Browsing by Subject "MODIS"

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  • Liu, Jinxiu; Heiskanen, Janne; Maeda, Eduardo Eiji; Pellikka, Petri K. E. (2018)
    West African savannas are subject to regular fires, which have impacts on vegetation structure, biodiversity and carbon balance. An efficient and accurate mapping of burned area associated with seasonal fires can greatly benefit decision making in land management. Since coarse resolution burned area products cannot meet the accuracy needed for fire management and climate modelling at local scales, the medium resolution Landsat data is a promising alternative for local scale studies. In this study, we developed an algorithm for continuous monitoring of annual burned areas using Landsat time series. The algorithm is based on burned pixel detection using harmonic model fitting with Landsat time series and breakpoint identification in the time series data. This approach was tested in a savanna area in southern Burkina Faso using 281 images acquired between October 2000 and April 2016. An overall accuracy of 79.2% was obtained with balanced omission and commission errors. This represents a significant improvement in comparison with MODIS burned area product (67.6%), which had more omission errors than commission errors, indicating underestimation of the total burned area. By observing the spatial distribution of burned areas, we found that the Landsat based method misclassified cropland and cloud shadows as burned areas due to the similar spectral response, and MODIS burned area product omitted small and fragmented burned areas. The proposed algorithm is flexible and robust against decreased data availability caused by clouds and Landsat 7 missing lines, therefore having a high potential for being applied in other landscapes in future studies.
  • Han, Kyung M.; Jung, Chang H.; Park, Rae-Seol; Park, Soon-Young; Lee, Sojin; Kulmala, Markku; Petäjä, Tuukka; Karasinski, Grzegorz; Sobolewski, Piotr; Yoon, Young Jun; Lee, Bang Young; Kim, Kiyeon; Kim, Hyun S. (2021)
    In this study, more accurate information on the levels of aerosol optical depth (AOD) was calculated from the assimilation of the modeled AOD based on the optimal interpolation method. Additionally, more realistic levels of surface particulate matters over the Arctic were estimated using the assimilated AOD based on the linear relationship between the particulate matters and AODs. In comparison to the MODIS observation, the assimilated AOD was much improved compared with the modeled AOD (e.g., increase in correlation coefficients from -0.15-0.26 to 0.17-0.76 over the Arctic). The newly inferred monthly averages of PM10 and PM2.5 for April-September 2008 were 2.18-3.70 mu g m(-3) and 0.85-1.68 mu g m(-3) over the Arctic, respectively. These corresponded to an increase of 140-180%, compared with the modeled PMs. In comparison to in-situ observation, the inferred PMs showed better performances than those from the simulations, particularly at Hyytiala station. Therefore, combining the model simulation and data assimilation provided more accurate concentrations of AOD, PM10, and PM2.5 than those only calculated from the model simulations.
  • Ulsig, Laura; Nichol, Caroline J.; Huemmrich, Karl F.; Landis, David R.; Middleton, Elizabeth M.; Lyapustin, Alexei I.; Mammarella, Ivan; Levula, Janne; Porcar-Castell, Albert (2017)
    Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI). This study investigates the potential of a Photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS) were derived from a 13-year-long time series of PRI and NDVI based on a MAIAC (multi-angle implementation of atmospheric correction) processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (R-2 = 0.36-0.8), which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (R-2 > 0.6 in all cases). The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology.
  • Sogacheva, L.; Kolmonen, P.; Virtanen, T. H.; Rodriguez, E.; Sundstrom, A. -M.; de Leeuw, G. (2015)
  • Böttcher, Kristin; Markkanen, Tiina; Thum, Tea; Aalto, Tuula; Aurela, Mika; Reick, Christian H.; Kolari, Pasi; Arslan, Ali N.; Pulliainen, Jouni (2016)
    The objective of this study was to assess the performance of the simulated start of the photosynthetically active season by a large-scale biosphere model in boreal forests in Finland with remote sensing observations. The start of season for two forest types, evergreen needle-and deciduous broad-leaf, was obtained for the period 2003-2011 from regional JSBACH (Jena Scheme for Biosphere-Atmosphere Hamburg) runs, driven with climate variables from a regional climate model. The satellite-derived start of season was determined from daily Moderate Resolution Imaging Spectrometer (MODIS) time series of Fractional Snow Cover and the Normalized Difference Water Index by applying methods that were targeted to the two forest types. The accuracy of the satellite-derived start of season in deciduous forest was assessed with bud break observations of birch and a root mean square error of seven days was obtained. The evaluation of JSBACH modelled start of season dates with satellite observations revealed high spatial correspondence. The bias was less than five days for both forest types but showed regional differences that need further consideration. The agreement with satellite observations was slightly better for the evergreen than for the deciduous forest. Nonetheless, comparison with gross primary production (GPP) determined from CO2 flux measurements at two eddy covariance sites in evergreen forest revealed that the JSBACH-simulated GPP was higher in early spring and led to too-early simulated start of season dates. Photosynthetic activity recovers differently in evergreen and deciduous forests. While for the deciduous forest calibration of phenology alone could improve the performance of JSBACH, for the evergreen forest, changes such as seasonality of temperature response, would need to be introduced to the photosynthetic capacity to improve the temporal development of gross primary production.
  • Maeda, Eduardo Eiji; Ma, Xuanlong; Wagner, Fabien Hubert; Kim, Hyungjun; Oki, Taikan; Eamus, Derek; Huete, Alfredo (2017)
    Evapotranspiration (ET) of Amazon forests is a main driver of regional climate patterns and an important indicator of ecosystem functioning. Despite its importance, the seasonal variability of ET over Amazon forests, and its relationship with environmental drivers, is still poorly understood. In this study, we carry out a water balance approach to analyse seasonal patterns in ET and their relationships with water and energy drivers over five sub-basins across the Amazon Basin. We used in situ measurements of river discharge, and remotely sensed estimates of terrestrial water storage, rainfall, and solar radiation. We show that the characteristics of ET seasonality in all sub-basins differ in timing and magnitude. The highest mean annual ET was found in the northern Rio Negro basin (similar to 1497 mm year(-1)) and the lowest values in the Solimoes River basin (similar to 986 mm year(-1)). For the first time in a basin-scale study, using observational data, we show that factors limiting ET vary across climatic gradients in the Amazon, confirming local-scale eddy covariance studies. Both annual mean and seasonality in ET are driven by a combination of energy and water availability, as neither rainfall nor radiation alone could explain patterns in ET. In southern basins, despite seasonal rainfall deficits, deep root water uptake allows increasing rates of ET during the dry season, when radiation is usually higher than in the wet season. We demonstrate contrasting ET seasonality with satellite greenness across Amazon forests, with strong asynchronous relationships in ever-wet watersheds, and positive correlations observed in seasonally dry watersheds. Finally, we compared our results with estimates obtained by two ET models, and we conclude that neither of the two tested models could provide a consistent representation of ET seasonal patterns across the Amazon.
  • Lukes, Petr; Rautiainen, Miina; Manninen, Terhikki; Stenberg, Pauline; Mottus, Matti (2014)
    Land surface albedo is an essential climate variable controlling the planetary radiative energy budget, yet it is still among the main uncertainties of the radiation budget in the current climate modeling. To date, albedo satellite products have not been linked to extensive forest inventory data sets due to the lack of ground reference data. Here, we used comprehensive and detailed maps of forest inventory variables to couple forest structure and MODIS albedo products for both winter and summer conditions. We investigated how the relationships between forest variables and albedo change seasonally and along latitudinal gradients in the forest biomes of Finland between 60° and 70° N. We observed an increase in forest albedo with increasing latitude in winter but not in summer. Also, relationships between forest variables and the black-sky albedo or directional–hemispherical reflectance (DHR) at different latitudes were tighter in winter than in summer, especially for forest biomass. Summer albedo was only weakly correlated with the traditional inventory variables. Our findings suggest that the relationships between forest variables and DHR depend on latitude.
  • Tang, Xuguang; Li, Hengpeng; Desai, Ankur R.; Nagy, Zoltan; Luo, Juhua; Kolb, Thomas E.; Olioso, Albert; Xu, Xibao; Yao, Li; Kutsch, Werner; Pilegaard, Kim; Köstner, Barbara; Ammann, Christof (2014)
  • Abera, Temesgen; Heiskanen, Janne; Pellikka, Petri; Maeda, Eduardo (2020)
    Precipitation extremes have a strong influence on the exchange of energy and water between the land surface and the atmosphere. Although the Horn of Africa has faced recurrent drought and flood events in recent decades, it is still unclear how these events impact energy exchange and surface temperature across different ecosystems. Here, we analyzed the impact of precipitation extremes on spectral albedo (total shortwave, visible, and near-infrared (NIR) broadband albedos), energy balance, and surface temperature in four natural vegetation types: forest, savanna, grassland, and shrubland. We used remotely sensed observations of surface biophysical properties and climate from 2001 to 2016. Our results showed that, in forests and savannas, precipitation extremes led to divergent spectral changes in visible and NIR albedos, which cancelled each other limiting shortwave albedo changes. An exception to this pattern was observed in shrublands and grasslands, where both visible and NIR albedo increased during drought events. Given that shrublands and grasslands occupy a large fraction of the Horn of Africa (52%), our results unveil the importance of these ecosystems in driving the magnitude of shortwave radiative forcing in the region. The average regional shortwave radiative forcing during drought events (-0.64 W m(-2), SD 0.11) was around twice that of the extreme wet events (0.33 W m(-2), SD 0.09). Such shortwave forcing, however, was too small to influence the surface-atmosphere coupling. In contrast, the surface feedback through turbulent flux changes was strong across vegetation types and had a significant (P <0.05) impact on the surface temperature and net radiation anomalies, except in forests. The strongest energy exchange and surface temperature anomalies were observed over grassland and the smallest over forest, which was shown to be resilient to precipitation extremes. These results suggest that land management activities that support forest preservation, afforestation, and reforestation can help to mitigate the impact of drought through their role in modulating energy fluxes and surface temperature anomalies in the region.
  • Mei, L.; Xue, Y.; de Leeuw, G.; Guang, J.; Wang, Y.; Li, Y.; Xu, H.; Yang, L.; Hou, T.; He, X.; Wu, C.; Dong, J.; Chen, Z. (2011)
  • Yan, Yu; Huang, Kaiyue; Shao, Dongdong; Xu, Yingjun; Gu, Wei (2019)
    Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012-2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing-melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring.
  • Peltoniemi, Mikko; Aurela, Mika; Böttcher, Kristin; Kolari, Pasi; Loehr, John; Hokkanen, Tatu; Karhu, Jouni; Linkosalmi, Maiju; Tanis, Cemal Melih; Metsamaki, Sari; Tuovinen, Juha-Pekka; Vesala, Timo; Arslan, Ali Nadir (2018)
    Ecosystems' potential to provide services, e.g. to sequester carbon, is largely driven by the phonological cycle of vegetation. Timing of phenological events is required for understanding and predicting the influence of climate change on ecosystems and to support analyses of ecosystem functioning. Analyses of conventional camera time series mounted near vegetation has been suggested as a means of monitoring phenological events and supporting wider monitoring of phenological cycle of biomes that is frequently done with satellite earth observation (EO). Especially in the boreal biome, sparsely scattered deciduous trees amongst conifer-dominant forests pose a problem for EO techniques as species phenological signal mix, and render EO data difficult to interpret. Therefore, deriving phonological information from on the ground measurements would provide valuable reference data for earth observed phonology products in a larger scale. Keeping this in mind, we established a network of digital cameras for automated monitoring of phenological activity of vegetation in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1-3 cameras. In this study, we used data from 12 sites to investigate how well networked cameras can detect the phenological development of birches (Betula spp.) along a latitudinal gradient. Birches typically appear in small quantities within the dominant species. We tested whether the small, scattered birch image elements allow a reliable extraction of colour indices and the temporal changes therein. We compared automatically derived phenological dates from these birch image elements both to visually determined dates from the same image time series and to independent observations recorded in the phenological monitoring network covering the same region, Automatically extracted season start dates, which were based on the change of green colour fraction in spring, corresponded well with the visually interpreted start of the season, and also to the budburst dates observed in the field. Red colour fraction turned out to be superior to the green colour-based indices in predicting leaf yellowing and fall. The latitudinal gradients derived using automated phenological date extraction corresponded well with the gradients estimated from the phenological field observations. We conclude that small and scattered birch image elements allow reliable extraction of key phonological dates for the season start and end of deciduous species studied here, thus providing important species-specific data for model validation and for explaining the temporal variation in EO phenology products.
  • Mõttus, Matti; Stenberg, Pauline; Rautiainen, Miina (AMERICAN GEOPHYSICAL UNION, 2007)
  • Mõttus, Matti; Stenberg, Pauline; Rautiainen, Miina (AMERICAN GEOPHYSICAL UNION, 2007)
  • Sogacheva, Larisa; Kolmonen, Pekka; Virtanen, Timo H.; Rodriguez, Edith; Saponaro, Giulia; De Leeuw, Gerrit (2017)
    Cloud misclassification is a serious problem in the retrieval of aerosol optical depth (AOD), which might considerably bias the AOD results. On the one hand, residual cloud contamination leads to AOD overestimation, whereas the removal of high-AOD pixels (due to their misclassification as clouds) leads to underestimation. To remove cloudcontaminated areas in AOD retrieved from reflectances measured with the (Advanced) Along Track Scanning Radiometers (ATSR-2 and AATSR), using the ATSR dual-view algorithm (ADV) over land or the ATSR single-view algorithm (ASV) over ocean, a cloud post-processing (CPP) scheme has been developed at the Finnish Meteorological Institute (FMI) as described in Kolmonen et al. (2016). The application of this scheme results in the removal of cloudcontaminated areas, providing spatially smoother AOD maps and favourable comparison with AOD obtained from the ground-based reference measurements from the AERONET sun photometer network. However, closer inspection shows that the CPP also removes areas with elevated AOD not due to cloud contamination, as shown in this paper. We present an improved CPP scheme which better discriminates between cloud-free and cloud-contaminated areas. The CPP thresholds have been further evaluated and adjusted according to the findings. The thresholds for the detection of high-AOD regions (> 60% of the retrieved pixels should be high-AOD (> 0.6) pixels), and cloud contamination criteria for lowAOD regions have been accepted as the default for AOD global post-processing in the improved CPP. Retaining elevated AOD while effectively removing cloud-contaminated pixels affects the resulting global and regional mean AOD values as well as coverage. Effects of the CPP scheme on both spatial and temporal variation for the period 2002-2012 are discussed. With the improved CPP, the AOD coverage increases by 10-15% with respect to the existing scheme. The validation versus AERONET shows an improvement of the correlation coefficient from 0.84 to 0.86 for the global data set for the period 2002-2012. The global aggregated AOD over land for the period 2003-2011 is 0.163 with the improved CPP compared to 0.144 with the existing scheme. The aggregated AOD over ocean and globally (land and ocean together) is 0.164 with the improved CPP scheme (compared to 0.152 and 0.150 with the existing scheme, for ocean and globally respectively). Effects of the improved CPP scheme on the 10-year time series are illustrated and seasonal and temporal changes are discussed. The improved CPP method introduced here is applicable to other aerosol retrieval algorithms. However, the thresholds for detecting the high-AOD regions, which were developed for AATSR, might have to be adjusted to the actual features of the instruments.
  • Abera, Temesgen Alemayehu; Heiskanen, Janne Hermanni; Pellikka, Petri Kauko Emil; Maeda, Eduardo Eiji (2018)
    Climate–vegetation interaction can be perturbed by human activities through deforestation and natural extreme climatic events. These perturbations can affect the energy and water balance, exacerbating heat stress associated with droughts. Such phenomena are particularly relevant in the Horn of Africa, given its economic and social vulnerability to environmental changes. In this paper, we used 16-year time series (2001–2016) of remotely sensed environmental data with the objective of 1) clarifying how rainfall–vegetation interaction affects land surface temperature (LST) seasonality across the Horn of Africa, and 2) evaluating how this interaction affects LST anomalies during forest loss and drought events. Our results showed that vegetation seasonality follows rainfall modality patterns in 81% of the region. On the other hand, seasonality of daytime LST was negatively related to vegetation greenness patterns across ecoregions, and rainfall modality. LST varied more strongly in grasslands and shrublands than over other vegetation classes. Comparison of LST before and after forest loss in three selected areas (two in Ethiopia and one in Kenya) revealed an annual average increase in LST of 0.7 °C, 1.8 °C, and 0.2 °C after climate variability correction, respectively. The average increase in LST was relatively high and consistent during dry months (1.5 °C, 3 °C, and 0.6 °C). As expected, the rainfall anomalies during droughts (2010/2011, 2015, and 2016) were positively correlated with vegetation greenness anomalies. Nonetheless, the degree with which vegetation cover is affected by extreme rainfall events has a strong influence in regulating the impact of droughts on temperature anomalies. This highlights the importance of vegetation resilience and land cover management in regulating the impact of extreme events.
  • Heinilä, Anna Maaria Kirsikka; Salminen, Miia; Metsämäki, Sari; Pellikka, Petri Kauko Emil; Koponen, Sampsa; Pulliainen, Jouni (2019)
    We aim a better understanding of the effect of spring-time snow melt on the remotely sensed scene reflectance by using an extensive amount of optical spectral data obtained from an airborne hyperspectral campaign in Northern Finland. We investigate the behaviour of thin snow reflectance for different land cover types, such as open areas, boreal forests and treeless fells. Our results not only confirm the generally known fact that the reflectance of a melting thin snow layer is considerably lower than that of a thick snow layer, but we also present analyses of the reflectance variation over different land covers and in boreal forests as a function of canopy coverage. According to common knowledge, the highly variating reflectance spectra of partially transparent, most likely also contaminated thin snow pack weakens the performance of snow detection algorithms, in particular in the mapping of Fractional Snow Cover (FSC) during the end of the melting period. The obtained results directly support further development of the SCAmod algorithm for FSC retrieval, and can be likewise applied to develop other algorithms for optical satellite data (e.g. spectral unmixing methods), and to perform accuracy assessments for snow detection algorithms. A useful part of this work is the investigation of the competence of Normalized Difference Snow Index (NDSI) in snow detection in late spring, since it is widely used in snow mapping. We conclude, based on the spectral data analysis, that the NDSI-based snow mapping is more accurate in open areas than in forests. However, at the very end of the snow melting period the behavior of the NDSI becomes more unstable and unpredictable in non-forests with shallow snow, increasing the inaccuracy also in non-forested areas. For instance in peatbogs covered by melting snow layer (snow depth <30 cm) the mean NDSI-0.6 was observed, having coefficient of variation as high as 70%, whereas for deeper snow packs the mean NDSI shows positive values.
  • Atlaskina, K.; Berninger, F.; de Leeuw, G. (2015)
    Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March-May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50 degrees N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56% of variation of albedo in March, 76% in April and 92% in May. Therefore the effects of other parameters were investigated only for areas with 100% SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between -15 and -10 degrees C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100% SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.
  • Pulliainen, Jouni; Salminen, Miia; Heinilä, Kirsikka; Cohen, Juval; Hannula, Henna-Reetta (2014)
    This work aims at the development and validation of a zeroth order radiative transfer (RT) approach to describe the visible band (555 nm) reflectance of conifer-dominated boreal forest for the needs of remote sensing of snow. This is accomplished by applying airborne and mast-borne spectrometer data sets together with high-resolution information on forest canopy characteristics. In case of aerial spectrometer observations, tree characteristics determined from airborne LIDAR observations are applied to quantify the effect of forest canopy on scene reflectance. The results indicate that a simple RT model is feasible to describe extinction and reflectance properties of both homogeneous and heterogeneous forest scenes (corresponding to varying scales of satellite data footprints and varying structures of forest canopies). The obtained results also justify the application of apparent forest canopy transmissivity to describe the influence of forest to reflectance, as is done e.g. in the SCAmod method for the continental scale monitoring of fractional snow cover (FSC) from optical satellite data. Additionally, the feasibility of the zeroth order RT approach is compared with the use of linear mixing model of scene reflectance. Results suggest that the nonlinear RT approach describes the scene reflectance of a snow-covered boreal forest more realistically than the linear mixing model (in case when shadows on tree crowns and surface are not modeled separately, which is a relevant suggestion when considering the use of models for large scale snow mapping applications). (C) 2014 The Authors. Published by Elsevier Inc.
  • Hou, Meiting; Venalainen, Ari K.; Wang, Linping; Pirinen, Pentti; Gao, Yao; Jin, Shaofei; Zhu, Yuxiang; Qin, Fuying; Hu, Yonghong (2020)
    Spring greening in boreal forest ecosystems has been widely linked to increasing temperature, but few studies have attempted to unravel the relative effects of climate variables such as maximum temperature (TMX), minimum temperature (TMN), mean temperature (TMP), precipitation (PRE) and radiation (RAD) on vegetation growth at different stages of growing season. However, clarifying these effects is fundamental to better understand the relationship between vegetation and climate change. This study investigated spatio-temporal divergence in the responses of Finland's boreal forests to climate variables using the plant phenology index (PPI) calculated based on the latest Collection V006 MODIS BRDF-corrected surface reflectance products (MCD43C4) from 2002 to 2018, and identified the dominant climate variables controlling vegetation change during the growing season (May-September) on a monthly basis. Partial least squares (PLS) regression was used to quantify the response of PPI to climate variables and distinguish the separate impacts of different variables. The study results show the dominant effects of temperature on the PPI in May and June, with TMX, TMN and TMP being the most important explanatory variables for the variation of PPI depending on the location, respectively. Meanwhile, drought had an unexpectedly positive impact on vegetation in few areas. More than 50 % of the variation of PPI could be explained by climate variables for 68.5 % of the entire forest area in May and 87.7 % in June, respectively. During July to September, the PPI variance explained by climate and corresponding spatial extent rapidly decreased. Nevertheless, the RAD was found be the most important explanatory variable to July PPI in some areas. In contrast, the PPI in August and September was insensitive to climate in almost all of the regions studied. Our study gives useful insights on quantifying and identifying the relative importance of climate variables to boreal forest, which can be used to predict the possible response of forest under future warming.