Spatiotemporal estimation of air temperature patterns at the street level using high resolution satellite imagery

Pelta, Ran; Chudnovsky, Alexandra

doi:10.1016/j.scitotenv.2016.11.042

Cited by 30 publications

(14 citation statements)

References 35 publications

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“…6d) poor. This is also consistent with some previous studies (Benali et al, 2012;Pelta and Chudnovsky, 2017). Besides, the stations located in southern China achieve better performance than northwestern stations in this study.…”

Section: Spatial Evaluation Of Model Performancesupporting

confidence: 93%

Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data

Shen

Jiang

et al. 2020

Remote Sensing of Environment

117

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Air temperature (Ta) is an essential climatological component that controls and influences various earth surface processes. In this study, we make the first attempt to employ deep learning for Ta mapping mainly based on space remote sensing and ground station observations. Considering that Ta varies greatly in space and time and is sensitive to many factors, assimilation data and socioeconomic data are also included for a multi-source data fusion based estimation. Specifically, a 5-layers structured deep belief network (DBN) is employed to better capture the complicated and non-linear relationships between Ta and different predictor variables. Layer-wise pre-training process for essential features extraction and fine-tuning process for weight parameters optimization ensure the robust prediction of Ta spatio-temporal distribution. The DBN model was implemented for 0.01° daily maximum Ta mapping across China. The ten-fold cross-validation results indicate that the DBN model achieves promising results with the RMSE of 1.996°C, MAE of 1.539°C, and R of 0.986 at the national scale. Compared with multiple linear regression (MLR), back-propagation neural 2 network (BPNN) and random forest (RF) method, the DBN model reduces the MAE values by 1.340°C , 0.387°C and 0.222°C , respectively. Further analysis on spatial distribution and temporal tendency of prediction errors both validate the great potentials of DBN in Ta estimation.

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Section: Spatial Evaluation Of Model Performancesupporting

confidence: 93%

Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data

Shen

Jiang

et al. 2020

Remote Sensing of Environment

117

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“…Remote sensing data play an important role in many fields, such as air temperature estimation [9], land cover monitoring [10,11], fire incidence assessment [12], drought prediction [13], and monitoring of crop distribution [14,15]. However, due to technological and budget constraints, space-borne sensors must compromise among spatial resolution, temporal resolution, and repeat periods [16], and data from any single sensor cannot fully reflect the spatiotemporal crop patterns [17,18], which generally presents a complex spatiotemporal heterogeneity [19].…”

Section: Introductionmentioning

confidence: 99%

The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy

Sun

Chen

et al. 2019

IJGI

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Remote sensing data with high spatial and temporal resolutions can help to improve the accuracy of the estimation of crop planting acreage, and contribute to the formulation and management of agricultural policies. Therefore, it is important to determine whether multisource sensors can obtain high spatial and temporal resolution remote sensing data for the target sensor with the help of the spatiotemporal fusion method. In this study, we employed three different sensor datasets to obtain one normalized difference vegetation index (NDVI) time series dataset with a 5.8-m spatial resolution using a spatial and temporal adaptive reflectance fusion model (STARFM). We studied the effectiveness of using multisource remote sensing data to extract crop classifications and analyzed whether the increase in the NDVI time series density could significantly improve the accuracy of the crop classification. The results indicated that multisource sensor data could be used for crop classification after spatiotemporal fusion and that the data source was not limited by the sensor platform. With the increase in the number of NDVI phases, the classification accuracy of the support vector machine (SVM) and the random forest (RF) classifier gradually improved. If the added NDVI phases were not in the optimal time period for wheat recognition, the classification accuracy was not greatly improved. Under the same conditions, the classification accuracy of the RF classifier was higher than that of the SVM. In addition, this study can serve as a good reference for the selection of the optimal time range for base image pairs in the spatiotemporal fusion method for high accuracy mapping of crops, and help avoid excessive data collection and processing.

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“…High-resolution satellite imagery is one of the advances in remote sensing technology. Satellite images depicting the conditions of the Earth's surface used to have a small spatial resolution, but now they provide spatial data with high resolution [36][37][38]…”

Section: Methodsmentioning

confidence: 99%

Spatial pattern analysis and identification type of open green space in Yogyakarta city

2018

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Open green Space is a spatial plan model aiming to maintain the quality of the environment. Nowadays, the number and distribution of Open green Space have decreased, particularly in urban areas. Such decline is due to many reasons, one of which is land conversion caused by social activities. This research aimed to analyse the distribution pattern and identify the type of Open green Space in Yogyakarta City. The method in this study using moran's I Index approach, GIS and direct field review. The results showed that the Open green Space was clustered with Moran's Index of 0,074594. Validation with field check indicated that there was an absence of even, thorough, and most recent Open green Space identification. Visual detection using high-resolution satellite images and aerial photos affirmed that many open green spaces had not been inventoried, justifying the need for data update in Yogyakarta City. The identification process resulted in different types of Open green Space, but the most widely distributed ones in Yogyakarta City were in the forms of Private yard, Office park, Green belt, and City Parks.

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Spatiotemporal estimation of air temperature patterns at the street level using high resolution satellite imagery

Cited by 30 publications

References 35 publications

Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data

Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data

The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy

Spatial pattern analysis and identification type of open green space in Yogyakarta city

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