Land surface temperature downscaling using random forest regression: primary result and sensitivity analysis

2018 ◽  
Author(s):  
Xin Pan ◽  
Yingbao Yang ◽  
Chen Cao ◽  
Xiaolong Li ◽  
Liangliang Shan ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Random Forest ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Primary Result ◽  
Random Forest Regression ◽  
Temperature Downscaling

scholarly journals MODELLING OF LAND SURFACE TEMPERATURE USING GRAY LEVEL CO-OCCURRENCE MATRIX AND RANDOM FOREST REGRESSION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 23-28
Author(s):  
A. C. Blanco ◽  
J. B. Babaan ◽  
J. E. Escoto ◽  
C. K. Alcantara
Keyword(s):  
Random Forest ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Volume Ratio ◽  
Temporal Variations ◽  
Surface Model ◽  
Gray Level ◽  
Random Forest Regression

Abstract. Modelling of land surface temperature (LST) is conducted to be able to explain the spatial and temporal variations of LST using a set of explanatory variables. LST in a previous study was modelled as a linear function of vegetation cover and built up cover as quantified by the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI), respectively, and other variables, namely, albedo, solar radiation (SR), surface area-volume ratio (SVR), and skyview factor (SVF). SVF requires a digital surface model of sufficient resolution while SVR computation needs 3D volumetric features representing buildings as input. These inputs are typically not readily available. In addition, NDVI and NDBI do not fully describe the spatial variability of vegetation and built-up cover within an LST pixel. In this study, PlanetScope images (3m resolution) were processed to provide soil-adjusted vegetation index (SAVI) and VgNIR Built-up Index (VgNIR-BI) layers. The following gray level co-occurrence matrices (GLCM) were generated from SAVI and VgNIR-BI: Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, and Correlation. Random Forest regression was run for several cases with different combinations of GLCM features and non-GLCM variables. Using GLCM features alone yielded less satisfactory models. However, the use of additional GLCM features in combination with other variables resulted in lower MSE and a slight increase in R2. Considering NDBI, NDVI, SAVI_GLCM_contrast, VgNIR-BI_GLCM_contrast, VgNIR-BI_GLCM_dissimilarity, and SAVI_GLCM_contrast only, the RF model yielded an MSE=1.657 and validation R2=0.822. While this 6-variable model’s performance is slightly less, the need for DSM and 3D building models which are necessary for the generation of SVF and SVR layers is eliminated. Exploratory regression (ER) was also conducted. The best 6-variable ER model (Adj. R2=0.79) consists of SVR, NDBI, NDVI, SAVI_GLCM_second_moment, VgNIR-BI_GLCM_mean, and VgNIR-BI_GLCM_entropy. In comparison, OLS regression using the 6 non-GLCM variables yielded an Adj. R2=0.691. The results of RFR and ER both indicate the value of GLCM features in providing valuable information to the models of LST. LST is best described through a combination of GLCM features describing relatively homogenous areas (i.e., dominant land cover or low-frequency areas) and the more heterogenous areas (i.e., edges or high-frequency areas) and non-GLCM variables.

scholarly journals A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution

2021 ◽  
Vol 13 (11) ◽  
pp. 2211
Author(s):  
Shuo Xu ◽  
Jie Cheng ◽  
Quan Zhang
Keyword(s):  
Random Forest ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Learning Algorithm ◽  
Mainland China ◽  
Fusion Method ◽  
Moisture Condition

Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.

scholarly journals On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest

2022 ◽  
Vol 14 (2) ◽  
pp. 279
Author(s):  
Qiong Wu ◽  
Zhaoyi Li ◽  
Changbao Yang ◽  
Hongqing Li ◽  
Liwei Gong ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Landscape Metrics ◽  
Landscape Pattern ◽  
Land Surface ◽  
Landscape Patterns ◽  
Impervious Surfaces ◽  
Multi Scale

Urbanization processes greatly change urban landscape patterns and the urban thermal environment. Significant multi-scale correlation exists between the land surface temperature (LST) and landscape pattern. Compared with traditional linear regression methods, the regression model based on random forest has the advantages of higher accuracy and better learning ability, and can remove the linear correlation between regression features. Taking Beijing’s metropolitan area as an example, this paper conducted multi-scale relationship analysis between 3D landscape patterns and LST using Pearson Correlation Coefficient (PCC), Multiple Linear Regression and Random Forest Regression (RFR). The results indicated that LST was relatively high in the central area of Beijing, and decreased from the center to the surrounding areas. The interpretation effect of 3D landscape metrics on LST was more obvious than that of the 2D landscape metrics, and 3D landscape diversity and evenness played more important roles than the other metrics in the change of LST. The multi-scale relationship between LST and the landscape pattern was discovered in the fourth ring road of Beijing, the effect of the extent of change on the landscape pattern is greater than that of the grain size change, and the interpretation effect and correlation of landscape metrics on LST increase with the increase in the rectangle size. Impervious surfaces significantly increased the LST, while the impervious surfaces located at low building areas were more likely to increase LST than those located at tall building areas. It seems that increasing the distance between buildings to improve the rate of energy exchange between urban and rural areas can effectively decrease LST. Vegetation and water can effectively reduce LST, but large, clustered and irregularly shaped patches have a better effect on land surface cooling than small and discrete patches. The Coefficients of Rectangle Variation (CORV) power function fitting results of landscape metrics showed that the optimal rectangle size for studying the relationship between the 3D landscape pattern and LST is about 700 m. Our study is useful for future urban planning and provides references to mitigate the daytime urban heat island (UHI) effect.

scholarly journals Lake Phenology of Freeze-Thaw Cycles Using Random Forest: A Case Study of Qinghai Lake

2020 ◽  
Vol 12 (24) ◽  
pp. 4098
Author(s):  
Weixiao Han ◽  
Chunlin Huang ◽  
Hongtao Duan ◽  
Juan Gu ◽  
Jinliang Hou
Keyword(s):  
Surface Water ◽  
Random Forest ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Qinghai Lake ◽  
Water Index ◽  
Freeze Thaw ◽  
Normalized Difference Water Index ◽  
Break Up

Lake phenology is essential for understanding the lake freeze-thaw cycle effects on terrestrial hydrological processes. The Qinghai-Tibetan Plateau (QTP) has the most extensive ice reserve outside of the Arctic and Antarctic poles and is a sensitive indicator of global climate changes. Qinghai Lake, the largest lake in the QTP, plays a critical role in climate change. The freeze-thaw cycles of lakes were studied using daily Moderate Resolution Imaging Spectroradiometer (MODIS) data ranging from 2000–2018 in the Google Earth Engine (GEE) platform. Surface water/ice area, coverage, critical dates, surface water, and ice cover duration were extracted. Random forest (RF) was applied with a classifier accuracy of 0.9965 and a validation accuracy of 0.8072. Compared with six common water indexes (tasseled cap wetness (TCW), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), automated water extraction index (AWEI), water index 2015 (WI2015) and multiband water index (MBWI)) and ice threshold value methods, the critical freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS), and break-up end (BUE) dates were extracted by RF and validated by visual interpretation. The results showed an R2 of 0.99, RMSE of 3.81 days, FUS and BUS overestimations of 2.50 days, and FUE and BUE underestimations of 0.85 days. RF performed well for lake freeze-thaw cycles. From 2000 to 2018, the FUS and FUE dates were delayed by 11.21 and 8.21 days, respectively, and the BUS and BUE dates were 8.59 and 1.26 days early, respectively. Two novel key indicators, namely date of the first negative land surface temperature (DFNLST) and date of the first positive land surface temperature (DFPLST), were proposed to comprehensively delineate lake phenology: DFNLST was approximately 37 days before FUS, and DFPLST was approximately 20 days before BUS, revealing that the first negative and first positive land surface temperatures occur increasingly earlier.

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