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Characteristics of taiga and tundra snowpack in development and validation of remote sensing of snow / Henna-Reetta Hannula (2022)  

Public Titre : Characteristics of taiga and tundra snowpack in development and validation of remote sensing of snow Type de document : Thèse/HDR Auteurs : Henna-Reetta Hannula, Auteur Editeur : Helsinki [Finland] : University of Helsinki Année de publication : 2022 Importance : 79 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-952-336-153-9 Note générale : Bibliographie Academic dissertation, Faculty of Science, University of Helsinki Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse des mélanges spectraux [Termes IGN] carte thématique [Termes IGN] changement climatique [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] distribution spatiale [Termes IGN] données spatiotemporelles [Termes IGN] échantillonnage de données [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] image infrarouge [Termes IGN] manteau neigeux [Termes IGN] problème inverse [Termes IGN] réflectance spectrale [Termes IGN] taïga [Termes IGN] toundra Résumé : (auteur) Remote sensing of snow is a method to measure snow cover characteristics without direct physical contact with the target from airborne or space-borne platforms. Reliable estimates of snow cover extent and snow properties are vital for several applications including climate change research and weather and hydrological forecasting. Optical remote sensing methods detect the extent of snow cover based on its high reflectivity compared to other natural surfaces. A universal challenge for snow cover mapping is the high spatiotemporal variability of snow properties and heterogeneous landscapes such as the boreal forest biome. The optical satellite sensor’s footprint may extend from tens of meters to a kilometer; the signal measured by the sensor can simultaneously emerge from several target categories within individual satellite pixels. By use of spectral unmixing or inverse model-based methods, the fractional snow cover (FSC) within the satellite image pixel can be resolved from the recorded electromagnetic signal. However, these algorithms require knowledge of the spectral reflectance properties of the targets present within the satellite scene and the accuracy of snow cover maps is dependent on the feasibility of these spectral model parameters. On the other hand, abrupt changes in land cover types with large differences in their snow properties may be located within a single satellite image pixel and complicate the interpretation of the observations. Ground-based in-situ observations can be used to validate the snow parameters derived by indirect methods, but these data are affected by the chosen sampling. This doctoral thesis analyses laboratory-based spectral reflectance information on several boreal snow types for the purpose of the more accurate reflectance representation of snow in mapping method used for the detection of fractional snow cover. Multi-scale reflectance observations representing boreal spectral endmembers typically used in optical mapping of snow cover, are exploited in the thesis. In addition, to support the interpretation of remote sensing observations in boreal and tundra environments, extensive in-situ dataset of snow depth, snow water equivalent and snow density are exploited to characterize the snow variability and to assess the uncertainty and representativeness of these point-wise snow measurements applied for the validation of remote sensing observations. The overall goal is to advance knowledge about the spectral endmembers present in boreal landscape to improve the accuracy of the FSC estimates derived from the remote sensing observations and support better interpretation and validation of remote sensing observations over these heterogeneous landscapes. The main outcome from the work is that laboratory-controlled experiments that exclude disturbing factors present in field circumstances may provide more accurate representation of wet (melting) snow endmember reflectance for the FSC mapping method. The behavior of snow band reflectance is found to be insensitive to width and location differences between visible satellite sensor bands utilized in optical snow cover mapping which facilitates the use of various sensors for the construction of historical data records. The results also reveal the high deviation of snow reflectance due to heterogeneity in snow macro- and microstructural properties. The quantitative statistics of bulk snow properties show that areal averages derived from in-situ measurements and used to validate remote sensing observations are dependent on the measurement spacing and sample size especially over land covers with high absolute snow depth variability, such as barren lands in tundra. Applying similar sampling protocol (sample spacing and sample size) over boreal and tundra land cover types that represent very different snow characteristics will yield to non-equal representativeness of the areal mean values. The extensive datasets collected for this work demonstrate that observations measured at various scales can provide different view angle to the same challenge but at the same time any dataset individually cannot provide a full understanding of the target complexity. This work and the collected datasets directly facilitate further investigation of uncertainty in fractional snow cover maps retrieved by optical remote sensing and the interpretation of satellite observations in boreal and tundra landscapes. Note de contenu : 1. Introduction 2. Snow and its properties 3. Multispectral optical remote sensing of snow 4. Study site, datasets and methods 5. Results and discussion 6. Conclusions and future work Numéro de notice : 24060 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Sciences : University of Helsinki : 2022 DOI : 10.35614/isbn.9789523361522 En ligne : https://doi.org/10.35614/isbn.9789523361522 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101997