scholarly journals Gap-Filling of 8-Day Terra MODIS Daytime Land Surface Temperature in High-Latitude Cold Region with Generalized Additive Models (GAM)

2021 ◽  
Vol 13 (18) ◽  
pp. 3667
Author(s):  
Dianfan Guo ◽  
Cuizhen Wang ◽  
Shuying Zang ◽  
Jinxi Hua ◽  
Zhenghan Lv ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
High Latitude ◽  
Land Surface ◽  
Growing Season ◽  
Additive Models ◽  
Permafrost Degradation ◽  
Cold Region ◽  
Gap Filling ◽  
Mean Square Errors

Land surface temperature (LST) is a crucial parameter driving the dynamics of the thermal state on land surface. In high-latitude cold region, a long-term, stable LST product is of great importance in examining the distribution and degradation of permafrost under pressure of global warming. In this study, a generalized additive model (GAM) approach was developed to fill the missing pixels of the MODIS/Terra 8-day Land Surface Temperature (MODIS LST) daytime products with the ERA5 Land Skin Temperature (ERA5ST) dataset in a high-latitude watershed in Eurasia. Comparison at valid pixels revealed that the MODIS LST was 4.8–13.0 °C higher than ERA5ST, which varies with land covers and seasons. The GAM models fairly explained the LST differences between the two products from multiple covariates including satellite-extracted environmental variables (i.e., normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and normalized difference snow index (NDSI) as well as locational information. Considering the dramatic seasonal variation of vegetation and frequent snow in the cold region, the gap-filling was conducted in two seasons. The results revealed the root mean square errors (RMSE) of 2.7 °C and 3.4 °C between the valid MODIS LST and GAM-simulated LST data in the growing season and snowing season, respectively. By including the satellite-extracted land surface information in the GAM model, localized variations of land surface temperature that are often lost in the reanalysis data were effectively compensated. Specifically, land surface wetness (NDWI) was found to be the greatest contributor to explaining the differences between the two products. Vegetation (NDVI) was useful in the growing season and snow cover (NDSI) cannot be ignored in the snow season of the study region. The km-scale gap-filled MODIS LST products provide spatially and temporally continuous details that are useful for monitoring permafrost degradation in cold regions in scenarios of global climate change.

scholarly journals A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

2021 ◽  
Vol 13 (14) ◽  
pp. 2838
Author(s):  
Yaping Mo ◽  
Yongming Xu ◽  
Huijuan Chen ◽  
Shanyou Zhu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Urban Heat Islands ◽  
Reconstruction Algorithms ◽  
Gap Filling ◽  
Practical Applications ◽  
Data Gaps

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

scholarly journals Optimization of the Local Split-Window Algorithm for FY-4A Land Surface Temperature Retrieval

2019 ◽  
Vol 11 (17) ◽  
pp. 2016
Author(s):  
Lijuan Wang ◽  
Ni Guo ◽  
Wei Wang ◽  
Hongchao Zuo
Keyword(s):  
Surface Temperature ◽  
Diurnal Variation ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Regional Scale ◽  
Pso Algorithm ◽  
Variation Characteristics ◽  
Split Window Algorithm ◽  
Mean Square Errors

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

scholarly journals Crop and Soil Temperature Difference an Additional Factor for Analysis of the Condition of Crops

2021 ◽  
Vol 333 ◽  
pp. 01004
Author(s):  
Dmitriy Emelyanov ◽  
Irina Botvich ◽  
Anatoly Shevyrnogov
Keyword(s):  
Spatial Distribution ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Significant Relationship ◽  
Land Surface ◽  
Agricultural Land ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Growing Season ◽  
Radiation Temperature

The study aims to study changes in land surface temperature (LST) of soil and vegetation on agricultural land planted with barley based on unmanned LST data. Simultaneously with the LST data, the spectral characteristics (NDVI) of crops were measured using the DJI P4 Multispectral. The paper shows the variability of vegetation indices and radiation temperature during the growing season. A significant relationship was found between the dynamics of NDVI and the dynamics of radiation temperature. The features of the variability of the spatial distribution of temperatures depending on precipitation are shown. The paper gives an example of a temperature map of the studied areas in the middle of the growing season, which shows the features of the spatial distribution of temperatures.

scholarly journals Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

2019 ◽  
Vol 11 (2) ◽  
pp. 182 ◽  
Author(s):  
Yongjiu Feng ◽  
Chen Gao ◽  
Xiaohua Tong ◽  
Shurui Chen ◽  
Zhenkun Lei ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Additive Models ◽  
Spatial Proximity ◽  
Dominant Factor ◽  
City Center ◽  
Spatial Factors ◽  
Suzhou City ◽  
The City

Land surface temperature (LST) is a fundamental Earth parameter, on both regional and global scales. We used seven Landsat images to derive LST at Suzhou City, in spring and summer 1996, 2004, and 2016, and examined the spatial factors that influence the LST patterns. Candidate spatial factors include (1) land coverage indices, such as the normalized difference built-up index (NDBI), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), (2) proximity factors such as the distances to the city center, town centers, and major roads, and (3) the LST location. Our results showed that the intensity of the surface urban heat island (SUHI) has continuously increased, over time, and the spatial distribution of SUHI was different between the two seasons. The SUHIs in Suzhou were mainly distributed in the city center, in 1996, but expanded to near suburban, in 2004 and 2016, with a substantial expansion at the highest level of SUHIs. Our buffer-zone-based gradient analysis showed that the LST decays logarithmically, or decreases linearly, with the distance to the Suzhou city center. As inferred by the generalized additive models (GAMs), strong relationships exist between the LST and the candidate factors, where the dominant factor was NDBI, followed by NDWI and NDVI. While the land coverage indices were the LST dominant factors, the spatial proximity and location also substantially influenced the LST and the SUHIs. This work improved our understanding of the SUHIs and their impacts in Suzhou, and should be helpful for policymakers to formulate counter-measures for mitigating SUHI effects.

Multiyear Dynamics of Remotely Mapped Characteristics of Ecosystems in Northern Eurasia 

2021 ◽  
Author(s):  
Victor Gornyy ◽  
Andrei Kiselev ◽  
Sergei Kritsuk ◽  
Iscander Latypov ◽  
Andrei Tronin
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Complex Structure ◽  
Water Layer ◽  
Ecosystem Dynamics ◽  
Northern Eurasia ◽  
Permafrost Degradation

<p><span><span>The climate change of the last decades has to be reflected in the ecosystems dynamics. By investigating the ecosystem dynamics one can get the information about climate change. One of the most suitable source of information about ecosystem dynamics is remote sensing satellite </span></span><span><span>data</span></span><span><span>. We used EOS multispectral images, gravimetric data of the GRACE satellite, and AURA satellite contents of sulfur dioxide data for the period of the last ~20 years. Daily and 8 days composites of different quantitative characteristics, reduced to the spatial resolution 1x1 km, were retrieved from standard products and raw data: - the daily averaged land surface temperature; - the duration of vegetation (the period of year when a land surface temperature is higher than +10</span></span><span><sup><span>o</span></sup></span><em><span><span>C</span></span></em><span><span>); - the Enhanced Vegetation Index (EVI); - the effective water layer thickness (EWLT) according satellite gravimentry); - the concentration of sulphur dioxide in atmosphere. The speed of each characteristics change was estimated and mapped by using linear regression. As the result, the regular chain of isometric domains of land surface temperature rising and decreasing was noticed from the West edge to the East edge of Northern Eurasia. The presence of this chain was the reason to express hypothesis about more complex structure of the modern atmospheric circulation and interaction between the Ferrel and the Polar cells. Additionally we have noticed that domain of the land surface temperature growth at the Northern part of West Siberia coincides with decreasing of EWLT. W</span></span><span><span>e </span></span><span><span>interpreted this phenomena as result of permafrost degradation. </span></span></p>

Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations

2010 ◽  
Vol 23 (3) ◽  
pp. 618-633 ◽  
Author(s):  
Arnon Karnieli ◽  
Nurit Agam ◽  
Rachel T. Pinker ◽  
Martha Anderson ◽  
Marc L. Imhoff ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Growing Season ◽  
Primary Factor ◽  
Drought Monitoring ◽  
Negative Slope ◽  
Limiting Factor ◽  
Vegetation Growth ◽  
Wide Range

Abstract A large number of water- and climate-related applications, such as drought monitoring, are based on spaceborne-derived relationships between land surface temperature (LST) and the normalized difference vegetation index (NDVI). The majority of these applications rely on the existence of a negative slope between the two variables, as identified in site- and time-specific studies. The current paper investigates the generality of the LST–NDVI relationship over a wide range of moisture and climatic/radiation regimes encountered over the North American continent (up to 60°N) during the summer growing season (April–September). Information on LST and NDVI was obtained from long-term (21 years) datasets acquired with the Advanced Very High Resolution Radiometer (AVHRR). It was found that when water is the limiting factor for vegetation growth (the typical situation for low latitudes of the study area and during the midseason), the LST–NDVI correlation is negative. However, when energy is the limiting factor for vegetation growth (in higher latitudes and elevations, especially at the beginning of the growing season), a positive correlation exists between LST and NDVI. Multiple regression analysis revealed that during the beginning and the end of the growing season, solar radiation is the predominant factor driving the correlation between LST and NDVI, whereas other biophysical variables play a lesser role. Air temperature is the primary factor in midsummer. It is concluded that there is a need to use empirical LST–NDVI relationships with caution and to restrict their application to drought monitoring to areas and periods where negative correlations are observed, namely, to conditions when water—not energy—is the primary factor limiting vegetation growth.

scholarly journals Wind Speed-Independent Two-Source Energy Balance Model Based on a Theoretical Trapezoidal Relationship between Land Surface Temperature and Fractional Vegetation Cover for Evapotranspiration Estimation

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Xiao-Gang Wang ◽  
Qing Kang ◽  
Xiao-Hong Chen ◽  
Wen Wang ◽  
Qing-Hua Fu
Keyword(s):  
Heat Flux ◽  
Energy Balance ◽  
Wind Speed ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vertical Direction ◽  
Accurate Estimation ◽  
Two Stage ◽  
Mean Square Errors

An accurate estimation of terrestrial evapotranspiration over heterogeneous surfaces using satellite imagery and few meteorological observations remains a challenging task. Wind speed (u), which is known to exhibit high temporal-spatial variation, is a significant constraint in the abovementioned task. In this study, a wind speed-independent two-source energy balance (WiTSEB) model is proposed on the basis of a theoretical land surface temperature (Tr)-fractional vegetation coverage (fc) trapezoidal space and a two-stage evapotranspiration decomposing method. The temperatures in theoretically driest boundaries of the Tr-fc trapezoid are iteratively calculated without u by using an assumption of the absence of sensible heat exchange between water-saturated surface and atmosphere in the vertical direction under the given atmospheric condition. The WiTSEB was conducted in HiWATER-MUSOEXE-12 in the middle reaches of the Heihe watershed across eight landscapes by using ASTER images. Results indicate that WiTSEB provides reliable estimates in latent heat flux (LE), with root-mean-square-errors (RMSE) and coefficient of determination of 68.6 W m−2 and 0.88, respectively. The RMSE of the ratio of the vegetation transpiration component to LE is 5.7%. Sensitivity analysis indicates WiTSEB does not aggravate the sensitivity on meteorological and remote sensing inputs in comparison with other two-source models. The errors of estimated Tr and observed soil heat flux result in LE overestimation/underestimation over parts of landscapes. The two-stage evapotranspiration decomposing method is carefully verified by ground observation.

scholarly journals Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2843 ◽  
Author(s):  
Liu ◽  
Tang ◽  
Yan ◽  
Li ◽  
Liang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Radiation ◽  
Bayesian Optimization ◽  
Radiation Budget ◽  
Temperature Cycle ◽  
Avhrr Data ◽  
Mean Square Errors

Advanced Very High Resolution Radiometer (AVHRR) sensors provide a valuable data source for generating long-term global land surface temperature (LST). However, changes in the observation time that are caused by satellite orbit drift restrict their wide application. Here, a generalized split-window (GSW) algorithm was implemented to retrieve the LST from the time series AVHRR data. Afterwards, a novel orbit drift correction (ODC) algorithm, which was based on the diurnal temperature cycle (DTC) model and Bayesian optimization algorithm, was also proposed for normalizing the estimated LST to the same local time. This ODC algorithm is pixel-based and it only needs one observation every day. The resulting LSTs from the six-year National Oceanic and Atmospheric Administration (NOAA)-14 satellite data were validated while using Surface Radiation Budget Network (SURFRAD) in-situ measurements. The average accuracies for LST retrieval varied from −0.4 K to 2.0 K over six stations and they also depended on the viewing zenith angle and season. The simulated data illustrate that the proposed ODC method can improve the LST estimate at a similar magnitude to the accuracy of the LST retrieval, i.e., the root-mean-square errors (RMSEs) of the corrected LSTs were 1.3 K, 2.2 K, and 3.1 K for the LST with a retrieval RMSE of 1 K, 2 K, and 3 K, respectively. This method was less sensitive to the fractional vegetation cover (FVC), including the FVC retrieval error, size, and degree of change within a neighboring area, which suggested that it could be easily updated by applying other LST expression models. In addition, ground validation also showed an encouraging correction effect. The RMSE variations of LST estimation that were introduced by ODC were within ±0.5 K, and the correlation coefficients between the corrected LST errors and original LST errors could approach 0.91.

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