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Operational Snow Monitoring in the Tibetan Plateau by Using SSM/I Data and Algorithm Validation

机译:使用SSM / I数据和算法验证,在藏高平台中运行雪监测

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The estimation of snow parameters such as snow extent, snow depth and snow water equivalent are very important. They are parameters in land surface schemes and are very useful in snow disaster assessment. Passive microwave remote sensing has advantages in retrieving these parameters, especially snow depth. However, this technique has not been applied to monitor snow in Tibetan Plateau so far. So since last winter we tried to operationally monitor snow in this area by using SSM/I data, providing daily snow depth maps to the concerning sections of local government, hi the meantime, the in-situ measurements of snow depth data in the Tibetan Plateau were collected to validate the retrieval algorithm employed in this study. In the paper, SSM/I images before and after a heavy snowfall were analyzed and compared with MODIS images .The results showed that the snow extent from SSM/I data is consistent with that from MODIS data, and snow depths from SSM/I are helpful for the assessment of snow disaster. However, compared with in-situ observations SSM/I derived snow depths are significantly overestimated. Since passive microwave remote sensing is almost transparently to atmosphere and cloud, it will play an important role in monitoring snow in the Tibetan Plateau, with the retrieval algorithm being improved. This will be more dominant when AMSR data are available.
机译:估计雪参数,如雪程,雪深和雪水等同物非常重要。它们是陆地方案中的参数,在雪灾评估中非常有用。被动微波遥感在检索这些参数方面具有优势,尤其是雪深。然而,到目前为止,这种技术尚未应用于藏高高原的监测雪。因此,自上来冬季以来,我们试图通过使用SSM / I数据在该地区进行雪,为当地政府的一部分提供日常雪气深度地图,嗨,在藏高高原中的雪深度数据的原位测量。收集以验证本研究中采用的检索算法。在论文中,分析了大雪之前和之后的SSM / I图像并与MODIS图像进行比较。结果表明,来自SSM / I数据的雪程与来自MODIS数据的雪程一致,SSM / I的雪地深度是一致的有助于评估雪灾。然而,与原位观察相比,SSM / I衍生的雪深度显着高估。由于被动微波遥感几乎透明地透明地对大气和云,因此它将在藏高高原的雪中发挥重要作用,提高了检索算法。当AMSR数据可用时,这将更占主导地位。

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