Linear unmixing of MODIS albedo composites to infer subpixel land cover type albedos

Kuusinen, Nea; Tomppo, Erkki; Berninger, Frank

doi:10.1016/j.jag.2012.10.005

Cited by 28 publications

(32 citation statements)

References 34 publications

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“…Future studies for improving understanding of biogeophysical impacts on regional climate of peatland forestation and other land cover or land use intensity changes could focus on the following issues: the parameterisation of albedo, the soil hydrology scheme, and the implementation and accuracy of land cover maps. Although the maximum background albedo values of FNFI land cover classes in this study are broadly consistent with the summertime albedo values derived especially for two observation stations in Finland in Kuusinen et al (2013), the estimated albedo for land cover classes in high-latitude areas show variations in a range of studies. The mean summertime albedo for coniferous forest is only 0.079 in Hollinger et al (2010), while it is 0.119 in our study.…”

Section: Perspectives To Improve Land-atmosphere Interactions Modellingsupporting

confidence: 87%

“…The derived and observed albedo values show good agreement for peat bogs, mixed forest, transitional woodland/shrub and agricultural areas, as well as for artificial areas. Although the maximum albedo values of coniferous forest and broad-leaved forest in this study are around 0.01 higher than those in Kuusinen et al (2013), they are reasonable for considering albedo differences between land cover classes. The three land cover classes (natural grasslands, moors and heathland, open spaces) are not found at the two stations; however, they take up only small proportions of the FNFI land cover maps.…”

Section: Appendix B: Modifications In Remo Lss In This Studycontrasting

confidence: 50%

“…It is based on the assumption that the vegetation albedo map and the soil albedo map in current REMO LSS are feasible to describe the albedo values of the land cover condition in FNFI10 because the two data sets are overlapping in time. Therefore, the soil albedo and the vegetation albedo values, (Kuusinen et al, 2013). The derived and observed albedo values show good agreement for peat bogs, mixed forest, transitional woodland/shrub and agricultural areas, as well as for artificial areas.…”

Section: Appendix B: Modifications In Remo Lss In This Studymentioning

confidence: 60%

“…The mean summertime albedo for coniferous forest is only 0.079 in Hollinger et al (2010), while it is 0.119 in our study. We used a summertime albedo for broad-leaved forest of 0.146, which is higher than the albedo values for deciduous in Kuusinen et al (2013) but still lower than 0.156 for aspen in Betts and Ball (1997) and 0.152 for deciduous in Hollinger et al (2010). The cropland albedo is 0.189 in Hollinger et al (2010), much higher than the cropland albedo of 0.156 used in our study.…”

Section: Perspectives To Improve Land-atmosphere Interactions Modellingmentioning

confidence: 99%

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Biogeophysical impacts of peatland forestation on regional climate changes in Finland

et al. 2014

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Abstract. Land cover changes can impact the climate by influencing the surface energy and water balance. Naturally treeless or sparsely treed peatlands were extensively drained to stimulate forest growth in Finland over the second half of 20th century. The aim of this study is to investigate the biogeophysical effects of peatland forestation on regional climate in Finland. Two sets of 18-year climate simulations were done with the regional climate model REMO by using land cover data based on pre-drainage (1920s) and postdrainage (2000s) Finnish national forest inventories. In the most intensive peatland forestation area, located in the middle west of Finland, the results show a warming in April of up to 0.43 K in monthly-averaged daily mean 2 m air temperature, whereas a slight cooling from May to October of less than 0.1 K in general is found. Consequently, snow clearance days over that area are advanced up to 5 days in the mean of 15 years. No clear signal is found for precipitation. Through analysing the simulated temperature and energy balance terms, as well as snow depth over five selected subregions, a positive feedback induced by peatland forestation is found between decreased surface albedo and increased surface air temperature in the snow-melting period. Our modelled results show good qualitative agreements with the observational data. In general, decreased surface albedo in the snow-melting period and increased evapotranspiration in the growing period are the most important biogeophysical aspects induced by peatland forestation that cause changes in climate. The results from this study can be further integrally analysed with biogeochemical effects of peatland forestation to provide background information for adapting future forest management to mitigate climate warming effects. Moreover, they provide insights about the impacts of projected forestation of tundra at high latitudes due to climate change.

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Section: Perspectives To Improve Land-atmosphere Interactions Modellingsupporting

confidence: 87%

Section: Appendix B: Modifications In Remo Lss In This Studycontrasting

confidence: 50%

Section: Appendix B: Modifications In Remo Lss In This Studymentioning

confidence: 60%

Section: Perspectives To Improve Land-atmosphere Interactions Modellingmentioning

confidence: 99%

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Biogeophysical impacts of peatland forestation on regional climate changes in Finland

et al. 2014

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“…Foody, 1996); (3) assisting and validating unmixing approaches to infer subpixel land cover specific biophysical properties (e.g. Kuusinen, Tomppo, & Berninger, 2013); (4) monitoring vegetation phenology (e.g. Zhang et al, 2003) while avoiding mixed pixels; (5) constructing global databases of crop calendars (e.g.…”

Section: Introductionmentioning

confidence: 99%

Exploiting the multi-angularity of the MODIS temporal signal to identify spatially homogeneous vegetation cover: A demonstration for agricultural monitoring applications

Duveiller

López-Lozano

Cescatti

2015

Remote Sensing of Environment

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MODIS has been providing daily imagery for retrieving land surface properties with a spatial resolution 250 m since the year 2000. In many places, this pixel size is closer to that of individual landscape elements, such as managed forest stands or crop fields, than those of time series more typically used in vegetation analyses (with pixel sizes ranging from about 1 km to 8 km). With such spatial resolution, combined with its increasingly long archive, MODIS data offers great potential for vegetation monitoring applications in general, and crop growth monitoring in particular. However, due to its whiskbroom design, the observation geometry of the MODIS instrument, combined with the spatial uncertainty in the registration of the images, can result in different (albeit neighbouring) physical areas being mapped onto the same pixel depending on the view zenith angle (which varies from day to the next). Rather than considering this as an inconvenience, a method is here proposed to exploit this peculiarity to identify pixels corresponding to a homogeneous plant cover, in order to retrieve surface specific time series of satellite products. This method is based on quantifying the temporal signal-to-noise ratio (SNR, hereafter) of the daily MODIS NDVI time series, defined as the variance of smoothed temporal signal over the variance of the residues. If consecutive observations of the same pixel (which have thus sampled the spatial vicinity of that pixel) provide similar NDVI values, the resulting temporal signal is relatively stable and the SNR is high. Such stability can indicate that the signal comes from a spatially homogeneous surface, such as a single large field covered by the same crop with similar agro-management practices. On the contrary, a noisy time series indicates instead a transition zone between different land uses or between fields with different management practices. SNR maps therefore serve as a proxy for sub-pixel homogeneity from which time series originating from a specific land cover or land use can be retrieved. This approach is demonstrated over 12 contrasting agricultural landscapes across the globe from which clearly distinctive crop specific signals are extracted. Exploiting the full MODIS archive to derive surface specific information in this way should open new avenues for regional to global agricultural monitoring applications. Expanding this method to derive satellite products for specific land cover classes could also be useful for many other applications linked to dynamics of land cover and land use change.

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The influence of canopy snow parameterizations on snow albedo feedback in boreal forest regions

2014

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Variation in snow albedo feedback (SAF) among Coupled Model Intercomparison Project phase 5 climate models has been shown to explain much of the variation in projected 21st century warming over Northern Hemisphere land. Prior studies using observations and models have demonstrated both considerable spread in the albedo and a negative bias in the simulated strength of SAF, over snow-covered boreal forests. Boreal evergreen needleleaf forests are capable of intercepting snowfall throughout the winter and consequently exert a significant impact on seasonal surface albedo. Two satellite data products and tower-based observations of albedo are compared with simulations from multiple versions of the Community Climate System Model (CCSM4) to investigate the causes of weak simulated SAF over the boreal forest. The largest bias occurs in April and May, when simulated SAF is one half the strength of SAF in observations. This is traced to two features of the canopy snow parameterizations used in the land model. First, there is no mechanism for the dynamic removal of snow from the canopy when temperatures are below freezing, which results in albedo values in midwinter that are biased high. Second, when temperatures do rise above freezing, all snow on the canopy is melted instantaneously, which results in an unrealistically early transition from a snow-covered to a snow-free canopy. These processes combine to produce large differences between simulated and observed monthly albedo and are the source of the weak bias in SAF. This analysis highlights the importance of canopy snow parameterizations for simulating the hemispheric scale climate response to surface albedo perturbations.

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Linear unmixing of MODIS albedo composites to infer subpixel land cover type albedos

Cited by 28 publications

References 34 publications

Biogeophysical impacts of peatland forestation on regional climate changes in Finland

Biogeophysical impacts of peatland forestation on regional climate changes in Finland

Exploiting the multi-angularity of the MODIS temporal signal to identify spatially homogeneous vegetation cover: A demonstration for agricultural monitoring applications

The influence of canopy snow parameterizations on snow albedo feedback in boreal forest regions

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