Downscaling Land Surface Temperature from MODIS Dataset with Random Forest Approach over Alpine Vegetated Areas

Bartkowiak, Paulina; Castelli, Mariapina; Notarnicola, Claudia

doi:10.3390/rs11111319

Cited by 58 publications

(36 citation statements)

References 67 publications

(112 reference statements)

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“…These differences in evapotranspiration are consistent with our findings of cooler temperatures in taller forests. Using multispectral sensors, others have found land surface temperature is affect by land cover type and biophysical characteristics, such as elevation and aspect, at moderate to coarse spatial resolutions [91,92]. Our methods were able to detect differences in land surface temperature at moderately high spatial resolution in the same vegetation type, elevation, and aspect, confirming that annual metrics increase the ability of remote sensors to distinguish ecological characteristics in tropical forests.…”

Section: Improving Ch Mappingsupporting

confidence: 75%

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

et al. 2019

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Developing accurate methods to map vegetation structure in tropical forests is essential to protect their biodiversity and improve their carbon stock estimation. We integrated LIDAR (Light Detection and Ranging), multispectral and SAR (Synthetic Aperture Radar) data to improve the prediction and mapping of canopy height (CH) at high spatial resolution (30 m) in tropical forests in South America. We modeled and mapped CH estimated from aircraft LiDAR surveys as a ground reference, using annual metrics derived from multispectral and SAR satellite imagery in a dry forest, a moist forest, and a rainforest of tropical South America. We examined the effect of the three forest types, five regression algorithms, and three predictor groups on the modelling and mapping of CH. Our CH models reached errors ranging from 1.2–3.4 m in the dry forest and 5.1–7.4 m in the rainforest and explained variances from 94–60% in the dry forest and 58–12% in the rainforest. Our best models show higher accuracies than previous works in tropical forests. The average accuracy of the five regression algorithms decreased from dry forests (2.6 m +/− 0.7) to moist (5.7 m +/− 0.4) and rainforests (6.6 m +/− 0.7). Random Forest regressions produced the most accurate models in the three forest types (1.2 m +/− 0.05 in the dry, 4.9 m +/− 0.14 in the moist, and 5.5 m +/− 0.3 the rainforest). Model performance varied considerably across the three predictor groups. Our results are useful for CH spatial prediction when GEDI (Global Ecosystem Dynamics Investigation lidar) data become available.

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Section: Improving Ch Mappingsupporting

confidence: 75%

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

et al. 2019

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“…More machine learning (ML) based downscaling approaches were explored to construct a nonlinear regression model which addresses the nonlinear relationship between LST and the land surface parameters. The current LST downscaling methods based on ML algorithms, containing artificial neural networks (ANN), support vector machine (SVM), and random forests (RF), have displayed higher accuracies in tackling the nonlinear relationship between LST and the land surface parameters [30][31][32][33][34]. Hutengs and Vohland [11] applied RF to downscale 1 km MOD11A1 LST 250 m in a complex terrestrial environment of Jordan River Region in the Eastern Mediterranean.…”

Section: Introductionmentioning

confidence: 99%

Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging

Zhang

Jiang

et al. 2020

Remote Sensing

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Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.

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“…The basic concept of RF is to construct a set of uncorrelated classification and decision regression trees. RF is capable to unite a big number of binary decision trees constructed using bootstrap samples from the training dataset and randomly select a subset of independent variables and dependent variables at each node [35]. The results of RF training turn out to be the voting output for all decision trees.…”

Section: ) Downscaling Lst Using a Random Forest Modelmentioning

confidence: 99%

“…Compared to common statistics-based downscaling LST methods, RF is more efficient in complex regions, especially in urban areas. Bartkowiak et al [35] evaluated three LST downscaling methods in Alpine vegetated areas, RF was found capable of modelling non-linear relationships between LST and variables in a very robust way. It has been proven that RF can improve LST downscaling performance in arid regions (especially in deserts) [40].…”

Section: ) Downscaling Lst Using a Random Forest Modelmentioning

confidence: 99%

A New Approach for Surface Urban Heat Island Monitoring Based on Machine Learning Algorithm and Spatiotemporal Fusion Model

Yao¹,

Chang

Ndayisaba

et al. 2020

IEEE Access

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Land surface temperature (LST) is an important indicator for assessing the surface urban heat island (SUHI) effect. This paper presents a novel approach to derive LST estimates by integrating machine learning algorithm and spatiotemporal fusion model at high spatial and temporal resolution. The spatial resolutions of Landsat TM and Landsat 8 LST data were first downscaled using random forest (RF) algorithm from 120 m and 100 m, respectively, to 30 m. The resultant LST data were fused with MODerate-resolution Imaging Spectroradiometer (MODIS) LST data, by means of the Flexible Spatiotemporal Data Fusion method (FSDAF), in order to generate high spatiotemporal resolution summer daytime LST data covering the center of Chengdu city in China. The proposed new method was used to estimate the spatiotemporal variations of the summer daytime SUHI from 2009 to 2018 over Chengdu city. Results show that: (1) RF performs way better than the classical downscaling algorithm-thermal sharpening algorithm (TsHARP) for LST, and produces higher accuracy for different land covers; (2) the fused high spatiotemporal resolution summer daytime LST values were evaluated with in situ LST obtained from Chengdu Meteorological Office and the final validation results indicated that the proposed method, in generating LST dataset, can provide more details of urban thermal environment and produce higher accuracy than the traditional FSDAF; (3) significantly increasing trends of summer daytime SUHI intensity (SUHII) in the study area were observed. SUHII increased from 2.78 ℃ in 2009 to 4.04 ℃ in 2018. The highest and lowest summer daytime LST estimates were recorded over impervious surface area (ISA) and waters, respectively.

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Downscaling Land Surface Temperature from MODIS Dataset with Random Forest Approach over Alpine Vegetated Areas

Cited by 58 publications

References 67 publications

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging

A New Approach for Surface Urban Heat Island Monitoring Based on Machine Learning Algorithm and Spatiotemporal Fusion Model

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