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In a boreal region, terrestrial vegetation carbon balance is controlled by vegetation phenology, which steers photosynthesis, respiration, and biomass turnover processes. In the absence of a full mechanistic understanding of environmental processes controlling vegetation phenology (i.e. senescence, dormancy, chilling), empirically-based models are often applied. These models are typically based on greenness proxies (obtained from satellite data) with fixed amplitude thresholds (e.g. of 15%, 50%, 90%) to determine timings of different phenological events. Yet, it is not known how well those percentiles correspond with the timings of events such as the green-up and the senescence. Especially in the boreal region, estimating timings of different phenological events across large spatial scales remains challenging due to the lack of sufficient ground validation data representative of both forest tree canopy and forest understory species compositions, which are both observed by a satellite sensor. From the land surface modeling perspective, there is a need to develop methods to improve the mapping of phenological events for prudent prediction of the land vegetation-atmosphere interactions under different future climates. In this study, we developed a new approach for calibrating boreal forest greenness amplitude thresholds which indicate timings of different phenological transitions in satellite data. The new approach to calibrate satellite-based greenness thresholds was demonstrated using boreal Finland as a case study area (60-70 N°). Using the approach, we computed satellite-based phenological events and compared them to ground reference data on temperature from a network of meteorological stations across Finland. We also investigated the effects of using different phenological events or ground reference temperature data on estimated growing season length. Results showed that while the standard greenness amplitude threshold values corresponded fairly well with the growing season start, the autumn phenology was not well captured by the standard greenness amplitude threshold values, which has direct impact on growing season length. Based on our data, boreal conifer forest senescence (default is 90%) corresponds with the timing of greenness amplitude of ~45%, while boreal conifer forest dormancy (default is 15%) corresponds with the timing of reaching greenness amplitude of ~0%. The approach allows flexible application across spatial scales (i.e. point or grid) and different satellite sensors, and may be combined with any land cover product, and it provides a meaningful linking between surface temperature data and seasonal reflectance measured by satellite sensors.

openaire: EC/H2020/771049/EU//FREEDLES Forest extent mapping is required for climate modeling and monitoring changes in ecosystem state. Different global land cover (LC) products employ simple tree cover (referred also as “forest cover” or even “vegetation cover”) definitions to differentiate forests from non-forests. Since 1990, a large number of forest extent maps have become available. Although many studies have compared forest extent data, they often use old data (i.e., around the year 2000). In this study, we assessed tree cover representations of three different annual, global LC products (MODIS VCF (MOD44B, Collection 6 (C6)), MCD12Q1 (C6), and CCI LC (v.2.1.1)) using the Finnish Multi-Source National Forest Inventory (MS-NFI) data for the year 2017. In addition, we present an intercomparison approach for analyzing spatial representations of coniferous and deciduous species. Intercomparison of different LC products is often overlooked due to challenges involved in non-standard and overlapping LC class definitions. Global LC products are used for monitoring changes in land use and land cover and modeling of surface fluxes. Given that LC is a major driver of global change through modifiers such as land surface albedo, more attention should be paid to spatial mapping of coniferous and deciduous species. Our results show that tree cover was either overestimated or underestimated depending on the LC product, and classification accuracy varied between 42 and 75%. Intercomparison of the LC products showed large differences in conifer and deciduous species spatial distributions. Spatial mapping of coniferous and deciduous tree covers was the best represented by the CCI LC product as compared with the reference MS-NFI data.

Highlights • Understory composition was the best correlated with structural forest canopy measures. • Tree canopy LAI and the fractional cover of understory were strongly related., Information on understory composition and its relationships with the overstory tree canopy, especially leaf area index (LAI), is crucially needed in, e.g., modeling land-atmosphere interactions and productivity of forests. There are also several global LAI products produced from satellite data which need to be validated with ground reference data. However, to date, only scarce field data on simultaneous structural properties of under- and overstory vegetation, and tree canopy LAI, have been available in boreal forests. This paper shows how understory composition and fractional cover of different species types varies in a boreal forest site, and how it is linked to structural properties of the tree layer. The study is based on 301 understory plots collected in an area of ∼16 km2 around Hyytiälä forestry field station, Finland (61°50′N, 24°17′E) in a southern boreal forest site. Forest understory plot data was accompanied with measurements of both standard forest inventory variables and optically-based canopy light transmittance data. Clear differences in average species composition between different site fertility types were observed, but also large variation within each site fertility type was noted. Forest understory composition was better correlated with structural forest canopy measures (e.g., tree canopy LAI, canopy cover, canopy openness) than with traditional forest inventory variables such as tree height or diameter. Forest canopy LAI and the fractional cover of understory were strongly related, especially in more fertile sites. Our results highlight the role of tree canopy structural metrics as modifiers of the understory light climate and growing conditions, also, in boreal forests.

Forest structural properties largely govern surface fluxes of moisture, energy, and momentum that strongly affect regional climate and hydrology. Forest structural properties are greatly shaped by forest management activities, especially in the Fennoscandia (Norway, Sweden, and Finland). Insight into transient developments in forest structure in response to management intervention is therefore essential to understanding the role of forest management in mitigating regional climate change. The aim of this study is to present a simple grid-based framework – the Fennoscandic Forest State Simulator (F2S2) -- for predicting time-dependent forest structural trajectories in a manner compatible with land models employed in offline or asynchronously coupled climate and hydrological research. F2S2 enables the prescription of future regional forest structure as a function of: i) exogenously defined scenarios of forest harvest intensity; ii) forest management intensity; iii) climate forcing. We demonstrate its application when applied as a stand-alone tool for forecasting three alternative future forest states in Norway that differ with respect to background climate forcing, forest harvest intensity (linked to two Shared Socio-economic Pathways (SSPs)), and forest management intensity. F2S2 captures impacts of climate forcing and forest management on general trends in forest structural development over time, and while climate is the main driver of longer-term forest structural dynamics, the role of harvests and other management-driven effects cannot be overlooked. To our knowledge this is the first paper presenting a method to map forest structure in space and time in a way that is compatible with land surface or hydrological models employing sub-grid tiling.

openaire: EC/H2020/771049/EU//FREEDLES Metsien albedon vaihtelun selvittäminen antaa tietoa energiavirroista maan ja ilmakehän välillä sekä tarkennettuja lähtötietoja ilmastomallinnukseen. Siihen, pitäisikö albedon vaihtelu erityyppisten metsien välillä ottaa huomioon ilmastopolitiikassa tai metsänhoidon päätöksenteossa, ei pystytä vastaamaan ennen kuin kaikki metsien ilmastoon vaikuttavat tekijät on arvioitu samanaikaisesti. Boreaalisten metsien albedolla on omat erityispiirteensä verrattuna muiden kasvillisuusvyöhykkeiden albedoon; metsiemme albedo vaihtelee muun muassa lumitilanteen, puulajikoostumuksen, metsän rakenteen sekä aluskasvillisuuden mukaan. Tässä katsausartikkelissa kerromme mikä albedo on, miksi siitä ollaan kiinnostuneita, miten metsän albedoa on mahdollista arvioida ja mitkä tekijät vaikuttavat siihen. Tarkastelumme painottuu suomalaisiin metsiin. Lisäksi tarkastelemme Suomen metsien albedon kehitystä viime vuosikymmenten aikana ja pohdimme mitä seikkoja boreaalisten metsien albedosta ei vielä ymmärretä.

Predicting the surface albedo of a forest of a given species composition or plant functional type is complicated by the wide range of structural attributes it may display. Accurate characterizations of forest structure are therefore essential to reducing the uncertainty of albedo predictions in forests, particularly in the presence of snow. At present, forest albedo parameterizations remain a nonnegligible source of uncertainty in climate models, and the magnitude attributable to insufficient characterization of forest structure remains unclear. Here we employ a forest classification scheme based on the assimilation of Fennoscandic (i.e., Norway, Sweden, and Finland) national forest inventory data to quantify the magnitude of the albedo prediction error attributable to poor characterizations of forest structure. For a spatial domain spanning ~611,000 km2 of boreal forest, we find a mean absolute wintertime (December–March) albedo prediction error of 0.02, corresponding to a mean absolute radiative forcing ~0.4 W/m2. Further, we evaluate the implication of excluding albedo trajectories linked to structural transitions in forests during transient simulations of anthropogenic land use/land cover change. We find that, for an intensively managed forestry region in southeastern Norway, neglecting structural transitions over the next quarter century results in a foregone (undetected) radiatively equivalent impact of ~178 Mt‐CO2‐eq. year−1 on average during this period—a magnitude that is roughly comparable to the annual greenhouse gas emissions of a country such as The Netherlands. Our results affirm the importance of improving the characterization of forest structure when simulating surface albedo and associated climate effects.

We propose 3D triangulations of airborne Laser Scanning (ALS) point clouds as a new approach to derive 3D canopy structures and to estimate forest canopy effective LAI (LAI e ). Computational geometry and topological connectivity were employed to filter the triangulations to yield a quasi-optimal relationship with the field measured LAI e . The optimal filtering parameters were predicted based on ALS height metrics, emulating the production of maps of LAI e and canopy volume for large areas. The LAI e from triangulations was validated with field measured LAI e and compared with a reference LAI e calculated from ALS data using logarithmic model based on Beer’s law. Canopy transmittance was estimated using All Echo Cover Index (ACI), and the mean projection of unit foliage area (β) was obtained using no-intercept regression with field measured LAI e . We investigated the influence species and season on the triangulated LAI e and demonstrated the relationship between triangulated LAI e and canopy volume. Our data is from 115 forest plots located at the southern boreal forest area in Finland and for each plot three different ALS datasets were available to apply the triangulations. The triangulation approach was found applicable for both leaf-on and leaf-off datasets after initial calibration. Results showed the Root Mean Square Errors (RMSEs) between LAI e from triangulations and field measured values agreed the most using the highest pulse density data (RMSE = 0.63, the coefficient of determination (R 2 ) = 0.53). Yet, the LAI e calculated using ACI-index agreed better with the field measured LAI e (RMSE = 0.53 and R 2 = 0.70). The best models to predict the optimal alpha value contained the ACI-index, which indicates that within-crown transmittance is accounted by the triangulation approach. The cover indices may be recommended for retrieving LAI e only, but for applications which require more sophisticated information on canopy shape and volume, such as radiative transfer models, the triangulation approach may be preferred.

The fraction of absorbed Photosynthetically Active Radiation (fPAR, 400–700 nm) can be retrieved from satellite measurements and is used to estimate and monitor ecosystem productivity and vegetation phenology. Although fPAR depends primarily on the Leaf Area Index (LAI), it is also influenced by the incoming radiation field and thus varies diurnally and seasonally. The overall aim of this study was to compare different ground and satellite-based fPAR estimates. The field data consisted of nearly 1000 plots located in the southern boreal forest zone in Finland. For all plots, data on canopy transmittance and forest inventory variables were available. We compared two different approaches to estimate fPAR and studied which satellite-based vegetation indices are best correlated with the instantaneous ground reference fPAR. Finally, a canopy fPAR model was used to assess some underlying assumptions of the satellite-based fPAR products: the similarity between instantaneous (at the time of satellite overpass) and daily-integrated fPAR. Results showed that allometric estimates of LAI and Beer’s law are not sufficient to estimate fPAR in a boreal forest, because canopy openness strongly influences the relationship between canopy LAI and fPAR. We found that vegetation indices calculated from Landsat were only moderately correlated (maximum R2 = 0.34) with the ground-based fPAR. Our study showed that the absolute difference between fPAR at the moment of the satellite overpass and daily integrated fPAR exceeds the 0.05 fPAR-units limit set by Global Climate Observing Systems −network in northern latitudes during the peak growing season. Thus, at high latitudes covered by boreal forests, the satellite-based fPAR systematically underestimates the daily integrated black sky fPAR.

Vegetation optical properties have a direct impact on canopy absorption and scattering and are thus needed for modeling surface fluxes. Although plant functional type (PFT) classification varies between different land surface models (LSMs), their optical properties must be specified. The aim of this study is to revisit the “time-invariant optical properties table” of the Simple Biosphere (SiB) model (later referred to as the “SiB table”) presented 30 years ago by Dorman and Sellers (1989), which has since been adopted by many LSMs. This revisit was needed as many of the data underlying the SiB table were not formally reviewed or published or were based on older papers or on personal communications (i.e., the validity of the optical property source data cannot be inspected due to missing data sources, outdated citation practices, and varied estimation methods). As many of today's LSMs (e.g., the Community Land Model (CLM), the Jena Scheme of Atmosphere Biosphere Coupling in Hamburg (JSBACH), and the Joint UK Land Environment Simulator (JULES)) either rely on the optical properties of the SiB table or lack references altogether for those they do employ, there is a clear need to assess (and confirm or correct) the appropriateness of those being used in today's LSMs. Here, we use various spectral databases to synthesize and harmonize the key optical property information of PFT classification shared by many leading LSMs. For forests, such classifications typically differentiate PFTs by broad geo-climatic zones (i.e., tropical, boreal, temperate) and phenology (i.e., deciduous vs. evergreen). For short-statured vegetation, such classifications typically differentiate between crops, grasses, and photosynthetic pathway. Using the PFT classification of the CLM (version 5) as an example, we found the optical properties of the visible band (VIS; 400–700 nm) to fall within the range of measured values. However, in the near-infrared and shortwave infrared bands (NIR and SWIR; e.g., 701–2500 nm, referred to as “NIR”) notable differences between CLM default and measured values were observed, thus suggesting that NIR optical properties are in need of an update. For example, for conifer PFTs, the measured mean needle single scattering albedo (SSA, i.e., the sum of reflectance and transmittance) estimates in NIR were 62 % and 78 % larger than the CLM default parameters, and for PFTs with flat leaves, the measured mean leaf SSA values in NIR were 20 %, 14 %, and 19 % larger than the CLM defaults. We also found that while the CLM5 PFT-dependent leaf angle values were sufficient for forested PFTs and grasses, for crop PFTs the default parameterization appeared too vertically oriented, thus warranting an update. In addition, we propose using separate bark reflectance values for conifer and deciduous PFTs and demonstrate how shoot-level clumping correction can be incorporated into LSMs to mitigate violations of turbid media assumption and Beer's law caused by the nonrandomness of finite-sized foliage elements.

Vegetation optical properties have a direct impact on canopy absorption and scattering and are thus needed for modeling surface fluxes. Although Plant Functional Type (PFT) classification varies between different land surface models (LSMs), their optical properties must be specified. The aim of this study is to revisit the time-invariant optical properties table of the Simple Biosphere (SiB) model (later referred as SiB-table) presented 30-years ago by Dorman and Sellers (1989) which has since become adopted by many LSMs. This revisit was needed as much of the data underlying the SiB-table was not formally reviewed or published or was based on older papers or personal communications (i.e. the validity of the optical property source data cannot be inspected due to missing data sources, outdated citation practices, and varied estimation methods). As many of today's LSMs (e.g. Community Land Model (CLM), Jena Scheme of Atmosphere Biosphere Coupling in Hamburg (JSBACH), and Joint UK Land Environment Simulator (JULES)) either rely on the optical properties of the SiB-table or lack references altogether for those they do employ, there is a clear need to assess (and confirm or correct) the appropriateness of those being used in today's LSMs. Here, we use various spectral databases to synthesize and harmonize the key optical property information of PFT classification shared by many leading LSMs. For forests, such classifications typically differentiate PFTs by broad geo-climatic zones (i.e. tropical, boreal, temperate) and phenology (i.e. deciduous vs. evergreen). For short-statured vegetation, such classifications typically differentiate between crops and grasses and by photosynthetic pathway. Using the PFT classification of the CLM (version 5) as an example, we found the optical properties of the visible band (VIS; 400–700 nm) to be appropriate. However, in the near-infrared and shortwave infrared bands (NIR+SWIR; e.g. 701–2500 nm, referred as NIR) notable differences between CLM default and measured estimates were observed, thus suggesting that NIR optical properties need updating in the model. For example, for conifer PFTs, the measured mean needle albedo estimates in NIR were 62 % and 78 % larger than the CLM default parameters, and for PFTs with flat-leaves, the measured mean leaf albedo values in NIR were 20 %, 14 % and 19 % larger than the CLM defaults. We also found that while the CLM5 PFT-dependent leaf angle definitions were sufficient for forested PFTs and grasses, for crop PFTs the default parameterization appeared too vertically oriented thus warranting an update. In addition, we propose using separate bark reflectance values for conifer and deciduous PFTs and introduce the concept and application of photon recollision probability (p). The p may be used to upscale needle spectra into shoot spectra to meet the common assumption that foliage is located randomly within the canopy volume (behind canopy radiative transfer calculation) to account for multiple scattering effects caused by needles clustered into shoots.

Forest management affects the distribution of tree species and the age class of a forest, shaping its overall structure and functioning and in turn the surface–atmosphere exchanges of mass, energy, and momentum. In order to attribute climate effects to anthropogenic activities like forest management, good accounts of forest structure are necessary. Here, using Fennoscandia as a case study, we make use of Fennoscandic National Forest Inventory (NFI) data to systematically classify forest cover into groups of similar aboveground forest structure. An enhanced forest classification scheme and related lookup table (LUT) of key forest structural attributes (i.e., maximum growing season leaf area index (LAImax), basal-area-weighted mean tree height, tree crown length, and total stem volume) was developed, and the classification was applied for multisource NFI (MS-NFI) maps from Norway, Sweden, and Finland. To provide a complete surface representation, our product was integrated with the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) map of present day land cover (v.2.0.7). Comparison of the ESA LC and our enhanced LC products (https://doi.org/10.21350/7zZEy5w3) showed that forest extent notably (κ = 0.55, accuracy 0.64) differed between the two products. To demonstrate the potential of our enhanced LC product to improve the description of the maximum growing season LAI (LAImax) of managed forests in Fennoscandia, we compared our LAImax map with reference LAImax maps created using the ESA LC product (and related cross-walking table) and PFT-dependent LAImax values used in three leading land models. Comparison of the LAImax maps showed that our product provides a spatially more realistic description of LAImax in managed Fennoscandian forests compared to reference maps. This study presents an approach to account for the transient nature of forest structural attributes due to human intervention in different land models.

In this study we use ground reference data from 962 forest plots to demonstrate the potential of Sentinel-2 (S2) bands in estimating canopy biophysical properties in boreal forests in Finland. We simulated canopy bidirectional reflectance factors (BRFs) using the PARAS model, which applies photon recollision probability. Results showed that the highest correlation between simulated S2 BRFs and fraction of absorbed photosynthetically active radiation (fPAR) was for the band combination band 7/band 9 (wavelengths 773–793 nm and 935–955 nm, respectively) (the coefficient of determination (R2) was 0.93). For effective leaf area index (LAIe) the best band combination was band 8/band 4 (wavelengths 785–900 nm and 650–680 nm, respectively) (R2 = 0.93). Based on this study, the above-ground biomass (AGB) and S2 band combinations did not show strong relationships (R2 = 0.24). The new inverted red-edge chlorophyll index (IRECI) and Sentinel-2 red-edge position – index (S2REP) showed moderate relationships w...

The fraction of absorbed Photosynthetically Active Radiation (fPAR) plays a critical role in carbon balance studies and is one of the Essential Climate Variables (ECV). fPAR can be used to monitor CO2 assimilation by vegetation both seasonally and interannually. Temporal courses of fPAR are difficult to measure in field conditions, and thus, they are most often estimated based on models which quantify the dependency of absorbed radiation on canopy structure. In this study, we demonstrate how a physically-based canopy radiation model was adapted into an fPAR model, and compare modeled and measured fPAR in structurally different forest stands. The model is based on the spectral invariants theory, and uses leaf area index (LAI), canopy gap fractions and spectra of foliage and understory as input data. For validation of the model, measurements of instantaneous fPAR were performed using the TRAC instrument in nine Scots pine, Norway spruce and Silver birch stands in southern Finland. Good agreement was found between modeled and measured fPAR. Next, we applied the model to predict temporal courses of fPAR. For this, continuous data on incoming radiation were obtained from a nearby flux tower. Polynomial functions were fitted to the measured canopy gap fractions to create a hemispherical gap fraction distribution for each stand. These hemispherical gap fraction distributions were used together with sky irradiance models to simulate incoming radiation fields. Field fPAR estimates agreed with modeled ones (RMSE for morning and noon were 0.03 and 0.06, respectively). Application of the model to simulate diurnal and seasonal values of fPAR for the study stands indicated that the ratio of direct-to-total incident radiation and LAI are the key factors behind the magnitude and variation of stand-level fPAR values.

The amount of green leaf area, quantified by the leaf area index (LAI), is one of the key factors determining ecosystem net primary production and energy exchange between land surfaces and the atmosphere. LAI can be measured indirectly using optical methods that are based on the tight relationship between LAI and canopy light transmittance, or through allometric regression models. Until now there has been very little research to compare LAI estimated by the two different approaches. In this study we compare optically-based estimates of LAI to estimates produced using local foliage biomass models for three boreal tree species. Our study is based on 661 Scots pine, Norway spruce and Silver birch stands in the southern boreal zone in Finland. Routine stand inventory and LAI-2000 Plant Canopy Analyzer measurements were performed in all stands. We used three allometric foliage mass models to calculate foliage masses for each stand. The foliage mass estimates were then converted to LAI using specific leaf area (SLA) values. A theoretical stem diameter distribution was also used together with the only diameter-dependent foliage mass models. Optical LAI was corrected for shoot-level clumping (i.e., the clumping of needles into shoots). Finally, we estimated LAI from measured canopy gap fractions by inverting a canopy radiation model. Allometric foliage mass models produced significantly different foliage mass estimates (and the LAI estimates based on them). Among the allometric methods, however, the most sophisticated in terms of input tree variables agreed fairly well with each other and also with the clumping-corrected optical methods. The results indicate a need for caution when using foliage biomass or LAI estimates calculated using different models, especially if the estimates are to be used as an input for other models. The simple shoot-level clumping correction of optical LAI showed the best fit with allometric LAI. Our results support the use of a simple shoot-level clumping correction instead of more complicated correction methods in optical LAI estimation in coniferous forests.

Medium resolution leaf area index (LAI) products have usually been validated only at the peak of the growing season for boreal forest areas. In this study, we evaluated seasonal variations in MODIS Collection 5 (C5) LAI products for an evergreen coniferous forest area in southern Finland. A time series of field measurements of LAI and nine fine resolution satellite images (SPOT4 HRVIR, EO-1 Hyperion) for the snow free period in 2010 were used for developing empirical regression models for LAI estimation. We used two different reference LAI: the first one corresponded to an effective overstory LAI, and the second one was a clumping-corrected LAI that included both over- and understory vegetation. According to the results, MODIS LAI retrievals were closer to the effective LAI in the spring and autumn, when saturation of MODIS LAI main algorithm did not occur. On the other hand, in the summer, when saturation occurred, MODIS LAI was closer to the total LAI. Therefore, as a result of high LAI retrieved under the condition of saturation, the seasonal course of LAI was exaggerated in comparison to the reference LAI time series. Furthermore, MODIS LAI retrievals were unrealistically small before the leaf burst if considering the large proportion of evergreen coniferous forest in the study area. The results emphasize the need for multi-seasonal validation data sets in order to better understand the performance of LAI retrieval algorithms for evergreen coniferous forests throughout the snow free period.

Field measurements of leaf area index (LAI) are needed for many ecological and remote sensing applications, but they are laborious and time-consuming. The sampling designs of optical LAI measurements have been mainly driven by the field-of-view of the sensor as well as the upscaling process and spatial resolution of satellite imagery. An optimized sampling design would improve the accuracy of the LAI measurements and reduce the time needed for field work. However, a systematic comparison of sampling designs for LAI measurements in boreal forests has not been done. The aim of this study was to analyze the effect of sampling designs on the accuracy of LAI estimates obtained with the LAI-2000 Plant Canopy Analyzer in a boreal forest. Field data was collected from six study sites in Hyytiala, central Finland. Two sites were dominated by Pinus sylvestris, two sites by Picea abies, and two sites by Betula pendula. Each site had a regular 9 × 9 -grid of LAI measurement points. All sites were studied using regular and systematic sampling techniques. Statistical analysis was used to examine the site-specific number of measurement points needed in random sampling to achieve a given precision, and as a starting point for regular and systematic sampling. Results indicated that twelve randomly located measurement points are enough to characterize site-specific LAI with standard error less than 0.15 LAI units. If the twelve sampling points are organized systematically, the average standard error is reduced to 0.06 units and the coefficient of variation to less than 0.03.

The influence of the seasonal cycle of boreal forest understory has been noticed in global remote sensing of vegetation, especially in remote sensing of biophysical properties (e.g. leaf area index) of the tree-layer in a forest. A general problem in the validation of operationally produced global biophysical vegetation products is the lack of ground reference data on the seasonal variability of different land surface types. Currently, little is known about the spectral properties of the understory layers of boreal forests, and even less is known about the seasonal dynamics of the spectra. In this paper, we report seasonal trajectories of understory reflectance spectra measured in a European boreal forest. Four study sites representing different forest fertility site types were selected from central Finland. The understory composition was recorded and its spectra measured with an ASD FieldSpec Hand-Held UV/VNIR Spectroradiometer ten times during the growing period (from May to September) in 2010. Our results show that the spectral differences between and within understory types are the largest at the peak of the growing season in early July whereas in the beginning and end of the growing season (i.e. early May and late September, respectively) the differences between the understory types are marginal. In general, the fertile sites had the brightest NIR spectra throughout the growing season whereas infertile types appeared darker in NIR. Our results also indicated that a mismatch in the seasonal development of understory and tree layers does not occur in boreal forests: the understory and tree layer vegetation develop at a similar pace in the spring (i.e. there are no or only few spring ephemerals present), and the forests with the strongest seasonal dynamics in tree canopy structure (LAI) have also the strongest dynamics in understory spectra.

Creative Commons Attribution License (CC BY 4.0) Remote sensing of the fraction of absorbed Photosynthetically Active Radiation (fPAR) has become a timely option to monitor forest productivity. However, only a few studies have had ground reference fPAR datasets containing both forest canopy and understory fPAR from boreal forests for the validation of satellite products. The aim of this paper was to assess the performance of two currently available satellite-based fPAR products: MODIS fPAR (MOD15A2, C5) and GEOV1 fPAR (g2_BIOPAR_FAPAR), as well as an NDVI-fPAR relationship applied to the MODIS surface reflectance product and a Landsat 8 image, in a boreal forest site in Finland. Our study area covered 16 km(2) and field data were collected from 307 forest plots. For all plots, we obtained both forest canopy fPAR and understory fPAR. The ground reference total fPAR agreed better with GEOV1 fPAR than with MODIS fPAR, which showed much more temporal variation during the peak-season than GEOV1 fPAR. At the chosen intercomparison date in peak growing season, MODIS NDVI based fPAR estimates were similar to GEOV1 fPAR, and produced on average 0.01 fPAR units smaller fPAR estimates than ground reference total fPAR. MODIS fPAR and Landsat 8 NDVI based fPAR estimates were similar to forest canopy fPAR.

The aim of this dissertation is to assess the accuracy of different ground reference methods used to validate satellite based leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (fPAR) products. LAI and fPAR are strongly linked, although they principally and practically measure different properties: LAI quantifies the areal interphase between soil and atmosphere, whereas fPAR quantifies the energy available for photosynthesis. Until now, the development of remote sensing based methods to estimate LAI and fPAR in a boreal forest has been hindered by the scarcity of ground data, which is required to validate and develop existing algorithms. The aim of the first part of this dissertation was to assess the impacts of different methodological approaches to estimate LAI in boreal forests, and to validate satellite based LAI products. Results showed that the accuracy of ground based LAI estimates is sensitive to both the retrieval methods and sampling scheme used to collect the optical LAI data. The satellite based measurements of LAI demonstrated a large temporal variability in LAI. The second part of the dissertation focused on measuring and modeling fPAR in a boreal forest. A new scheme for measuring and modeling ground reference fPAR based on photon recollision probability was presented in this dissertation. Ground reference fPAR is usually estimated only for the forest canopy layer. This study is among the first ones to validate the new global satellite based fPAR product called GEOV1 using data of both the forest canopy and understory layers from boreal forests. Results showed that satellite based fPAR products may correspond better with the total fPAR, instead of only the forest canopy fPAR as has often been presumed. Tämän väitöskirjan tarkoituksena oli kehittää LAI:n ja fPAR:in maastomittausmenetelmiä ja arvioida nykyisten satelliittipohjaisten LAI- ja fPAR-tuotteiden toimivuutta boreaalisissa metsissä. Lehtialaindeksi (leaf area index, LAI) kuvaa lehtien toispuolista pinta-alaa maapinta-alaa kohden (m2/m2). Akronyymi fPAR on lyhennelmä sanoista fraction of absorbed photosynthetically active radiation (PAR) ja se kuvaa kasvillisuuden kykyä sitoa auringosta saapuvaa säteilyä. fPAR määräytyy LAI:n ja auringon kulman perusteella. LAI:ta ja fPAR:ia voidaan arvioida avaruudesta tehtävällä kaukokartoituksella ja mielenkiinnon kohteena voi olla esimerkiksi globaali ympäristön seuranta. Tällä hetkellä kaukokartoitusmenetelmien kehittymistä hidastaa maastoaineistojen puute, sillä maastoaineistot ovat välttämättömiä mallien tarkkuuden arvioinnissa. Koska LAI on yksi tärkeimpiä fPAR:iin vaikuttavia muuttujia, väitöskirjan ensimmäinen osio keskittyi LAI:n maastomittausmenetelmien tarkkuuden arviointiin. Ensimmäisen osan tarkoituksena oli selvittää, kuinka erilaiset LAI:n arviointitavat ja otanta-asetelmat toimivat boreaalisissa metsissä. Satelliitista mitattujen LAI-arvojen kelpoisuutta arvioitiin vertaamalla niitä maastossa mitattuihin arvoihin. Tulosten mukaan erilaiset LAI:n arviointitavat tuottavat systemaattisesti poikkeavia arvioita ja arvioiden tarkkuus riippuu paitsi käytetystä menetelmästä, myös maastomittausten otanta-asetelmasta. Tutkimuksessa havaittiin, että satelliitista mitattuihin LAI-arvoihin sisältyy paljon ajallista ja paikallista vaihtelua, joka johtuu osin satelliitin mittaaman signaalin saturoitumisesta. Väitöskirjan toinen osa keskittyi fPAR:in mittaamiseen ja mallintamiseen. Tutkimuksen aluksi esiteltiin uusi fPAR-malli, joka soveltuu laajojen alueiden fPAR-arviointiin. Mallin toimivuutta arvioitiin vertaamalla mitattuja ja mallinnettuja fPAR-arvoja toisiinsa. fPAR-mallin todettiin toimivan hyvin. Tämän jälkeen tutkittiin, kuinka hyvin nykyiset satelliittimittauksiin perustuvat fPAR-tuotteet vastaavat maastomittauksiin perustuvaa fPARia. Yleensä satelliittituotteiden toimivuutta arvioitaessa on keskitytty vain metsän latvuskerroksen sitoman säteilymäärän arviointiin, mutta tässä tutkimuksessa huomioitiin myös aluskasvillisuuden sitoma säteily. Tulokset osoittivat, että satelliittimittauksiin perustuva fPAR voi vastata paremmin metsikön latvuksen ja aluskasvillisuuden yhteenlaskettua fPAR:ia kuin pelkän latvuskerroksen fPAR:ia.

The green photosynthesizing leaf area of a canopy is an important characteristic of the status of the vegetation in terms of its health and production potential. At stand level, the amount of leaf area in a canopy is represented by a variable called the leaf area index (LAI), which is one of the key biophysical parameters in the global monitoring and mapping of vegetation by satellite remote sensing (Morisette et al., 2006). In this paper we adopt the, by now widely accepted, definition of LAI as the hemi-surface or half of the total surface area of all leaves or needles in the vegetation canopy divided by the horizontal ground area below the canopy. The definition is in line with the original definition of LAI, formulated for flat and (assumedly) infinitely thin leaves (Watson, 1947), as the one-sided leaf area per unit ground area. For coniferous canopies, the question arose on how to define the “one-sided” area of non-flat needles. While projected needle area formerly often has been used erroneously as a synonym to one-sided flat leaf area, it is now commonly accepted that the hemi-surface needle area represents the logical counterpart to the one-sided area of flat leaves (e.g. Chen & Black, 1992; Stenberg, 2006).

This study covers the preprocessing and atmospheric correction of a seasonal time series five Hyperion EO-1 images from Hyytiälä, Southern Finland (61° 51′N, 24° 17′E). The time series ranges from May 5th 2010 to July 11th 2010, covering much of the growing season and the seasonal changes in vegetation reflectance. Atmospheric correction of the time series was done with Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and ATmospheric CORrection (ATCOR) algorithms for comparison. Both algorithms performed well with Hyperion imagery. Different red edge inflection point (REIP) calculation methods were analyzed to determine their applicability for Hyperion imagery. REIP was calculated using four-point interpolation, Lagrangian interpolation, and fifth order polynomial fitting. Due to the dynamics of the red edge, polynomial fitting was seen as the best method for calculating the REIP. REIP did not correlate strongly with Leaf Area Index (LAI) but a stronger correlation was observed with understory REIP.

Key message We present a new approach to calibrate timings of phenological events from satellite data (e.g., Sentinel-2 MSI data) with readily available surface temperature data. The new approach improves the estimation of growing season length in boreal forests. Context Satellite data is used to calibrate phenology models employed in land surface model components of climate models. However, realistic quantification of forest phenological transitions, such as the greenup and senescence, across large spatial scales remains challenging due to the lack of sufficient ground validation data representative of both forest tree canopy and forest understory species compositions. Aims The aim of this study was to develop a new approach to benchmark boreal forest land surface phenology obtained from Sentinel-2 (S2) against surface temperature data. Methods We computed S2 phenological transition dates and compared them to ground reference data on temperature from a network of meteorological stations across Finland (60–70N°). Results Our results showed that applying standard phenometrics directly to S2 data to estimate the growing season length in boreal forests may lead to clear biases in all species groups. Conclusion Our approach to use temperature data to calibrate boreal forest phenometrics allows flexible application across spatial scales (i.e., point or grid) and different satellite sensors. It can be combined with any vegetation land cover product to provide a link between surface temperature data and forest seasonal reflectance properties.