Multi-sensor fusion using random forests for daily fractional snow cover at 30 m

Rittger, Karl; Krock, Mitchell; Kleiber, William; Bair, Edward H.; Brodzik, M. J.; Stephenson, Thomas R.; Rajagopalan, Balaji; Bormann, K. J.; Painter, T. H.

doi:10.1016/j.rse.2021.112608

Cited by 44 publications

(22 citation statements)

References 48 publications

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“…Scholars have conducted in-depth studies on fractional snow cover using several of the sensors mentioned above. These studies can be broadly classified as using the snow index algorithm [13,14], snow/no-snow reflectance interpolation algorithm [15], SCAmod algorithm [16,17], mixed pixel decomposition algorithm [18][19][20][21], and machine learning algorithms [22][23][24][25][26][27]. The snow index algorithm uses NDSI and fractional snow cover to establish a linear relationship.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Fractional Snow Cover over High Mountain Asia Using 1 km and 5 km AVHRR/2 with Simulated Mid-Infrared Reflective Band

et al. 2022

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Accurate long-term snow-covered-area mapping is essential for climate change studies and water resource management. The NOAA AVHRR/2 provides a unique data source for long-term, large-spatial-scale monitoring of snow-covered areas at a daily scale. However, the value of AVHRR/2 in mapping snow-covered areas is limited, due to its lack of a shortwave infrared band for snow/cloud discrimination. We simulated the reflectance in the 3.75 µm mid-infrared band with a radiative transfer model and then developed three fractional-snow-cover retrieval algorithms for AVHRR/2 imagery at 1 km and 5 km resolutions. These algorithms are based on the multiple endmember spectral mixture analysis algorithm (MESMA), snow index (SI) algorithm, and non-snow/snow two endmember model (TEM) algorithm. Evaluation and comparison of these algorithms were performed using 313 scenarios that referenced snow-cover maps from Landsat-5/TM imagery at 30 m resolution. For all the evaluation data, the MESMA algorithm outperformed the other two algorithms, with an overall accuracy of 0.84 (0.85) and an RMSE of 0.23 (0.21) at the 1 km (5 km) scale. Regarding the effect of land cover type, we found that the three AVHRR/2 fractional-snow-cover retrieval algorithms have good accuracy in bare land, grassland, and Himalayan areas; however, the accuracy decreases in forest areas due to the shading of snow by the canopy. Regarding the topographic effect, the accuracy evaluation indices showed a decreasing and then increasing trend as the elevation increased. The accuracy was worst in the 4000–5000 m range, which was due to the severe snow fragmentation in the High Mountain Asia region; the early AVHRR/2 sensors could not effectively monitor the snow cover in this region. In this study, by increasing the number of bands of AVHRR/2 1 km data for fractional-snow-cover retrieval, a good foundation for subsequent long time series kilometre- resolution snow-cover monitoring has been laid.

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Section: Introductionmentioning

confidence: 99%

Retrieval of Fractional Snow Cover over High Mountain Asia Using 1 km and 5 km AVHRR/2 with Simulated Mid-Infrared Reflective Band

et al. 2022

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“…4(d)). The 30 m scale observations lend credence to the value of techniques that can leverage canopy-free observations corresponding to locations at which MODIS 775 cannot obtain canopy-free observations, thus improving our ability to detect various snow properties from space (Rittger et al, 2021b). However, as the canopy cover increases, all available algorithms face challenges when mapping snow; the detection abilities of these algorithms deteriorate rapidly with increasing canopy cover conditions.…”

Section: Landsat Vs Modis Insightsmentioning

confidence: 87%

Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets

Stillinger

Rittger²,

Raleigh³

et al. 2022

Preprint

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Abstract. Snow cover mapping algorithms utilizing multispectral satellite data at various spatial resolutions are available, each treating subpixel variation differently. Past evaluations of snow mapping accuracy typically relied on satellite data collected at a higher spatial resolution than the data in question. However, these optical data cannot characterize snow cover mapping performance under forest canopies or at the meter scale. Here, we use 3 m spatial resolution snow depth maps collected on 116 days by an aerial laser scanner to validate band ratio and spectral mixture snow cover mapping algorithms. Such a comprehensive evaluation of sub-canopy snow mapping performance has not been undertaken previously. The following standard (produced operationally by an agency) products are evaluated: NASA gap-filled Moderate-resolution Imaging Spectroradiometer (MODIS) MOD10A1F, NASA gap-filled Visible Infrared Imaging Radiometer Suite (VIIRS) VNP10A1F, and USGS Landsat 8 Level-3 Fractional Snow Covered Area. Two spectral unmixing approaches are also evaluated: Snow Covered Area and Grain size (SCAG) and Snow Property Inversion from Remote Sensing (SPIReS), both of which are gap-filled MODIS products and are also run on Landsat 8. We assess subpixel snow mapping performance while considering the fractional snow covered area (fSCA), canopy cover, sensor zenith angle, and other variables within six global seasonal snow classes. Metrics are calculated at the pixel and basin scales, including the root-mean-square error (RMSE), bias, and F statistic (a detection measure). The newer MOD10A1F Version 61 and VNP10A1F Version 1 product biases (-7.1 %, -9.5 %) improve significantly when linear equations developed for older products are applied (2.8 %, -2.7 %) to convert band ratios to fSCA. The F statistics are unchanged (94.4 %, 93.1 %) and the VNP10A1F RMSE improves (18.6 % to 15.7 %) while the MOD10A1F RMSE worsens (12.7 % to 13.7 %). Consistent with previous studies, spectral mixture approaches (SCAG, SPIReS) show lower biases (-0.1 %, -0.1 %) and RMSE (12.1 %,12.0 %), with higher F statistics (95.6 %, 96.1 %) relative to the band ratio approaches for MODIS. Landsat 8 products are all spectral mixture methods with low biases (-0.4 to 0.3 %), low RMSE (11.4 to 15.8 %), and high F statistics (97.3 to 99.1 %). Spectral unmixing methods can improve snow cover mapping at the global scale.

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“…These studies have shown that CNN achieves different degrees of accuracy, depending on the precipitation rate and the condition complexity; it has, e.g., lower accuracy in extreme wet conditions [83]. Other studies have shown a higher downscaling accuracy of GeoAI methods by having a spatial component in the model, e.g., spatial RF vs. RF in downscaling daily fractional snow cover [84] and land surface temperature from MODIS data [85][86][87].…”

Section: Hydrological Data Fusion and Geospatial Downscalingmentioning

confidence: 99%

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

Gonzales‐Inca

Calle

Croghan

et al. 2022

Water

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This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.

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Multi-sensor fusion using random forests for daily fractional snow cover at 30 m

Cited by 44 publications

References 48 publications

Retrieval of Fractional Snow Cover over High Mountain Asia Using 1 km and 5 km AVHRR/2 with Simulated Mid-Infrared Reflective Band

Retrieval of Fractional Snow Cover over High Mountain Asia Using 1 km and 5 km AVHRR/2 with Simulated Mid-Infrared Reflective Band

Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

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