Seasonal variation in MODIS LAI for a boreal forest area in Finland

Janne Heiskanen*, Miina Rautiainen, Pauline Stenberg, Matti Mõttus, Veli Heikki Vesanto, Lauri Korhonen, Titta Majasalmi

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

70 Sitaatiot (Scopus)

Abstrakti

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.

AlkuperäiskieliEnglanti
Sivut104-115
Sivumäärä12
JulkaisuRemote Sensing of Environment
Vuosikerta126
DOI - pysyväislinkit
TilaJulkaistu - marraskuuta 2012
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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