Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States

Li, Xiaoma; Zhou, Yuyu; Asrar, Ghassem; Zhu, Zhengyuan

doi:10.1016/j.rse.2017.12.010

Cited by 132 publications

(78 citation statements)

References 57 publications

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“…MODIS LST data are produced using the generalized split-window algorithm and the day/night algorithm, which have been continuously optimized for quality improvement (Wan 2014;Wan and Dozier 1996). Although a daily 1 km spatial resolution dataset is available, monthly products on a 0.05°geographical grid (MOD11C3) have been used in this study because the availability and reliability of LST data increases with spatial aggregation (Bosilovich 2006) and temporal aggregation (Li et al 2018), resulting in fewer gaps.…”

Section: Study Areamentioning

confidence: 99%

Spatial and temporal variation of daytime and nighttime MODIS land surface temperature across Nepal

Luintel

et al. 2019

Atmospheric and Oceanic Science Letters

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Land surface temperature (LST) is an important variable for assessing climate change and related environmental impacts observed in recent decades. Regular monitoring of LST using satellite sensors such as MODIS has the advantage of global coverage, including topographically complex regions such as Nepal. In order to assess the climatic and environmental changes, daytime and nighttime LST trend analysis from 2000 to 2017 using Terra-MODIS monthly daytime and nighttime LST datasets at seasonal and annual scales over the territory of Nepal was performed. The magnitude of the trend was quantified using ordinary linear regression, while the statistical significance of the trend was identified by the Modified Mann-Kendall test. Our findings suggest that the nighttime LST in Nepal increased more prominently compared to the daytime LST, with more pronounced warming in the pre-monsoon and monsoon seasons. The annual nighttime LST increased at a rate of 0.05 K yr −1 (p < 0.01), while the daytime LST change was statistically insignificant. Spatial heterogeneity of the LST and LST change was observed both during the day and the night. The daytime LST remained fairly unchanged in large parts of Nepal, while a nighttime LST rise was dominant all across Nepal in the pre-monsoon and monsoon seasons. Our results on LST trends and their spatial distribution can facilitate a better understanding of regional climate changes. 利用MODIS分析的白天和夜间尼泊尔地表温度的时空分布摘要地表温度(LST)是评估气候变化，以及最近几十年观测到的气候变化相关环境影响的重要变量。使用卫星传感器，如MODIS，定期监测LST具有全球覆盖的优势，包括了尼泊尔等地形复杂地区。为了评估气候和环境的变化，使用Terra-MODIS月平均日间和夜间LST数据集对尼泊尔境内2000年至2017年的日间和夜间LST趋势进行了季节和年度分析。采用普通线性回归对趋势大小进行量化，并利用改进的Mann-Kendall检验确定趋势的统计学意义。我们的研究结果表明，尼泊尔夜间相比于白天的LST增加得更为显著，并且相比于其他时期，季风前期和季风期的变暖更为明显。夜间LST的年增长率为0.05 K yr-1 (p <0.01) ，而白天LST的变化无统计学意义。白天和夜间LST和LST变化均存在空间异质性。白天的LST在尼泊尔的大部分地区基本没有变化，而夜间的LST上升在季风前期和季风期的尼泊尔各地占主导地位。我们对LST趋势及其空间分布的研究结果有助于更好地了解区域气候变化。 ARTICLE HISTORY

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Section: Study Areamentioning

confidence: 99%

Spatial and temporal variation of daytime and nighttime MODIS land surface temperature across Nepal

Luintel

et al. 2019

Atmospheric and Oceanic Science Letters

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“…It is therefore necessary to evaluate whether the training samples are representative or not. Finally, as in previous LST reconstruction methods [7,8,10,13,14,18], the reconstructed LST values of the proposed method are estimated under cloud-free conditions. However, the missing data are mostly pixels covered by clouds when deriving LST from satellite data.…”

Section: Discussionmentioning

confidence: 99%

“…To date, several methods have been developed and applied to reconstruct the missing values in remotely sensed LSTs [8][9][10][11]. Most of these approaches can be classified into three categories: (1) spatial information-based methods [7,12,13], (2) multi-temporal information-based methods [9,[14][15][16], and (3) spatiotemporal information-based methods [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Geostationary Satellite Land Surface Temperature Imagery Based on a Multiscale Feature Connected Convolutional Neural Network

Yin

Yang

et al. 2019

Remote Sensing

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Geostationary satellite land surface temperature (GLST) data are important for various dynamic environmental and natural resource applications for terrestrial ecosystems. Due to clouds, shadows, and other atmospheric conditions, the derived LSTs are often missing a large number of values. Reconstructing the missing values is essential for improving the usability of the geostationary satellite LST data. However, current reconstruction methods mainly aim to fill the values of a small number of invalid pixels with many valid pixels, which can provide useful land surface temperature values. When the missing data extent becomes large, the reconstruction effect will worsen because the relationship between different spatiotemporal geostationary satellite LSTs is complex and highly nonlinear. Inspired by the superiority of the deep convolutional neural network (CNN) in solving highly nonlinear and dynamic problems, a multiscale feature connection CNN model is proposed to fill missing LSTs with large missing regions. The proposed method has been tested on both FengYun-2G and Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager geostationary satellite LST datasets. The results of simulated and actual experiments show that the proposed method is accurate to within about 1 °C, with 70% missing data rates. This is feasible and effective for large regions of LST reconstruction tasks.

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“…We compared the BME method with four other commonly used LST gap-filling methods, including Crosson's method of supplementing MYD data with the same day's MOD data [22], the time interpolation method HANTS [23], the Kriging spatial interpolation method, and the hybrid gap-filling method proposed by Li [30]. It is worth noting that the same hard data and the same spatial covariance model of the BME method were entered into Kriging, and the only difference was that the Kriging method does not consider soft data.…”

Section: Comparisons With Other Methodsmentioning

confidence: 99%

Reconstructing One Kilometre Resolution Daily Clear-Sky LST for China’s Landmass Using the BME Method

Zhang

Chen

et al. 2019

Remote Sensing

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The land surface temperature (LST) is a key parameter used to characterize the interaction between land and the atmosphere. Therefore, obtaining highly accurate, spatially consistent and temporally continuous LSTs in large areas is the basis of many studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product is commonly used to achieve this. However, it has many missing values caused by clouds and other factors. The current gap-filling methods need to be improved when applied to large areas. In this study, we used the Bayesian maximum entropy (BME) method, which considers spatial and temporal correlation, and takes multiple regression results of the Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), longitude and latitude as soft data to reconstruct space-complete daily clear-sky LSTs with a 1 km resolution for China’s landmass in 2015. The average Root Mean Square Error (RMSE) of this method was 1.6 K for the daytime and 1.2 K for the nighttime when we simultaneously covered more than 10,000 verification points, including blocks that were continuous in space, and the average RMSE of a single discrete verification point for 365 days was 0.4 K for the daytime and 0.3 K for the nighttime when we covered four discrete points. Urban and snow land cover types have a higher accuracy than forests and grasslands, and the accuracy is higher in winter than in summer. The high accuracy and great ability of this method to capture extreme values in urban areas can help improve urban heat island research. This method can also be extended to other study areas, other time periods, and the estimation of other geographical attribute values. How to effectively convert clear-sky LST into real LST requires further research.

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Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States

Cited by 132 publications

References 57 publications

Spatial and temporal variation of daytime and nighttime MODIS land surface temperature across Nepal

Spatial and temporal variation of daytime and nighttime MODIS land surface temperature across Nepal

Reconstructing Geostationary Satellite Land Surface Temperature Imagery Based on a Multiscale Feature Connected Convolutional Neural Network

Reconstructing One Kilometre Resolution Daily Clear-Sky LST for China’s Landmass Using the BME Method

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