SnowWarp: An open science and open data tool for daily monitoring of snow dynamics

Laurin, Gaia Vaglio; Francini, Saverio; Penna, Daniele; Zuecco, Giulia; Chirici, Gherardo; Berman, Ethan E.; Coops, Nicholas C.; Castelli, Giulio; Bresci, Elena; Preti, Federico; Valentini, Riccardo

doi:10.1016/j.envsoft.2022.105477

Cited by 3 publications

(1 citation statement)

References 46 publications

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“…The open accessibility of data and cloud computing platforms that enable the calculation of sophisticated information globally [ 24 , 25 , 26 ] characterize the current era of remote sensing. To improve the reliability of remote sensing products and exploit them in order to provide statistically rigorous estimates for large-scale or country reporting, huge efforts have been developed in recent years [ 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Per-Pixel Forest Attribute Mapping and Error Estimation: The Google Earth Engine and R dataDriven Tool

Francini,

Marcelli,

Chirici

et al. 2024

Sensors

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Remote sensing products are typically assessed using a single accuracy estimate for the entire map, despite significant variations in accuracy across different map areas or classes. Estimating per-pixel uncertainty is a major challenge for enhancing the usability and potential of remote sensing products. This paper introduces the dataDriven open access tool, a novel statistical design-based approach that specifically addresses this issue by estimating per-pixel uncertainty through a bootstrap resampling procedure. Leveraging Sentinel-2 remote sensing data as auxiliary information, the capabilities of the Google Earth Engine cloud computing platform, and the R programming language, dataDriven can be applied in any world region and variables of interest. In this study, the dataDriven tool was tested in the Rincine forest estate study area—eastern Tuscany, Italy—focusing on volume density as the variable of interest. The average volume density was 0.042, corresponding to 420 m3 per hectare. The estimated pixel errors ranged between 93 m3 and 979 m3 per hectare and were 285 m3 per hectare on average. The ability to produce error estimates for each pixel in the map is a novel aspect in the context of the current advances in remote sensing and forest monitoring and assessment. It constitutes a significant support in forest management applications and also a powerful communication tool since it informs users about areas where map estimates are unreliable, at the same time highlighting the areas where the information provided via the map is more trustworthy. In light of this, the dataDriven tool aims to support researchers and practitioners in the spatially exhaustive use of remote sensing-derived products and map validation.

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Section: Introductionmentioning

confidence: 99%

Per-Pixel Forest Attribute Mapping and Error Estimation: The Google Earth Engine and R dataDriven Tool

Francini,

Marcelli,

Chirici

et al. 2024

Sensors

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An assessment approach for pixel-based image composites

Francini

Hermosilla

Coops

et al. 2023

ISPRS Journal of Photogrammetry and Remote Sensing

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Snow Depth Estimation and Spatial and Temporal Variation Analysis in Tuha Region Based on Multi-Source Data

Yang,

He,

Luo

et al. 2024

Sustainability

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In the modelling of hydrological processes on a regional scale, remote-sensing snow depth products with a high spatial and temporal resolution are essential for climate change studies and for scientific decision-making by management. The existing snow depth products have low spatial resolution and are mostly applicable to large-scale studies; however, they are insufficiently accurate for the estimation of snow depth on a regional scale, especially in shallow snow areas and mountainous regions. In this study, we coupled SSM/I, SSMIS, and AMSR2 passive microwave brightness temperature data and MODIS, TM, and Landsat 8 OLI fractional snow cover area (fSCA) data, based on Python, with 30 m spatially resolved fractional snow cover area (fSCA) data obtained by the spatio-temporal dynamic warping algorithm to invert the low-resolution passive microwave snow depths, and we developed a spatially downscaled snow depth inversion method suitable for the Turpan–Hami region. However, due to the long data-processing time and the insufficient arithmetical power of the hardware, this study had to set the spatial resolution of the result output to 250 m. As a result, a day-by-day 250 m spatial resolution snow depth dataset for 20 hydrological years (1 August 2000–31 July 2020) was generated, and the accuracy was evaluated using the measured snow depth data from the meteorological stations, with the results of r = 0.836 (p ≤ 0.01), MAE = 1.496 cm, and RMSE = 2.597 cm, which are relatively reliable and more applicable to the Turpan–Hami area. Based on the spatially downscaled snow depth data produced, this study found that the snow in the Turpan–Hami area is mainly distributed in the northern part of Turpan (Bogda Mountain), the northwestern part of Hami (Barkun Autonomous Prefecture), and the central part of the area (North Tianshan Mountain, Barkun Mountain, and Harlik Mountain). The average annual snow depth in the Turpan–Hami area is only 0.89 cm, and the average annual snow depth increases with elevation, in line with the obvious law of vertical progression. The annual mean snow depth in the Turpan–Hami area showed a “fluctuating decreasing” trend with a rate of 0.01 cm·a−1 over the 20 hydrological years in the Turpan–Hami area. Overall, the spatially downscaled snow depth inversion algorithm developed in this study not only solves the problem of coarse spatial resolution of microwave brightness temperature data and the difficulty of obtaining accurate shallow snow depth but also solves the problem of estimating the shallow snow depth on a regional scale, which is of great significance for gaining a further understanding of the snow accumulation information in the Tuha region and for promoting the investigation and management of water resources in arid zones.

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SnowWarp: An open science and open data tool for daily monitoring of snow dynamics

Cited by 3 publications

References 46 publications

Per-Pixel Forest Attribute Mapping and Error Estimation: The Google Earth Engine and R dataDriven Tool

Per-Pixel Forest Attribute Mapping and Error Estimation: The Google Earth Engine and R dataDriven Tool

An assessment approach for pixel-based image composites

Snow Depth Estimation and Spatial and Temporal Variation Analysis in Tuha Region Based on Multi-Source Data

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