An Improved Algorithm for Discriminating Soil Freezing and Thawing Using AMSR-E and AMSR2 Soil Moisture Products

Abstract

Discriminating between surface soil freeze/thaw states with the use of passive microwave brightness temperature has been an effective approach so far. However, soil moisture has a direct impact on the brightness temperature of passive microwave remote sensing, which may result in uncertainties in the widely used dual-index algorithm (DIA). In this study, an improved algorithm is proposed to identify the surface soil freeze/thaw states based on the original DIA in association with the AMSR-E and AMSR2 soil moisture products to avoid the impact of soil moisture on the brightness temperature derived from passive microwave remotely-sensed soil moisture products. The local variance of soil moisture (LVSM) with a 25-day interval was introduced into this algorithm as an effective indicator for selecting a threshold to update and modify the original DIA to identify surface soil freeze/thaw states. The improved algorithm was validated against in-situ observations of the Soil Moisture/Temperature Monitoring Network (SMTMN). The results suggest that the temporal and spatial variation characteristics of LVSM can significantly discriminate between surface soil freeze/thaw states. The overall discrimination accuracy of the improved algorithm was approximately 89% over a remote area near the town of Naqu on the East-Central Tibetan Plateau, which demonstrated an obvious improvement compared with the accuracy of 82% derived with the original DIA. More importantly, the correct classification rate for the modified pixels was over 96%.

Spatiotemporal green water dynamics and their responses to variations of climatic and underlying surface factors: A case study in the Sanjiang Plain, China

“…In this study, ESSI-3 model simulations were conducted during the period 1982-2018, with the first three years serving as a warmup period (the simulation results for the period of 1982-1984 is not considered in the following analysis) for balancing main hydrological component conditions such as soil and groundwater aquifers, and the remaining years being used for calibration and validation. Since the driving data input to the model had multiple spatial resolutions, the bilinear interpolation approach was used to generate grid datasets with a consistent spatial resolution (1 km) (Gao et al, 2018;Wang et al, 2022).…”

“…SMS is a key state variable of the regional hydrological cycle. However, it is difficult to assess the accuracy of simulated SMS based on in-situ measurements owing to the scarcity of gauged stations (Gao et al, 2018). Thus, the terrestrial water storage (TWS) simulated in ESSI-3 model was evaluated as an alternative solution for SMS assessment.…”

Remote Sensing Monitoring and Numerical Simulation Coupling Studies on Frozen Soil in Cold Regions

“…The DIA proposed by Zuerndorfer, et al (1990) involved two brightness temperature parameters for the discrimination of ground surface soil states, was improved by using LVSM by Gao et al (2018). The improved algorithm can be summarized in Equations ( 1), ( 2) and (3).…”

Evaluating the sensitivity and influential factors of freeze-thaw erosion in Tibet, China

“…Freeze-thaw caused by frequent changes of temperature is one of the most crucial characteristics of the land surface in high latitudes, which leads to the change of water volume in soil parent material pores or rock cracks (Gao et al, 2018). One of the serious hazards caused by Freeze-thaw changes is Freeze-thaw erosion.…”