Testing snow water equivalent retrieval algorithms for passive microwave remote sensing in an alpine watershed of western Canada
نویسندگان
چکیده
Brightness temperatures (TBs) from the special sensor microwave imager (SSM/I) and advanced microwave scanning radiometer (AMSR-E) from 2003 to 2007 are utilized to retrieve and evaluate the snow water equivalent (SWE) over the complex terrain of the Quesnel River Basin (QRB), British Columbia, Canada. Various algorithms including the Environment Canada (EC) algorithms, the spectral polarization difference (SPD) algorithm, and an artificial neural network (ANN) for both SSM/I and AMSR-E are evaluated against in situ SWE observations using several statistical metrics. The results show that the EC algorithms developed specifically for the southern prairies and boreal forest perform poorly across the complex topography and generally deep snow of the QRB. For other frequency combinations of SSM/I and AMSR-E measurements, significant relationships between TB difference and in situ SWE exist only when the snow accumulation is less than a threshold of 250 or 400 mm, which varies at the different in situ stations. Overall, AMSR-E provides better estimates of retrieved SWE than SSM/I. Compared to the algorithms based on TB difference, the ANNs for SSM/I and AMSR-E perform much better. The ANNs trained with all channels of AMSR-E have the best performance in fitting SWE and are able to resolve the temporal variations of SWE at all in situ stations. However, due to the complexity of the topography and vegetation in this mountainous watershed, the ANNs based only on limited in situ stations are not able to retrieve the spatial variations of SWE in this area. Résumé. Les températures de radiance des capteurs « Special Sensor Microwave Imager » (SSM/I) et « Advanced Microwave Scanning Radiometer » (AMSR-E) de 2003 à 2007 sont utilisées pour estimer et évaluer l’épaisseur équivalente en eau (EEE) de la neige dans le bassin versant de la rivière Quesnel, en Colombie-Britannique, Canada. Une variété d’algorithmes, dont ceux d’Environnement Canada (EC), de la différence spectrale polarisée et d’un réseau artificiel de neurones (RAN) pour SSM/I et AMSR-E, sont évalués contre des données prises sur le champ de l’accumulation en neige utilisant plusieurs statistiques. Les résultats démontrent que les algorithmes par EC pour les prairies et les forêts boréales du Canada performent d’une façon insatisfaisante pour la topographie complexe et la neige abondante du bassin versant de la Rivière Quesnel. Pour d’autres combinaisons de données de SSM/I et AMSR-E, des relations significatives entre les températures de radiance et l’accumulation en neige existent seulement lorsque celle-ci ne dépasse pas soit 250 mm EEE ou 400 mm EEE, dépendant de l’endroit. En général, AMSR-E donne de meilleurs estimations de l’accumulation en neige que SSM/I. Comparés aux algorithmes basés sur une différence de température de radiance, le RAN pour SSM/I et AMSR-E performe beaucoup mieux. Les RANs entraı̂nés avec tous les canaux d’AMSR-E obtiennent les meilleurs résultats en extrayant l’accumulation en neige tout en reconstruisant son évolution temporelle aux stations in situ. Cependant, à cause de la complexité du terrain et de la végétation dans ce bassin versant, les RANs basés sur des données limitées à quelques stations in-situ sont incapables de reconstruire les variations spatiales de l’accumulation en neige dans cet endroit.
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