Satellite-Based Extension of the Soil Freezing Curve Paradigm: Detecting Extrinsic Freeze/Thaw Thresholds with SMAP in Mid-Latitudinal Agricultural Fields

Abstract. We present a novel method for surface freeze/thaw (F/T) classification based on L-band brightness temperature (TB), as measured by the Soil Moisture Active Passive (SMAP) mission, combined with thermodynamic temperature estimates, whether in situ or derived from near real-time model output. Variations in the cryosphere have significant, lasting impacts on physical, biological, and social systems, and act as sensitive indicators of climate change. Remote sensing at microwave frequencies is uniquely suited for monitoring the cryosphere’s spatial and temporal dynamics. Indeed, SMAP was tasked with providing a daily classification of the surface F/T state as one of two primary mission goals. Although surface F/T events are extrinsically driven phenomena, most existing classification algorithms rely on intrinsic thresholds – those derived from single-variable observables – that may not accurately reflect in situ conditions. Meanwhile, soil physicists have long used a robust framework to study the relationship between unfrozen water content and sub-freezing temperature, known as the soil freezing characteristic curve (SFC). These curves, and to a lesser extent their soil thawing characteristic curve (STC) branches, have been well studied in laboratory settings using a variety of instruments and methods. These concepts have not been extended to remote sensing (RS) until now. The remotely sensed surface freezing characteristic curves (SurFCs) introduced here are the satellite-pixel-scale counterpart to SFCs. SurFCs are constructed with SMAP TB measurements, which are inversely correlated with water content, along with thermodynamic temperature records at two mid-latitude sites. We used in situ temperature data from SMAP core validation sites near Kenaston, Saskatchewan and Carman, Manitoba, covering a combined total of nine years, alongside modelled temperature estimates from the Goddard Earth Observing System Model, Version 5 Forward Processing product (GEOS-5 FP). SurFCs constructed with in situ soil temperatures showed a structure like that of SFCs, including analogue thawing branches, identified as surface thawing characteristic curves (SurTCs). Lastly, we show SurTCs can serve as a tool for identifying extrinsic thresholds – transition points linked to both the system’s physical state and its external drivers – enhancing the realism and operational accuracy of satellite-based F/T classification. Overall, the proposed TBHmin approach improved detection accuracy by 39.4 % compared to the widely used Normalized Polarization Ratio (NPR) method. This analysis challenges the prevailing assumption that 0.15 °C is a universal F/T threshold. Instead, we argue that the threshold should be determined from measurements of the system’s physical response and environmental forcing (SurFC/SurTC). Although useful, a 0.15 °C classifier is not uniformly applicable across freeze–thaw phenomena or measurement methods.

Authors

Institutions

Publication Details

Published
2025-10-10
DOI
https://doi.org/10.5194/egusphere-2025-3630
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Satellite-Based Extension of the Soil Freezing Curve Paradigm: Detecting Extrinsic Freeze/Thaw Thresholds with SMAP in Mid-Latitudinal Agricultural Fields

Erica Tetlock, Andreas Colliander, Renato Pardo Lara, Aaron Berg et al.
Soil Moisture and Remote Sensing
article

Satellite-Based Extension of the Soil Freezing Curve Paradigm: Detecting Extrinsic Freeze/Thaw Thresholds with SMAP in Mid-Latitudinal Agricultural Fields

Erica Tetlock, Andreas Colliander, Renato Pardo Lara, Aaron Berg, Jaison Thomas Ambadan, Jarrett Powers
article en

Abstract

Abstract. We present a novel method for surface freeze/thaw (F/T) classification based on L-band brightness temperature (TB), as measured by the Soil Moisture Active Passive (SMAP) mission, combined with thermodynamic temperature estimates, whether in situ or derived from near real-time model output. Variations in the cryosphere have significant, lasting impacts on physical, biological, and social systems, and act as sensitive indicators of climate change. Remote sensing at microwave frequencies is uniquely suited for monitoring the cryosphere’s spatial and temporal dynamics. Indeed, SMAP was tasked with providing a daily classification of the surface F/T state as one of two primary mission goals. Although surface F/T events are extrinsically driven phenomena, most existing classification algorithms rely on intrinsic thresholds – those derived from single-variable observables – that may not accurately reflect in situ conditions. Meanwhile, soil physicists have long used a robust framework to study the relationship between unfrozen water content and sub-freezing temperature, known as the soil freezing characteristic curve (SFC). These curves, and to a lesser extent their soil thawing characteristic curve (STC) branches, have been well studied in laboratory settings using a variety of instruments and methods. These concepts have not been extended to remote sensing (RS) until now. The remotely sensed surface freezing characteristic curves (SurFCs) introduced here are the satellite-pixel-scale counterpart to SFCs. SurFCs are constructed with SMAP TB measurements, which are inversely correlated with water content, along with thermodynamic temperature records at two mid-latitude sites. We used in situ temperature data from SMAP core validation sites near Kenaston, Saskatchewan and Carman, Manitoba, covering a combined total of nine years, alongside modelled temperature estimates from the Goddard Earth Observing System Model, Version 5 Forward Processing product (GEOS-5 FP). SurFCs constructed with in situ soil temperatures showed a structure like that of SFCs, including analogue thawing branches, identified as surface thawing characteristic curves (SurTCs). Lastly, we show SurTCs can serve as a tool for identifying extrinsic thresholds – transition points linked to both the system’s physical state and its external drivers – enhancing the realism and operational accuracy of satellite-based F/T classification. Overall, the proposed TBHmin approach improved detection accuracy by 39.4 % compared to the widely used Normalized Polarization Ratio (NPR) method. This analysis challenges the prevailing assumption that 0.15 °C is a universal F/T threshold. Instead, we argue that the threshold should be determined from measurements of the system’s physical response and environmental forcing (SurFC/SurTC). Although useful, a 0.15 °C classifier is not uniformly applicable across freeze–thaw phenomena or measurement methods.

Agriculture and Agri-Food Canada (CA), Jet Propulsion Laboratory (US), Environment and Climate Change Canada (CA), University of Guelph (CA)
Openalex Percentile: Top 74%
Soil Moisture and Remote Sensing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.