Zhang THuang J.-CLei QLiang XLindsey SLuo JZhu A.-XBao WLiu, HongbinHongbinLiu2022-04-252022-04-25202203418162https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114814897&doi=10.1016%2fj.catena.2021.105696&partnerID=40&md5=9e54fe08e02bbaabe07d81dc1c86f43ahttps://scholars.lib.ntu.edu.tw/handle/123456789/606297Soil temperature is a key parameter affecting changes in the balance among energy flow, water flow, nutrient cycling, and ecological stability. In previous research on soil temperature estimation, field measurement has been valuable. However its value is uncertain for regional scale research. A regression equation algorithm approach introduces the environmental factors into consideration. The correlations between soil temperature and environmental covariates may not be linear, and most of them conform to a parabolic relationship. Therefore, based on 53 years' data (1958-2010) from 140 meteorological stations in Australia, this study applied a 3-parameter cosine wave model to fit the monthly air and soil temperature. Further, this study identified the correlations between the parameters and landscape factors using stepwise regression. The root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (NS) were used to evaluate the accuracy of the model. The results showed that: (1) The cosine-wave function can effectively identify the monthly air/soil temperature fluctuation and the prediction accuracy increases gradually from north to south in Australia. (2) The precipitation, horizontal irradiation and elevation are predominant factors that affecting soil temperature fluctuation given a defined air temperature fluctuation. (3) Landscape factors regulate the hysteresis of air/soil temperature fluctuation. The hysteresis of soil temperature response to air temperature in Australia is in order of duration: central > south > north. Therefore, the application of the cosine function is a useful and novel technique for estimation of soil temperature, which could provide better understanding of how soil temperature fluctuation responds to the global warming. ? 2021 Elsevier B.V.Cosine functionFeature analysisGeographic settingModel parametersSoil temperatureabundance estimationaccuracy assessmentair temperaturealgorithmecological stabilityempirical analysisenergy fluxenvironmental factorglobal warmingnutrient cyclingparameterizationregression analysissoil temperaturewater flowAustralia[SDGs]SDG6[SDGs]SDG13[SDGs]SDG14[SDGs]SDG15journal article10.1016/j.catena.2021.1056962-s2.0-85114814897