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| DOI | 10.1016/j.rse.2021.112294 |
| Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression | |
| Kuter S. | |
| 发表日期 | 2021 |
| ISSN | 00344257 |
| 卷号 | 255 |
| 英文摘要 | This study; i) investigates the suitability of two frequently employed machine learning algorithms in remote sensing, namely, random forests (RFs) and support vector regression (SVR) for fractional snow cover (FSC) estimation from MODIS Terra data, and ii) compares them with the previously proposed artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) methods over an heterogeneous and complex alpine terrain. The dataset comprises 20 Landsat 8 – MODIS image pairs that belong to European Alps acquired from Apr 2013 to Dec 2016. The fifteen image pairs are used to generate the training dataset necessary to build the models, whereas the remaining five are employed as a separate test dataset. The reference FSC maps are derived from the binary classified Landsat 8 snow/no snow maps at 30 m resolution. In order to assess the effect of sampling type and sample size, nine different training datasets are generated. The RF and SVR models are trained accordingly by using various settings of model tuning parameters. During the training of the models, MODIS top-of-atmosphere reflectance values of bands 1–7, NDSI, NDVI and land cover class are input as independent variables (i.e., predictors) to estimate the dependent variable (i.e., response), i.e., FSC value. The resolution of the generated FSC maps is 500 m. The results indicate that the ANN, MARS, RF and SVR models exhibit high consistency with reference FSC values as indicated by low RMSE (~0.14) and high R (~0.93) values. In order to analyze the effect of using three auxiliary variables, i.e., NDSI, NDVI and land cover class, to the predictive ability of the models; ANN, MARS, RF and SVR models are also trained without these predictor variables, i.e., by only using MODIS bands 1–7. The models trained without three auxiliary variables slightly differ from the ones trained with the full set of predictors by only resulting in a mean decrease in R <0.012 and a mean increase in RMSE <0.009, showing that they perform well in solving the complex functional dependencies by only using MODIS reflectance data. In terms of computational efficiencies of the proposed algorithms measured by the CPU times spent during model training, MARS and RF algorithms outperform ANN and SVR methods. © 2021 Elsevier Inc. |
| 英文关键词 | Alps; Artificial neural networks; Fractional snow cover mapping; Landsat 8; Machine learning; MODIS Terra; Multivariate adaptive regression splines; Remote sensing of snow; Support vector machines |
| 语种 | 英语 |
| scopus关键词 | Complex networks; Decision trees; Machine learning; Neural networks; Radiometers; Random forests; Reflection; Remote sensing; Snow; Statistical tests; Dependent variables; Fractional snow covers; Functional dependency; Independent variables; Multivariate adaptive regression splines; Predictive abilities; Predictor variables; Support vector regression (SVR); Support vector regression; algorithm; estimation method; land cover; machine learning; MODIS; NDVI; regression analysis; remote sensing; satellite data; satellite imagery; snow cover; support vector machine; Terra (satellite); Alps |
| 来源期刊 | Remote Sensing of Environment
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/178954 |
| 作者单位 | Çankırı Karatekin University, Faculty of Forestry, Department of Forest Engineering, Çankırı, 18200, Turkey |
| 推荐引用方式 GB/T 7714 | Kuter S.. Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression[J],2021,255. |
| APA | Kuter S..(2021).Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression.Remote Sensing of Environment,255. |
| MLA | Kuter S.."Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression".Remote Sensing of Environment 255(2021). |
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