干旱区生态环境知识资源中心(Arid): Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem

Arid

DOI	10.3390/rs13204105
	Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem
	Reyes Rojas, Luis A.; Moletto-Lobos, Italo; Corradini, Fabio; Mattar, Cristian; Fuster, Rodrigo; Escobar-Avaria, Cristian
通讯作者	Mattar, C (corresponding author), Lab Anal Biosphere LAB, Santiago 8820808, Chile.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:20
英文摘要	Evapotranspiration (ET) is key to assess crop water balance and optimize water-use efficiency. To attain sustainability in cropping systems, especially in semi-arid ecosystems, it is necessary to improve methodologies of ET estimation. A method to predict ET is by using land surface temperature (LST) from remote sensing data and applying the Operational Simplified Surface Energy Balance Model (SSEBop). However, to date, LST information from Landsat-8 Thermal Infrared Sensor (TIRS) has a coarser resolution (100 m) and longer revisit time than Sentinel-2, which does not have a thermal infrared sensor, which compromises its use in ET models as SSEBop. Therefore, in the present study we set out to use Sentinel-2 data at a higher spatial-temporal resolution (10 m) to predict ET. Three models were trained using TIRS' images as training data (100 m) and later used to predict LST at 10 m in the western section of the Copiapo Valley (Chile). The models were built on cubist (Cub) and random forest (RF) algorithms, and a sinusoidal model (Sin). The predicted LSTs were compared with three meteorological stations located in olives, vineyards, and pomegranate orchards. RMSE values for the prediction of LST at 10 m were 7.09 K, 3.91 K, and 3.4 K in Cub, RF, and Sin, respectively. ET estimation from LST in spatial-temporal relation showed that RF was the best overall performance (R-2 = 0.710) when contrasted with Landsat, followed by the Sin model (R-2 = 0.707). Nonetheless, the Sin model had the lowest RMSE (0.45 mm d(-1)) and showed the best performance at predicting orchards' ET. In our discussion, we argue that a simplistic sinusoidal model built on NDVI presents advantages over RF and Cub, which are constrained to the spatial relation of predictors at different study areas. Our study shows how it is possible to downscale Landsat-8 TIRS' images from 100 m to 10 m to predict ET.
英文关键词	evapotranspiration surface temperature semi-arid ecosystems remote sensing Landsat-8 Sentinel-2 NDVI
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000714657300001
WOS关键词	LAND-SURFACE TEMPERATURE ; VEGETATION INDEX ; WATER ; DISAGGREGATION ; MANAGEMENT ; PRECIPITATION ; REQUIREMENTS ; VARIABILITY ; ALGORITHM ; CLIMATE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368376
作者单位	[Reyes Rojas, Luis A.; Fuster, Rodrigo; Escobar-Avaria, Cristian] Univ Chile, Lab Terr Anal LAT, Santiago 8820808, Chile; [Moletto-Lobos, Italo; Mattar, Cristian] Lab Anal Biosphere LAB, Santiago 8820808, Chile; [Corradini, Fabio] Inst Invest Agr, INIA Platina, Santiago 8831314, Chile
推荐引用方式 GB/T 7714	Reyes Rojas, Luis A.,Moletto-Lobos, Italo,Corradini, Fabio,et al. Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem[J],2021,13(20).
APA	Reyes Rojas, Luis A.,Moletto-Lobos, Italo,Corradini, Fabio,Mattar, Cristian,Fuster, Rodrigo,&Escobar-Avaria, Cristian.(2021).Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem.REMOTE SENSING,13(20).
MLA	Reyes Rojas, Luis A.,et al."Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem".REMOTE SENSING 13.20(2021).