相比,感热通量对反照率更加敏感,土壤感热、植被感热和总感热通量对反照率的敏感系数分别达到-0.96、-0.97和-0.66,而土壤潜热和总潜热通量对地表反照率的敏感系数仅为0.1左右;植被潜热通量对植被覆盖度的敏感性程度很高,敏感系数范围为0.92~0.96,而土壤感热通量对植被覆盖度最不敏感,敏感系数只有0.18左右。     

FluorMODleaf: A new leaf fluorescence emission model based on the PROSPECT model

A new model of chlorophyll a fluorescence emission by plant leaves, FluorMODleaf, is presented. It is an extension of PROSPECT, a widely used leaf optical properties model that regards the leaf as a pile of N absorbing and diffusing elementary plates. In FluorMODleaf, fluorescence emission of an infinitesimal layer of thickness dx is integrated over the entire elementary plate. The fluorescence source function is based on the excitation spectrum of diluted isolated thylakoids and on the emission spectra of isolated photosystems, PSI and PSII, which are the main pigment-protein complexes involved in the initial stages of photosynthesis. Scattering within the leaf is produced by multiple reflections within and between elementary plates. The input variables of FluorMODleaf are: the number of elementary plates N, also called leaf structure parameter, the total chlorophyll content C_ab, the total carotenoid content C_cx, the equivalent water thickness C_w, and the dry matter content C_m (or leaf mass per area), as in the new PROSPECT-5, plus the σ_II/σ_I ratio referring to the relative absorption cross section of PSI and PSII, and the fluorescence quantum efficiency of PSI and PSII, τ_I and τ_II, that are introduced here as mean fluorescence lifetimes. The model, which considers the reabsorption of emitted light within the leaf, allows good quantitative estimation of both upward and downward apparent spectral fluorescence yield (ASFY) at different excitation wavelengths from 400 nm to 700 nm. It also emphasizes the role of scattering in fluorescence emission by leaves having high chlorophyll content.     

Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops

An investigation of the estimation of leaf biochemistry in open tree crop canopies using high-spatial hyperspectral remote sensing imagery is presented. Hyperspectral optical indices related to leaf chlorophyll content were used to test different radiative transfer modelling assumptions in open canopies where crown, soil and shadow components were separately targeted using 1 m spatial resolution ROSIS hyperspectral imagery. Methods for scaling-up of hyperspectral single-ratio indices such as R₇₅₀/R₇₁₀ and combined indices such as MCARI, TCARI and OSAVI were studied to investigate the effects of scene components on indices calculated from pure crown pixels and from aggregated soil, shadow and crown reflectance. Methods were tested on 1-m resolution hyperspectral ROSIS datasets acquired over two olive groves in southern Spain during the HySens 2002 campaign conducted by the German Aerospace Center (DLR). Leaf-level biochemical estimation using 1-m ROSIS data when targeting pure olive tree crowns employed PROSPECT-SAILH radiative transfer simulation. At lower spatial resolution, therefore with significant effects of soil and shadow scene components on the aggregated pixels, a canopy model to account for such scene components had to be used for a more appropriate estimation approach for leaf biochemical concentration. The linked models PROSPECT-SAILH-FLIM improved the estimates of chlorophyll concentration from these open tree canopies, demonstrating that crown-derived relationships between hyperspectral indices and biochemical constituents cannot be readily applied to hyperspectral imagery of lower spatial resolutions due to large soil and shadow effects. Predictive equations built on a MCARI/OSAVI scaled-up index through radiative transfer simulation minimized soil background variations in these open canopies, demonstrating superior performance compared to other single-ratio indices previously shown as good indicators of chlorophyll concentration in closed canopies. The MCARI/OSAVI index was demonstrated to be less affected than TCARI/OSAVI by soil background variations when calculated from the pure crown component even at the typically low LAI orchard and grove canopies.     

Simple analytical formula for calculating average photon recollision probability in vegetation canopies

The concept of recollision probability originates from the theory of canopy spectral invariants (‘p-theory’) but is a simplification that involves several heuristic assumptions. Nonetheless, the concept has been shown to be a useful tool for incorporating the effects of 3D structure on canopy absorptive and reflective properties in forest reflectance models. A method is presented by which an average value of the canopy recollision probability (pˆ) can be calculated from canopy gap fraction data, which are provided for example by the LAI-2000 plant canopy analyzer or can be extracted from fisheye photographs. The new method was used to calculate the average recollision probabilities (pˆ values) of uniform leaf and shoot canopies, showing good agreement with results from Monte Carlo simulations. Strengths of the method presented here are the explicitly formulated relationship between recollision probability and canopy structure, and its direct applicability in canopy RT studies.     

Enhancing a leaf radiative transfer model to estimate concentrations and in vivo specific absorption coefficients of total carotenoids and chlorophylls a and b from single-needle reflectance and transmittance

The relative concentrations of different pigments within a leaf have significant physiological and spectral consequences. Photosynthesis, light use efficiency, mass and energy exchange, and stress response are dependent on relationships among an ensemble of pigments. This ensemble also determines the visible characteristics of a leaf, which can be measured remotely and used to quantify leaf biochemistry and structure. But current remote sensing approaches are limited in their ability to resolve individual pigments. This paper focuses on the incorporation of three pigments—chlorophyll a, chlorophyll b, and total carotenoids—into the LIBERTY leaf radiative transfer model to better understand relationships between leaf biochemical, biophysical, and spectral properties.Pinus ponderosa and Pinus jeffreyi needles were collected from three sites in the California Sierra Nevada. Hemispheric single-leaf visible reflectance and transmittance and concentrations of chlorophylls a and b and total carotenoids of fresh needles were measured. These data were input to the enhanced LIBERTY model to estimate optical and biochemical properties of pine needles. The enhanced model successfully estimated reflectance (RMSE = 0.0255, BIAS = 0.00477, RMS%E = 16.7%), had variable success estimating transmittance (RMSE = 0.0442, BIAS = 0.0294, RMS%E = 181%), and generated very good estimates of carotenoid concentrations (RMSE = 2.48 µg/cm², BIAS = 0.143 µg/cm², RMS%E = 20.4%), good estimates of chlorophyll a concentrations (RMSE = 10.7 µg/cm², BIAS = − 0.992 µg/cm², RMS%E = 21.1%), and fair estimates of chlorophyll b concentrations (RMSE = 7.49 µg/cm², BIAS = − 2.12 µg/cm², RMS%E = 43.7%). Overall root mean squared errors of reflectance, transmittance, and pigment concentration estimates were lower for the three-pigment model than for the single-pigment model. The algorithm to estimate three in vivo specific absorption coefficients is robust, although estimated values are distorted by inconsistencies in model biophysics. The capacity to invert the model from single-leaf reflectance and transmittance was added to the model so it could be coupled with vegetation canopy models to estimate canopy biochemistry from remotely sensed data.     

Testing for nonlinearity in mean and volatility for heteroskedastic models

A simple test for threshold nonlinearity in either the mean or volatility equation, or both, of a heteroskedastic time series model is proposed. The procedure extends current Bayesian Markov chain Monte Carlo methods and threshold modelling by employing a general double threshold GARCH model that allows for an explosive, non-stationary regime. Posterior credible intervals on model parameters are used to detect and specify threshold nonlinearity in the mean and/or volatility equations. Simulation experiments demonstrate that the method works favorably in identifying model specifications varying in complexity from the conventional GARCH up to the full double-threshold nonlinear GARCH model with an explosive regime, and is robust to over-specification in model orders.     

Reflectance seasonality and its relation to the canopy leaf area index in an eastern Siberian larch forest: Multi-satellite data and radiative transfer analyses

Reliable monitoring of seasonality in the forest canopy leaf area index (LAI) in Siberian forests is required to advance the understanding of climate-forest interactions under global environmental change and to develop a forest phenology model within ecosystem modeling. Here, we compare multi-satellite (AVHRR, MODIS, and SPOT/VEGETATION) reflectance, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and LAI with aircraft-based spectral reflectance data and field-measured forest data acquired from April to June in 2000 in a larch forest near Yakutsk, Russia. Field data in a 30 × 30-m study site and aircraft data observed around the field site were used. Larch is a dominant forest type in eastern Siberia, but comparison studies that consider multi-satellite data, aircraft-based reflectance, and field-based measurement data are rarely conducted. Three-dimensional canopy radiative transfer calculations, which are based on Antyufeev and Marshak's [Antyufeev, V.S., & Marshak, A.L. (1990). Monte Carlo method and transport equation in plant canopies, Remote Sensing of Environment, 31, 183-191] Monte Carlo photon transport method combined with North's [North, P.R. (1996). Three-dimensional forest light interaction model using a Monte Carlo method, IEEE Transactions on Geoscience and Remote Sensing, 34(4), 946-956] geometric-optical hybrid forest canopy scene, helped elucidate the relationship between canopy reflectance and forest structural parameters, including several forest floor conditions. Aircraft-based spectral measurements and the spectral response functions of all satellite sensors confirmed that biases in reflectance seasonality caused by differences in spectral response functions among sensors were small. However, some reflectance biases occur among the near infrared (NIR) reflectance data from satellite products; these biases were potentially caused by absolute calibration errors or cloud/cloud shadow contamination. In addition, reflectance seasonality in AVHRR-based NIR data was very small compared to other datasets, which was partially due to the spring-to-summer increase in the amount of atmospheric water vapor. Radiative transfer simulations suggest that bi-directional reflectance effects were small for the study site and observation period; however, changes in tree density and forest floor conditions affect the absolute value of NIR reflectance, even if the canopy leaf area condition does not change. Reliable monitoring of canopy LAI is achieved by minimizing these effects through the use of NIR reflectance difference, i.e., the difference in reflectance on the observation day from the reflectance on a snow-free/pre-foliation day. This may yield useful and robust parameters for multi-satellite monitoring of the larch canopy LAI with less error from intersensor biases and forest structure/floor differences. Further validation with field data and combined use of other index (e.g. normalized difference water index, NDWI) data will enable an extension of these findings to all Siberian deciduous forests.     

The CM-SAF operational scheme for the satellite based retrieval of solar surface irradiance — A LUT based eigenvector hybrid approach

The radiation budget at the earth surface is an essential climate variable for climate monitoring and analysis as well as for verification of climate model output and reanalysis data. Accurate solar surface irradiance data is a prerequisite for an accurate estimation of the radiation budget and for an efficient planning and operation of solar energy systems.This paper describes a new approach for the retrieval of the solar surface irradiance from satellite data. The method is based on radiative transfer modelling and enables the use of extended information about the atmospheric state. Accurate analysis of the interaction between the atmosphere, surface albedo, transmission and the top of atmosphere albedo has been the basis for the new method, characterised by a combination of parameterisations and “eigenvector” look-up tables. The method is characterised by a high computing performance combined with a high accuracy. The performed validation shows that the mean absolute deviation is of the same magnitude as the confidence level of the BSRN (Baseline Surface Radiation Measurement) ground based measurements and significant lower as the CM-SAF (Climate Monitoring Satellite Application Facility) target accuracy of 10 W/m². The mean absolute difference between monthly means of ground measurements and satellite based solar surface irradiance is 5 W/m² with a mean bias deviation of − 1 W/m² and a RMSD (Root Mean Square Deviation) of 5.4 W/m² for the investigated European sites. The results for the investigated African sites obtained by comparing instantaneous values are also encouraging. The mean absolute difference is with 2.8% even lower as for the European sites being 3.9%, but the mean bias deviation is with − 1.1% slightly higher as for the European sites, being 0.8%. Validation results over the ocean in the Mediterranean Sea using shipboard data complete the validation. The mean bias is − 3.6 W/m² and 2.3% respectively. The slightly higher mean bias deviation over ocean is at least partly resulting from inherent differences due to the movement of the ship (shadowing, allocation of satellite pixel). The validation results demonstrate that the high accuracy of the surface solar irradiance is given in different climate regions. The discussed method has also the potential to improve the treatment of radiation processes in climate and Numerical Weather Prediction (NWP) models, because of the high accuracy combined with a high computing speed.     

A feasible method for fractional snow cover mapping in boreal zone based on a reflectance model

A feasible method for mapping the fraction of Snow Covered Area (SCA) in the boreal zone is presented. The method (SCAmod) is based on a semi-empirical model, where three reflectance contributors (wet snow, snow-free ground and forest canopy), interconnected by an effective canopy transmissivity and SCA, constitute the observed reflectance from the target area. Given the reflectance observation, SCA is solved from the model. The predetermined values for the reflectance contributors can be adjusted to an optional wavelength region, which makes SCAmod adaptable to various optical sensors. The effective forest canopy transmissivity specifies the effect of forests on the local reflectance observation; it is estimated using Earth observation data similar to that employed in the actual SCA estimation. This approach enables operational snow mapping for extensive areas, as auxiliary forest data are not needed.Our study area covers 404 000 km², comprising all drainage basins of Finland (with an average area of 60 km²) and some transboundary drainage basins common with Russia, Norway and Sweden. Applying SCAmod to NOAA/AVHRR cloud-free data acquired during melting periods 2001-2003, we estimated the areal fraction of snow cover for all the 5845 basins. The validation against in situ SCA from the Finnish snow course network indicates that with SCAmod, 15% (absolute SCA-units) accuracy for SCA is gained. Good results were also obtained from the validation against snow cover information provided by the Finnish weather station network, for example, 94% of snow-free and fully snow-covered basins were recognized. A general formula for deriving the statistical accuracy of SCA estimates provided by SCAmod is presented, complemented by the results when the AVHRR data are employed.Snow melting in Finland has been operatively monitored with SCAmod in Finnish Environment Institute (SYKE) since year 2001. The SCA estimates have been assimilated to the Finnish national hydrological modelling and forecasting system since 2003, showing a substantial improvement in forecasts.