Learning Regional Snow Water Equivalent and Snow Height Variations from Sentinel-1 InSAR Acquisitions

Luca Barco; Lorenzo Innocenti; Bianca Bartoli; Claudio Rossi; Edoardo Arnaudo; Paolo Garza

cs.LGSep 28, 2026

Learning Regional Snow Water Equivalent and Snow Height Variations from Sentinel-1 InSAR Acquisitions

Authors: Luca Barco, Lorenzo Innocenti, Bianca Bartoli, Claudio Rossi, Edoardo Arnaudo, Paolo Garza

Organizations: Politecnico di Torino, Dipartimento di Automatica e Informatica, Torino, Italy · Fondazione LINKS, AI Data and Space, Italy

Abstract

Managing water resources in mountainous regions depends heavily on reliable Snow Water Equivalent (SWE) and Snow Height (HS) data, yet these variables remain difficult to track at scale. This study evaluates three machine learning architectures (XGBoost, U-Net and SegFormer) for the joint estimation of SWE and HS variations from Sentinel-1 InSAR data over the Italian Alps, using the IT-SNOW reanalysis as reference. SegFormer achieves the best results on both targets, with an MAE of 10.391 cm for HS and 27.113 mm w.e. for SWE and the lowest variability across initializations. A feature sensitivity analysis shows that including all available features does not guarantee the lowest error, with model- and task-specific sensitivities. Spatial metrics (R2, Pearson's r) separate the three architectures far more clearly than mean error (MAE, RMSE) does, and decomposing the error per window attributes most of it to a systematic offset in the estimated mean variation rather than to the spatial pattern.

Figures & tables

Fig. 1: Example of InSAR Intensity Interferogram over the Area of Interest (Orobie Alps and Adamello)

	HS		SWE
	MAE	RMSE	MAE	RMSE
All Features	$14.429\pm 1.164$	$20.076\pm 2.941$	$31.238\pm 0.682$	$43.366\pm 0.736$
Excluded Feature
Coherence	$12.945\pm 0.641$	$17.080\pm 0.544$	$\mathbf{29.597\pm 1.450}$	$\mathbf{42.322\pm 1.679}$
Intensity	$12.575\pm 0.123$	$16.654\pm 0.098$	$30.474\pm 1.639$	$43.389\pm 1.271$
Phase	$\mathbf{12.374\pm 0.294}$	$\mathbf{16.465\pm 0.392}$	$31.420\pm 1.554$	$43.947\pm 1.217$

TABLE I: Feature sensitivity results for XGBoost

(a) Baseline — all features, multi-target
		HS				SWE
Model	Configuration	MAE	RMSE	R 2	r	MAE	RMSE	R 2	r
XGBoost	–	$14.429\pm 1.164$	$20.076\pm 2.941$	$-2.631\pm 0.697$	$0.002\pm 0.006$	$31.238\pm 0.682$	$43.366\pm 0.736$	$-1.468\pm 0.152$	$0.097\pm 0.019$
U-Net	–	$12.754\pm 0.353$	$18.224\pm 0.580$	$-2.138\pm 0.169$	$0.018\pm 0.028$	$31.635\pm 1.670$	$45.263\pm 1.251$	$-1.698\pm 0.310$	$0.081\pm 0.034$
SegFormer	–	$10.391\pm 0.445$	$14.684\pm 0.210$	$-0.800\pm 0.082$	$0.065\pm 0.035$	$27.113\pm 1.227$	$40.388\pm 1.254$	$-0.667\pm 0.069$	$0.139\pm 0.004$
(b) Multi-target vs. single-target at matched feature sets

TABLE IV: Spatial metrics for the three architectures and comparison between multi-target and single-target training. Block (a) uses the complete feature set; block (b) uses the configuration with the lowest error in Tables III and III .

Fig. 2: Qualitative results for the InSAR pair 2023/01/11–2023/01/23, all-features configuration, seed 2024. First row (HS): ground truth, XGBoost, U-Net and SegFormer predictions; second row: the same sequence for SWE. Colour scales are symmetric (blue-green: accumulation, brown: ablation); Metrics refer to this pair only and can differ considerably from the period averages in Table IV .

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Extending Dynamic World Surface Water Mapping to Sentinel-1 with AlphaEarth Embeddings

Rohit Mukherjee, Frederick Policelli, Beth Tellman, TC Chakraborty+3

Pacific Northwest National Laboratory, Richland, WA 99354 USA

Sentinel-2

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Dynamic World (DW) maps land use and land cover globally at 10 m from Sentinel-2 (S2) imagery, but only for cloud-free observations, which limits where and when surface water can be mapped. We use the DW water class as weak supervision for a Sentinel-1 (S1) synthetic aperture radar (SAR) model so that DW-like water maps can be produced for every S1 acquisition. Google's AlphaEarth Foundations (AEF) annual embedding supplies spatial context, while S1 backscatter supplies the acquisition-time observation. On 53 globally distributed scenes with independent annotations of 3 m PlanetScope imagery acquired within 48 h of the S1 overpass, the S1-only model already reaches a pooled water intersection over union (IoU) of 0.77, comparable to 0.75 for the operational OPERA DSWx-S1 product, and adding AEF raises it to 0.85. The fused model improves on the S1-only model on 44 of 53 scenes and exceeds OPERA on 48, and on the independent S1S2-Water benchmark it reaches 0.94, compared with 0.87 for OPERA. Optical land-cover products can thus provide scalable training labels for SAR surface water mapping.

Abstract

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