Browsing by Subject "LEAF-AREA INDEX"

Sort by: Order: Results:

Now showing items 1-19 of 19
  • Gielen, Bert; Acosta, Manuel; Altimir, Nuria; Buchmann, Nina; Cescatte, Alessandro; Ceschia, Eric; Fleck, Stefan; Hortnagal, Lukas; Klumpp, Katja; Kolari, Pasi; Lohile, Annalea; Loustau, Denis; Maranon-Jimenez, Sara; Manisp, Languy; Matteucci, Giorgio; Merbold, Lutz; Metzger, Christine; Moureaux, Christine; Montagnani, Leonardo; Nilsson, Mats B.; Osborne, Bruce; Papale, Dario; Pavelka, Marian; Saunders, Matthew; Simioni, Guillaume; Soudani, Kamel; Sonnentag, Oliver; Tallec, Tiphaine; Tuittila, Eeva-Stiina; Peichl, Matthias; Pokorny, Radek; Vincke, Caroline; Wohljahrt, Georg (2018)
    The Integrated Carbon Observation System is a Pan-European distributed research infrastructure that has as its main goal to monitor the greenhouse gas balance of Europe. The ecosystem component of Integrated Carbon Observation System consists of a multitude of stations where the net greenhouse gas exchange is monitored continuously by eddy covariance measurements while, in addition many other measurements are carried out that are a key to an understanding of the greenhouse gas balance. Amongst them are the continuous meteorological measurements and a set of non-continuous measurements related to vegetation. The latter include Green Area Index, aboveground biomass and litter biomass. The standardized methodology that is used at the Integrated Carbon Observation System ecosystem stations to monitor these vegetation related variables differs between the ecosystem types that are represented within the network, whereby in this paper we focus on forests, grasslands, croplands and mires. For each of the variables and ecosystems a spatial and temporal sampling design was developed so that the variables can be monitored in a consistent way within the ICOS network. The standardisation of the methodology to collect Green Area Index, above ground biomass and litter biomass and the methods to evaluate the quality of the collected data ensures that all stations within the ICOS ecosystem network produce data sets with small and similar errors, which allows for inter-comparison comparisons across the Integrated Carbon Observation System ecosystem network.
  • Hartikainen, Saara M.; Jach, Agnieszka; Grane, Aurea; Robson, Thomas Matthew (2018)
  • Ruiz-Benito, Paloma; Vacchiano, Giorgio; Lines, Emily R.; Reyer, Christopher P.O.; Ratcliffe, Sophia; Morin, Xavier; Hartig, Florian; Mäkelä, Annikki; Yousefpour, Rasoul; Chaves, Jimena E.; Palacios-Orueta, Alicia; Benito-Garzón, Marta; Morales-Molino, Cesar; Camarero, J. Julio; Jump, Alistair S.; Kattge, Jens; Lehtonen, Aleksi; Ibrom, Andreas; Owen, Harry J.F.; Zavala, Miguel A. (2020)
    Climate change is expected to cause major changes in forest ecosystems during the 21st century and beyond. To assess forest impacts from climate change, the existing empirical information must be structured, harmonised and assimilated into a form suitable to develop and test state-of-the-art forest and ecosystem models. The combination of empirical data collected at large spatial and long temporal scales with suitable modelling approaches is key to understand forest dynamics under climate change. To facilitate data and model integration, we identified major climate change impacts observed on European forest functioning and summarised the data available for monitoring and predicting such impacts. Our analysis of c. 120 forest-related databases (including information from remote sensing, vegetation inventories, dendroecology, palaeoecology, eddy-flux sites, common garden experiments and genetic techniques) and 50 databases of environmental drivers highlights a substantial degree of data availability and accessibility. However, some critical variables relevant to predicting European forest responses to climate change are only available at relatively short time frames (up to 10-20 years), including intra-specific trait variability, defoliation patterns, tree mortality and recruitment. Moreover, we identified data gaps or lack of data integration particularly in variables related to local adaptation and phenotypic plasticity, dispersal capabilities and physiological responses. Overall, we conclude that forest data availability across Europe is improving, but further efforts are needed to integrate, harmonise and interpret this data (i.e. making data useable for non-experts). Continuation of existing monitoring and networks schemes together with the establishments of new networks to address data gaps is crucial to rigorously predict climate change impacts on European forests.
  • Li, Zhouyuan; Zhang, Heng; Xu, Yanjie; Wang, Shaopeng (2021)
    1. Rapid biodiversity loss has triggered decades of research on the relationships between biodiversity and community stability. Recent studies highlighted the importance of species traits for understanding biodiversity-stability relationships. The species with high growth rates ('fast' species) are expected to be less resistant to environmental stress but recover faster if disturbed; in contrast, the species with slow growth rates ('slow' species) can be more resistant but recover more slowly if disturbed. Such a 'fast-slow' trait continuum provides a new perspective for understanding community stability, but its validity has mainly been examined in plant communities. Here, we investigate how 'fast-slow' trait composition, together with species richness and environmental factors, regulate avian community stability at a continental scale. 2. We used bird population records from the North American Breeding Bird Survey during 1988-2017 and defined avian community stability as the temporal invariability of total community biomass. We calculated species richness and the community-weighted mean (CWM) and functional diversity (FD) of four key life-history traits, including body size, nestling period (i.e. period of egg incubation and young bird fledging), life span and clutch size (i.e. annual total number of eggs). Environmental factors included temperature, precipitation and leaf area index (LAI). 3. Our analyses showed that avian community stability was mainly driven by the CWM of the 'fast-slow' trait. Communities dominated by 'fast' species (i.e. species with small body size, short nestling period and life span and large clutch size) were more stable than those dominated by 'slow' species (i.e. species with large body size, long nestling period and life span and small clutch size). Species richness and the FD of the 'fast-slow' trait explained much smaller proportions of variation in avian community stability. Temperature had direct positive effects on avian community stability, while precipitation and leaf area index affected community stability indirectly by influencing species richness and trait composition. 4. Our study demonstrates that composition of 'fast-slow' traits is the major biotic driver of avian community stability over North America. Temperature is the most important abiotic factor, but its effect is weaker than that of the 'fast-slow' trait. An integrated framework combining 'fast-slow' trait composition and temperature is needed to understand the response of avian communities in a changing environment.
  • Neumann, Mathias; Moreno, Adam; Thurnher, Christopher; Mues, Volker; Härkönen, Sanna; Mura, Matteo; Bouriaud, Olivier; Lang, Mait; Cardellini, Giuseppe; Thivolle-Cazat, Alain; Bronisz, Karol; Merganic, Jan; Alberdi, Iciar; Astrup, Rasmus; Mohren, Frits; Zhao, Maosheng; Hasenauer, Hubert (2016)
    Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at
  • Junttila, Samuli; Kaasalainen, Sanna; Vastaranta, Mikko; Hakala, Teemu; Nevalainen, Olli; Holopainen, Markus (2015)
    Global warming is posing a threat to the health and condition of forests as the amount and length of biotic and abiotic disturbances increase. Most methods for detecting disturbances and measuring forest health are based on multi- and hyperspectral imaging. We conducted a test with spruce and pine trees using a hyperspectral Lidar instrument in a laboratory to determine the capability of combined range and reflectance measurements to investigate forest health. A simple drought treatment was conducted by leaving the harvested trees outdoors without a water supply for 12 days. The results showed statistically significant variation in reflectance after the drought treatment for both species. However, the changes differed between the species, indicating that drought-induced alterations in spectral characteristics may be species-dependent. Based on our results, hyperspectral Lidar has the potential to detect drought in spruce and pine trees.
  • Ylivinkka, Ilona; Itämies, Juhani; Klemola, Tero; Ruohomäki, Kai; Kulmala, Markku; Taipale, Ditte (2020)
    Laboratory studies have shown that heibivory-induced biogenic volatile organic compound (BVOC) emissions might enhance aerosol formation and growth. To increase understanding of the atmospheric relevance of this enhancement, we analyzed 25 years of data from SMEAR I (Station for Measuring Ecosystem-Atmosphere Relations) in northern Finland, where autumnal moth (Epirrita autumnata) larvae are prominent defoliators of mountain birch. We did not find a direct correlation between the autumnal moth density and aerosol processes, nor between the total number concentration and temperature, and hence the basal BVOC emissions. Instead, there is some evidence that the total particle concentration is elevated even for a few years after the infestation due to delayed defense response of mountain birch. The low total biomass of the trees concomitantly with low autumnal moth densities during most of the years of our study, may have impacted our results, hindering the enhancement of aerosol processes.
  • Taipale, Ditte; Kerminen, Veli-Matti; Ehn, Mikael; Kulmala, Markku; Niinemets, Ülo (2021)
    Most trees emit volatile organic compounds (VOCs) continuously throughout their life, but the rate of emission and spectrum of emitted VOCs become substantially altered when the trees experience stress. Despite this, models to predict the emissions of VOCs do not account for perturbations caused by biotic plant stress. Considering that such stresses have generally been forecast to increase in both frequency and severity in the future climate, the neglect of stress-induced plant emissions in models might be one of the key obstacles for realistic climate change predictions, since changes in VOC concentrations are known to greatly influence atmospheric aerosol processes. Thus, we constructed a model to study the impact of biotic plant stresses on new particle formation and growth throughout a full growing season. We simulated the influence on aerosol processes caused by herbivory by the European gypsy moth (Lymantria dispar) and autumnal moth (Epirrita autumnata) feeding on pedunculate oak (Quercus robur) and mountain birch (Betula pubescens var. pumila), respectively, and also fungal infections of pedunculate oak and balsam poplar (Populus balsamifera var. suaveolens) by oak powdery mildew (Erysiphe alphitoides) and poplar rust (Melampsora larici-populina), respectively. Our modelling results indicate that all the investigated plant stresses are capable of substantially perturbing both the number and size of aerosol particles in atmospherically relevant conditions, with increases in the amount of newly formed particles by up to about an order of magnitude and additional daily growth of up to almost 50 nm. We also showed that it can be more important to account for biotic plant stresses in models for local and regional predictions of new particle formation and growth during the time of infestation or infection than significant variations in, e.g. leaf area index and temperature and light conditions, which are currently the main parameters controlling predictions of VOC emissions. Our study thus demonstrates that biotic plant stress can be highly atmospherically relevant. To validate our findings, field measurements are urgently needed to quantify the role of stress emissions in atmospheric aerosol processes and for making integration of biotic plant stress emission responses into numerical models for prediction of atmospheric chemistry and physics, including climate change projection models, possible.
  • Räsänen, Tuomas; Juutinen, Sari; Aurela, Mika; Virtanen, Tarmo (2019)
    Remote sensing based biomass estimates in Arctic areas are usually produced using coarse spatial resolution satellite imagery, which is incapable of capturing the fragmented nature of tundra vegetation communities. We mapped aboveground biomass using field sampling and very high spatial resolution (VHSR) satellite images (QuickBird, WorldView-2 and WorldView-3) in four different Arctic tundra or peatland sites with low vegetation located in Russia, Canada, and Finland. We compared site-specific and cross-site empirical regressions. First, we classified species into plant functional types and estimated biomass using easy, non-destructive field measurements (cover, height). Second, we used the cover/height-based biomass as the response variable and used combinations of single bands and vegetation indices in predicting total biomass. We found that plant functional type biomass could be predicted reasonably well in most cases using cover and height as the explanatory variables (adjusted R-2 0.21-0.92), and there was considerable variation in the model fit when the total biomass was predicted with satellite spectra (adjusted R-2 0.33-0.75). There were dissimilarities between cross-site and site-specific regression estimates in satellite spectra based regressions suggesting that the same regression should be used only in areas with similar kinds of vegetation. We discuss the considerable variation in biomass and plant functional type composition within and between different Arctic landscapes and how well this variation can be reproduced using VHSR satellite images. Overall, the usage of VHSR images creates new possibilities but to utilize them to full potential requires similarly more detailed in-situ data related to biomass inventories and other ecosystem change studies and modelling.
  • Wellmann, Thilo; Andersson, Erik; Knapp, Sonja; Lausch, Angela; Palliwoda, Julia; Priess, Joerg; Scheuer, Sebastian; Haase, Dagmar (2023)
    While held to be a means for climate change adaptation and mitigation, nature-based solutions (NbS) themselves are vulnerable to climate change. To find ways of compensating for this vulnerability we combine a focused literature review on how information technology has been used to strengthen positive social-ecological-technological feedback, with the development of a prototype decision-support tool. Guided by the literature review, the tool integrates recent advances in using globally available remote sensing data to elicit information on functional diversity and ecosystem service provisioning with information on human service demand and population vulnerability. When combined, these variables can inform climate change adaptation strategies grounded in local social-ecological realities. This type of integrated monitoring and packaging information to be actionable have potential to support NbS management and local knowledge building for context-tailored solutions to societal challenges in urban environments.
  • Rautiainen, Miina; Lukes, Petr (2015)
    Boreal forests exhibit strong seasonal dynamics in their reflectance spectra during the short, snow-free growing period. This short communication paper reports an analysis of the seasonality of boreal forest spectra from the end of snowmelt until the time of maximal leaf area. We apply a forest reflectance model (FRT) to estimate the seasonal contribution of understow vegetation to forest reflectance from a time series of three Earth Observing 1 (EO-1) Hyperion images acquired in May, June and July. The reflectance simulations are based on detailed seasonal series of leaf area index and understory spectra measurements carried out in ten stands at the Hyytiala Forestry Field Station in Finland. Our results show that the contribution of understory to boreal forest reflectance is high in the visible domain, but it drops at the red edge and stays relatively low and constant in near infrared (NIR). Throughout the growing season, the contribution of the understory remains approximately the same in the NIR domain, whereas larger changes can be observed in the visible domain. (C) 2015 The Authors. Published by Elsevier Inc.
  • Bi, Jian; Knyazikhin, Yuri; Choi, Sungho; Park, Taejin; Barichivich, Jonathan; Ciais, Philippe; Fu, Rong; Ganguly, Sangram; Hall, Forrest; Hilker, Thomas; Huete, Alfredo; Jones, Matthew; Kimball, John; Lyapustin, Alexei I.; Mõttus, Matti; Nemani, Ramakrishna R.; Piao, Shilong; Poulter, Benjamin; Saleska, Scott R.; Saatchi, Sassan S.; Xu, Liang; Zhou, Liming; Myneni, Ranga B. (2015)
    Resolving the debate surrounding the nature and controls of seasonal variation in the structure and metabolism of Amazonian rainforests is critical to understanding their response to climate change. In situ studies have observed higher photosynthetic and evapotranspiration rates, increased litterfall and leaf flushing during the Sunlight-rich dry season. Satellite data also indicated higher greenness level, a proven surrogate of photosynthetic carbon fixation, and leaf area during the dry season relative to the wet season. Some recent reports suggest that rainforests display no seasonal variations and the previous results were satellite measurement artefacts. Therefore, here we re-examine several years of data from three sensors on two satellites under a range of sun positions and satellite measurement geometries and document robust evidence for a seasonal cycle in structure and greenness of wet equatorial Amazonian rainforests. This seasonal cycle is concordant with independent observations of solar radiation. Weattribute alternative conclusions to an incomplete study of the seasonal cycle, i. e. the dry season only, and to prognostications based on a biased radiative transfer model. Consequently, evidence of dry season greening in geometry corrected satellite data was ignored and the absence of evidence for seasonal variation in lidar data due to noisy and saturated signals was misinterpreted as evidence of the absence of changes during the dry season. Our results, grounded in the physics of radiative transfer, buttress previous reports of dry season increases in leaf flushing, litterfall, photosynthesis and evapotranspiration in well-hydrated Amazonian rainforests.
  • Liang, Xinlian; Kankare, Ville; Hyyppä, Juha; Wang, Yunsheng; Kukko, Antero; Haggren, Henrik; Yu, Xiaowei; Kaartinen, Harri; Jaakkola, Anttoni; Guan, Fengying; Holopainen, Markus; Vastaranta, Mikko (2016)
    Decision making on forest resources relies on the precise information that is collected using inventory. There are many different kinds of forest inventory techniques that can be applied depending on the goal, scale, resources and the required accuracy. Most of the forest inventories are based on field sample. Therefore, the accuracy of the forest inventories depends on the quality and quantity of the field sample. Conventionally, field sample has been measured using simple tools. When map is required, remote sensing materials are needed. Terrestrial laser scanning (TLS) provides a measurement technique that can acquire millimeter-level of detail from the surrounding area, which allows rapid, automatic and periodical estimates of many important forest inventory attributes. It is expected that TLS will be operationally used in forest inventories as soon as the appropriate software becomes available, best practices become known and general knowledge of these findings becomes more wide spread. Meanwhile, mobile laser scanning, personal laser scanning, and image-based point clouds became capable of capturing similar terrestrial point cloud data as TLS. This paper reviews the advances of applying TLS in forest inventories, discusses its properties with reference to other related techniques and discusses the future prospects of this technique. (C) 2016 The Authors. Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). This is an open access article under the CC BY-NC-ND license (
  • Virkkala, Anna-Maria; Virtanen, Tarmo; Lehtonen, Aleksi; Rinne, Janne; Luoto, Miska (2018)
    The Arctic tundra plays an important role in the carbon cycle as it stores 50% of global soil organic carbon reservoirs. The processes (fluxes) regulating these stocks are predicted to change due to direct and indirect effects of climate change. Understanding the current and future carbon balance calls for a summary of the level of knowledge regarding chamber-derived carbon dioxide (CO2) flux studies. Here, we describe progress from recently (2000-2016) published studies of growing-season CO2 flux chamber measurements, namely GPP (gross primary production), ER (ecosystem respiration), and NEE (net ecosystem exchange), in the tundra region. We review the study areas and designs along with the explanatory environmental drivers used. Most of the studies were conducted in Alaska and Fennoscandia, and we stress the need for measuring fluxes in other tundra regions, particularly in more extreme climatic, productivity, and soil conditions. Soil respiration and other greenhouse gas measurements were seldom included in the studies. Although most of the environmental drivers of CO2 fluxes have been relatively well investigated (such as the effect of vegetation type and soil microclimate on fluxes), soil nutrients, other greenhouse gases and disturbance regimes require more research as they might define the future carbon balance. Particular attention should be paid to the effects of shrubification, geomorphology, and other disturbance effects such as fire events, and disease and herbivore outbreaks. An improved conceptual framework and understanding of underlying processes of biosphere-atmosphere CO2 exchange will provide more information on carbon cycling in the tundra.
  • Dengel, S.; Grace, J.; MacArthur, A. (2015)
    We tested the hypothesis that diffuse radiation from cloudy and overcast skies penetrates the canopy more effectively than direct radiation from clear skies. We compared the flux density and spectral properties of direct and diffuse radiation (around solar noon (+/-1 h)) above, within and below a forest stand under sunny, cloudy and overcast conditions in a thinned Sitka spruce (Picea sitchensis (Bong.) Carr.) forest (28 years old, with a leaf area index of approximately 5.2m(2) m(-2)). We recorded vertical profiles of radiation penetration (from 350 to 1050 nm), and we also explored the horizontal pattern of radiation along a 115m transect. We showed that in "clear sky" conditions, the photosynthetically active radiation in the lower parts of the canopy was substantially attenuated, more so than under cloudy and overcast skies. It was particularly depleted in the blue part of the spectrum, but only slightly blue-depleted when the sky was overcast or cloudy. Moreover, the red : far-red ratio under clear skies fell to values less than 0.3 but only to 0.6 under cloudy or overcast skies. Near the ground, the light climate was strongly influenced by the thinning pattern (carried out in accordance with standard forestry management practice).
  • Pfeifer, Marion; Gonsamo, Alemu; Woodgate, William; Cayuela, Luis; Marshall, Andrew R.; Ledo, Alicia; Paine, Timothy C. E.; Marchant, Rob; Burt, Andrew; Calders, Kim; Courtney-Mustaphi, Colin; Cuni-Sanchez, Aida; Deere, Nicolas J.; Denu, Dereje; de Tanago, Jose Gonzalez; Hayward, Robin; Lau, Alvaro; Macia, Manuel J.; Olivier, Pieter I.; Pellikka, Petri; Seki, Hamidu; Shirima, Deo; Trevithick, Rebecca; Wedeux, Beatrice; Wheeler, Charlotte; Munishi, Pantaleo K. T.; Martin, Thomas; Mustari, Abdul; Platts, Philip J. (2018)
    Background: Canopy structure, defined by leaf area index (LAI), fractional vegetation cover (FCover) and fraction of absorbed photosynthetically active radiation (fAPAR), regulates a wide range of forest functions and ecosystem services. Spatially consistent field-measurements of canopy structure are however lacking, particularly for the tropics. Methods: Here, we introduce the Global LAI database: a global dataset of field-based canopy structure measurements spanning tropical forests in four continents (Africa, Asia, Australia and the Americas). We use these measurements to test for climate dependencies within and across continents, and to test for the potential of anthropogenic disturbance and forest protection to modulate those dependences. Results: Using data collected from 887 tropical forest plots, we show that maximum water deficit, defined across the most arid months of the year, is an important predictor of canopy structure, with all three canopy attributes declining significantly with increasing water deficit. Canopy attributes also increase with minimum temperature, and with the protection of forests according to both active (within protected areas) and passive measures (through topography). Once protection and continent effects are accounted for, other anthropogenic measures (e.g. human population) do not improve the model. Conclusions: We conclude that canopy structure in the tropics is primarily a consequence of forest adaptation to the maximum water deficits historically experienced within a given region. Climate change, and in particular changes in drought regimes may thus affect forest structure and function, but forest protection may offer some resilience against this effect.
  • Costa, Vicent Agusti Ribas; Durand, Maxime; Robson, T. Matthew; Porcar-Castell, Albert; Korpela, Ilkka; Atherton, Jon (2022)
    The plant area index (PAI) is a structural trait that succinctly parametrizes the foliage distribution of a canopy and is usually estimated using indirect optical techniques such as digital hemispherical photography. Critically, on-the-ground photographic measurements forgo the vertical variation of canopy structure which regulates the local light environment. Hence new approaches are sought for vertical sampling of traits. We present an uncrewed aircraft system (UAS) spherical photographic method to obtain structural traits throughout the depth of tree canopies. Our method explained 89% of the variation in PAI when compared with ground-based hemispherical photography. When comparing UAS vertical trait profiles with airborne laser scanning data, we found highest agreement in an open birch (Betula pendula/pubescens) canopy. Minor disagreement was found in dense spruce (Picea abies) stands, especially in the lower canopy. Our new method enables easy estimation of the vertical dimension of canopy structural traits in previously inaccessible spaces. The method is affordable and safe and therefore readily usable by plant scientists.
  • Majasalmi, Titta; Rautiainen, Miina; Stenberg, Pauline; Manninen, Terhikki (2015)
    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.
  • Manninen, Terhikki; Jääskeläinen, Emmihenna; Lohila, Annalea; Korkiakoski, Mika; Räsänen, Aleksi; Virtanen, Tarmo; Muhić, Filip; Marttila, Hannu; Ala-Aho, Pertti; Markovaara-Koivisto, Mira; Liwata-Kenttälä, Pauliina; Sutinen, Raimo; Hänninen, Pekka (2022)
    A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination (R-2) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.