DOI: 10.6084/m9.figshare.20044424
title: Datasets of estimating spatiotemporally continuous snow water equivalent from intermittent satellite track observations using machine learning methods

description: <strong>1. Topography and vegetation cover data in the Upper Tuolumne Watershed</strong> dataset: static_variable.mat description: There are four fields in the structure array: elevation, aspect, slope, and fractional vegetation cover. The data are with a spatial resolution of 16 arc-second. <br> Dimension: 5786*1 Dimension 1: the number of pixels in the Upper Tuolumne Watershed. <br> Used to plot Figure 1 and Figure S1 in the paper "Estimating spatiotemporally continuous snow water equivalent from intermittent satellite track observations using machine learning methods" <br> <strong>2. Snow reanalysis data</strong> dataset: snow_reanalysis.mat description: Snow water equivalent (SWE) data from the posterior snow reanalysis dataset from WY1985 to WY2022, on a daily time scale with a spatial resolution of 16 arc-second. The original dataset is available over the whole Western U.S., here we extract the data in the Upper Tuolumne River Basin, California. Developed by Fang et al., (2022). <br> <strong>3. Domain-wide SWE estimates</strong> dataset: estimation_12yrs.mat description: Track to Area (TTA) SWE data transformation using one statistical and four machine learning methods in four driest years, four normal years, and four wettest years from WY2000 to 2019. <br> This dataset is the domain-wide Apil 1st SWE estimation using the four alternative methods in the 12 years with different climate conditions. <br> Dimension of the dataset: 5786*12*4 Dimension 1: the number of pixels in the Upper Tuolumne Watershed. Dimension 2: 12 years. Dimension 3: four TTA methods. <br> <strong>4. Daily time series of domain-wide SWE estimates</strong> dataset: dnn_prediction_*days_gap.mat description: Assuming the temporal interval between two satellite overpasses is 1-, 5-, 10-, 15-, 20-, or 30-day, the daily time series of domain-wide SWE estimation based on the deep neural network (DNN) method for a dry year (WY2015), a normal year (WY2008), and a wet year (WY2017). <br> Dimension of the datasets: 3*5786*366 Dimension 1: three WY years. Dimension 2: the number of pixels in the study area. Dimension 3: days in a water year from Oct 1st. If the year is not a leap year, then the values on day 366 are NANs. <br> Used for the plotting of Figure 6 and Figure 7. <br> <strong>5. Feature sensitivity test</strong> dataset: relative_importance_missing_feature.mat; MAE_feature_uncertainty_*.mat description: (1) Missing feature analysis (Figure 8): 7*3; 7 meteorological variables (precipitation, air pressure, net longwave radiation, net shortwave radiation, air temperature, specific humidity, wind speed). (2) Feature uncertainty analysis (meteorological forcings) (Figure 9). 101*7: biases from -50% to 50% (1% as the interval) for a dry (WY2015), a normal (WY2008), and a wet (WY2017) year. <br> <strong>6. sensitivity to the number of ground tracks</strong> dataset: MAE_sensitivity_tracks.mat description: The accuracy of domain-wide SWE estimates is expected to increase as there are more overpasses of satellites in the study area. However, the satellite costs may also increase with the addition of ground tracks. We carried out a sensitivity test (estimation accuracy to the number of tracks) to explore the preferred number of ground tracks in the Upper Tuolumne Watershed. This dataset is the result of this sensitivity test assuming that the number of ground tracks changes from 1 to 6 in a dry year (WY2015), a normal year (WY2008), and a wet year (WY2017) based on the four "track-to-area" methods. <br> <br> Dimension: 3*6*4: Dimension 1: three years (WY2015, 2008, and 2017) Dimension 2: number of ground tracks: 1-6 Dimension 4: four different track to area (TTA) methods (MVLR, RF, SVM, and DNN in sequence). <br> This dataset was used to plot Figure 11 in the main rext.