Traditional optical remote sensing data have been widely used for snow cover detection and monitoring. However, they are limited to daytime detection and often suffer from large data gaps due to frequent cloud obscuration. This is in particular a serious challenge for high-latitude and polar regions where long nights prevail during the winter. Nighttime light sensors have a strong capability of sensing the low-level reflected moonlight. They potentially provide a new way to detect snow cover. In this study, we quantitatively analyzed the moonlight intensity for snow detection and developed a Minimum Error Thresholding (MET) algorithm to detect snow cover from the data collected by Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (S-NPP VIIRS) satellite data. For the two case study sites, Abisko in the sub-Arctic zone and the Tibetan Plateau, our analysis results suggest that the moonlight provides sufficient illumination to map snow cover for approximately 10 days in a lunar month. Our nighttime snow cover detection method was quantitatively evaluated by comparing our S-NPP VIIRS DNB snow cover estimates with in situ station observations, Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover products, and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products over Abisko region and the Tibetan Plateau during the 2017–2018 snow season. The overall accuracy of S-NPP VIIRS snow cover estimates was approximately 80.3% in Abisko region and 76.7% in the Tibetan Plateau. The data gaps in our S-NPP VIIRS DNB snow cover estimates were smaller than those of the MODIS snow cover products by 22.1% and 5.1% over Abisko region and the Tibetan Plateau, respectively. Further, we found that nearly 92.8% and 74.6% of data gaps in the MODIS snow-cover product can be filled up by incorporating our S-NPP VIIRS DNB snow cover estimates in Abisko region and the Tibetan Plateau. The total accuracy of daily MODIS snow cover products can be improved to 91.0% in the Tibetan Plateau. Our results indicate that S-NPP VIIRS DNB nighttime satellite data can provide reliable snow products over polar regions and mid-latitude mountainous areas, which is complementary to the standard MODIS snow cover products.

传统的光学遥感数据已广泛用于积雪检测和监测。然而，它们仅限于白天检测，并且由于频繁的云遮挡而经常遭受大的数据缺口。这对于冬季长夜盛行的高纬度和极地地区尤其是一个严峻的挑战。夜间光传感器具有很强的感应低水平反射月光的能力。它们可能提供一种检测积雪的新方法。在这项研究中，我们定量分析了用于雪检测的月光强度，并开发了一种最小误差阈值 (MET) 算法，用于从 Suomi 国家极地轨道伙伴关系可见红外成像辐射计套件 (S-NPP VIIRS) 卫星收集的数据中检测积雪数据。对于两个案例研究站点，在亚北极地区和青藏高原的阿比斯库，我们的分析结果表明，月光提供了足够的照明来绘制一个农历月份大约 10 天的积雪图。通过比较我们的 S-NPP VIIRS DNB 积雪估计与我们的夜间积雪检测方法进行了定量评估就地2017-2018 年雪季期间阿比斯库地区和青藏高原的台站观测、交互式多传感器冰雪测绘系统 (IMS) 积雪产品和中分辨率成像光谱仪 (MODIS) 积雪产品。S-NPP VIIRS 积雪估计的总体准确度在阿比斯库地区约为 80.3%，在青藏高原约为 76.7%。我们的 S-NPP VIIRS DNB 积雪估计数据差距比 MODIS 积雪产品在阿比斯库地区和青藏高原分别小 22.1% 和 5.1%。此外，我们发现通过结合我们在阿比斯库地区和青藏高原的 S-NPP VIIRS DNB 积雪估计，可以填补 MODIS 积雪产品中近 92.8% 和 74.6% 的数据空白。青藏高原每日MODIS积雪产品总准确度可提高至91.0%。我们的研究结果表明，S-NPP VIIRS DNB 夜间卫星数据可以在极地和中纬度山区提供可靠的积雪产品，是对标准 MODIS 积雪产品的补充。