A Spatially-Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation

IF 4.6 1区 地球科学 Q2 ENVIRONMENTAL SCIENCES Water Resources Research Pub Date : 2024-11-09 DOI:10.1029/2023wr036162
Shalini Mahanthege, William Kleiber, Karl Rittger, Balaji Rajagopalan, Mary J. Brodzik, Edward Bair
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Abstract

Snowpack in mountainous areas often provides water storage for summer and fall, especially in the Western United States. In situ observations of snow properties in mountainous terrain are limited by cost and effort, impacting both temporal and spatial sampling, while remote sensing estimates provide more complete spacetime coverage. Spatial estimates of fractional snow covered area (fSCA) at 30m are available every 16 days from the series of multispectral scanning instruments on Landsat platforms. Daily estimates at 463m spatial resolution are also available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite. Fusing Landsat and MODIS fSCA images creates high resolution daily spatial estimates of fSCA that are needed for various uses: to support scientists and managers interested in energy and water budgets for water resources and to understand the movement of animals in a changing climate. Here, we propose a new machine learning approach conditioned on MODIS fSCA, as well as a set of physiographic features, and fit to Landsat fSCA over a portion of the Sierra Nevada USA. The predictions are daily 30m fSCA. The approach relies on two stages of spatially-varying models. The first classifies fSCA into three categories and the second yields estimates within (0, 100) percent fSCA. Separate models are applied and fitted within sub-regions of the study domain. Compared with a recently-published machine learning model (Rittger, Krock, et al., 2021), this approach uses spatially local (rather than global) random forests, and improves the classification error of fSCA by 16%, and fractionally-covered pixel estimates by 18%.
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用于估算积雪覆盖面积的空间分布式机器学习方法
山区的积雪通常为夏季和秋季蓄水,尤其是在美国西部。对山区积雪特性的现场观测受到成本和工作量的限制,影响了时间和空间取样,而遥感估算则提供了更完整的时空覆盖。Landsat 平台上的一系列多光谱扫描仪器每 16 天可提供一次 30 米处积雪覆盖面积(fSCA)的空间估计值。Terra 卫星上的中分辨率成像分光仪(MODIS)仪器也可提供 463 米空间分辨率的每日估算值。将大地遥感卫星和中分辨率成像分光仪的 fSCA 图像融合在一起可生成高分辨率的 fSCA 日空间估算值,这些估算值可用于多种用途:为对水资源的能量和水预算感兴趣的科学家和管理人员提供支持,以及了解动物在不断变化的气候中的活动情况。在此,我们提出了一种新的机器学习方法,该方法以 MODIS fSCA 以及一系列地貌特征为条件,并与美国内华达山脉部分地区的 Landsat fSCA 相匹配。预测结果为每日 30m fSCA。该方法依赖于两个阶段的空间变化模型。第一阶段将 fSCA 分为三类,第二阶段得出 fSCA 在(0,100)% 范围内的估计值。在研究领域的子区域内分别应用和拟合不同的模型。与最近发表的机器学习模型(Rittger、Krock 等人,2021 年)相比,这种方法使用了空间局部(而非全局)随机森林,将 fSCA 的分类误差提高了 16%,将分数覆盖像素估计值提高了 18%。
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来源期刊
Water Resources Research
Water Resources Research 环境科学-湖沼学
CiteScore
8.80
自引率
13.00%
发文量
599
审稿时长
3.5 months
期刊介绍: Water Resources Research (WRR) is an interdisciplinary journal that focuses on hydrology and water resources. It publishes original research in the natural and social sciences of water. It emphasizes the role of water in the Earth system, including physical, chemical, biological, and ecological processes in water resources research and management, including social, policy, and public health implications. It encompasses observational, experimental, theoretical, analytical, numerical, and data-driven approaches that advance the science of water and its management. Submissions are evaluated for their novelty, accuracy, significance, and broader implications of the findings.
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