Coupling numerical simulation with remotely sensed information for the study of frozen soil dynamics

doi:10.3724/SP.J.1226.2020.00404

Sciences in Cold and Arid Regions ›› 2020, Vol. 12 ›› Issue (6): 404–417.doi: 10.3724/SP.J.1226.2020.00404

收稿日期:2020-09-30 接受日期:2020-12-01 出版日期:2020-12-31 发布日期:2021-01-14

Coupling numerical simulation with remotely sensed information for the study of frozen soil dynamics

HuiRan Gao^1,²,WanChang Zhang¹()

^1.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
^2.University of Chinese Academy of Sciences, Beijing 100049, China

Received:2020-09-30 Accepted:2020-12-01 Online:2020-12-31 Published:2021-01-14
Contact: WanChang Zhang E-mail:zhangwc@radi.ac.cn
Supported by:
the National Key R&D Program of(2016YFA0602302)

摘要/Abstract

Abstract:

The acquisition of spatial-temporal information of frozen soil is fundamental for the study of frozen soil dynamics and its feedback to climate change in cold regions. With advancement of remote sensing and better understanding of frozen soil dynamics, discrimination of freeze and thaw status of surface soil based on passive microwave remote sensing and numerical simulation of frozen soil processes under water and heat transfer principles provides valuable means for regional and global frozen soil dynamic monitoring and systematic spatial-temporal responses to global change. However, as an important data source of frozen soil processes, remotely sensed information has not yet been fully utilized in the numerical simulation of frozen soil processes. Although great progress has been made in remote sensing and frozen soil physics, yet few frozen soil research has been done on the application of remotely sensed information in association with the numerical model for frozen soil process studies. In the present study, a distributed numerical model for frozen soil dynamic studies based on coupled water-heat transferring theory in association with remotely sensed frozen soil datasets was developed. In order to reduce the uncertainty of the simulation, the remotely sensed frozen soil information was used to monitor and modify relevant parameters in the process of model simulation. The remotely sensed information and numerically simulated spatial-temporal frozen soil processes were validated by in-situ field observations in cold regions near the town of Naqu on the East-Central Tibetan Plateau. The results suggest that the overall accuracy of the algorithm for discriminating freeze and thaw status of surface soil based on passive microwave remote sensing was more than 95%. These results provided an accurate initial freeze and thaw status of surface soil for coupling and calibrating the numerical model of this study. The numerically simulated frozen soil processes demonstrated good performance of the distributed numerical model based on the coupled water-heat transferring theory. The relatively larger uncertainties of the numerical model were found in alternating periods between freezing and thawing of surface soil. The average accuracy increased by about 5% after integrating remotely sensed information on the surface soil. The simulation accuracy was significantly improved, especially in transition periods between freezing and thawing of the surface soil.

Key words: frozen soil, water-heat coupled model, passive microwave remote sensing, coupling, frozen soil dynamics

. [J]. Sciences in Cold and Arid Regions, 2020, 12(6): 404–417.

HuiRan Gao,WanChang Zhang. Coupling numerical simulation with remotely sensed information for the study of frozen soil dynamics[J]. Sciences in Cold and Arid Regions, 2020, 12(6): 404–417.

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Stations	Accuracy	R²	Stations	Accuracy	R²	Stations	Accuracy	R²
BC02	94.8%	0.89	CD06	94.8%	0.90	MS3545	98.1%	0.89
BC03	95.1%	0.89	MS3475	95.9%	0.76	MS3552	97.8%	0.93
BC04	94.8%	0.78	MS3482	94.5%	0.93	MS3559	97.3%	0.93
BC05	98.4%	0.87	MS3488	95.9%	0.91	MS3576	97.8%	0.96
BC06	97.8%	0.92	MS3494	96.7%	0.93	MS3593	98.1%	0.89
BC07	98.4%	0.86	MS3501	98.6%	0.93	MS3603	99.5%	0.90
BC08	96.7%	0.91	MS3513	96.4%	0.91	MS3614	97.3%	0.86
CD01	91.2%	0.93	MS3518	99.2%	0.90	MS3620	93.4%	0.88
CD02	94.2%	0.88	MS3523	98.1%	0.87	MS3627	92.3%	0.89
CD03	95.6%	0.91	MS3527	97.3%	0.94	MS3633	89.6%	0.85
CD04	94.5%	0.90	MS3533	98.1%	0.92	MSBJ	93.2%	0.89
CD05	97.0%	0.94	MS3538	98.1%	0.92	MSNQR	97.8%	0.94

Station	Before correction		After correction		Station	Before correction		After correction
Station	RMSE	R²	RMSE	R²	Station	RMSE	R²	RMSE	R²
BC02	3.33	0.89	2.85	0.93	MS3513	4.75	0.91	4.41	0.93
BC03	3.13	0.89	2.72	0.93	MS3518	4.20	0.90	3.75	0.93
BC04	5.12	0.78	4.66	0.86	MS3523	3.32	0.87	5.36	0.92
BC05	5.68	0.87	5.72	0.91	MS3527	4.59	0.94	4.31	0.95
BC06	4.25	0.92	3.98	0.94	MS3533	4.48	0.92	4.27	0.94
BC07	4.31	0.86	4.31	0.93	MS3538	3.32	0.92	5.05	0.94
BC08	4.83	0.91	5.54	0.91	MS3545	3.26	0.89	2.71	0.93
CD01	5.02	0.93	5.04	0.93	MS3552	4.53	0.93	4.30	0.94
CD02	3.98	0.88	3.42	0.94	MS3559	4.58	0.93	4.37	0.94
CD03	2.92	0.91	2.46	0.94	MS3576	2.59	0.96	3.64	0.96
CD04	3.50	0.90	3.14	0.94	MS3593	2.98	0.89	2.26	0.94
CD05	5.26	0.94	5.15	0.94	MS3603	4.54	0.90	4.31	0.92
CD06	5.02	0.90	4.88	0.91	MS3614	5.10	0.86	4.62	0.92
MS3475	5.05	0.76	3.27	0.88	MS3620	3.27	0.88	3.14	0.93
MS3482	2.96	0.93	3.62	0.95	MS3627	3.43	0.89	3.01	0.92
MS3488	4.10	0.91	3.73	0.94	MS3633	3.54	0.85	4.71	0.91
MS3494	4.43	0.93	4.18	0.94	MSBJ	4.19	0.89	4.08	0.93
MS3501	4.22	0.93	4.41	0.93	MSNQR	3.00	0.94	2.54	0.96
Average¹	4.08	0.89	3.99	0.93
Average²	3.88	0.90	3.76	0.93
Average³	3.96	0.88	3.85	0.92
Average⁴	4.27	0.87	4.19	0.89

Coupling numerical simulation with remotely sensed information for the study of frozen soil dynamics

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