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Remote Sensing of Land Surface Radiation Budget

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 30849

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Prof. Dr. Jie Cheng

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Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: thermal infrared remote sensing; atmospheric radiation and surface energy balance
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Dr. Tianxing Wang

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School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519000, China
Interests: remote sensing of earth’s radiation balance
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Dr. Xiaotong Zhang

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Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: remote sensing of radiation balance and energy budget sphere; data fusion and mining; data spatio-temporal analysis; machine learning
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Dr. Yunjun Yao

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State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: evaporation modeling; remote sensing; evapotranspiration product fusion
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Dr. Dongdong Wang

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Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Interests: quantitative remote sensing; earth radiation budget; remote sensing data integration
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Special Issue Information

Dear colleagues,

The land surface radiation budget (SRB), describing the radiation balance between the incoming radiation and outgoing radiation in both shortwave and longwave spectra domains at the surface, is essential to any land surface models that characterize hydrological, ecological, and biogeochemical processes. Major components of the land surface radiation budget are surface net radiation, heat conduction (i.e., soil heat flux), and turbulent heat flux components (i.e., sensible and latent heating). It has been proven that remote sensing is a valuable data source to accurately map the long-term SRB components at various spatial and temporal resolutions. In particular, many space agencies and organizations around the world have already released various SRB climate data record (CDR) products. However, current existing SRB products are of insufficient accuracy for some applications. The spatial pattern and temporal trend inconsistency are frequently reported in the current satellite derived SRB products. Moreover, the spatial coverage and spatial–temporal resolutions of SRB products also need to be improved. With this Special Issue, we will compile the state-of-art research that addresses various aspects of land surface radiation budget. Potential topics include but are not limited to the following:

Estimate of the components of the land surface radiation budget;
New concepts, ideas, and technology of measuring the land surface radiation budget;
Evaluation of current land surface radiation budget products;
Scale effect of land surface radiation budget products;
Downscaling or upscaling issues in land surface radiation budgets;
Cloud and aerosol radiative forcing;
Topographic effect modeling and validation;
Integration of multisource land surface radiation budget products;
SRB product generation, validation, and analysis;
Monitoring the long-term variation of land surface radiation budget;
Assessment and calibration of land surface models;
Interaction between the SRB and climate change.

Dr. Jie Cheng
Dr. Tianxing Wang
Dr. Xiaotong Zhang
Dr. Yunjun Yao
Dr. Dongdong Wang
Guest Editors

Manuscript Submission Information

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Keywords

Land surface radiation budget
Net radiation
Downward shortwave radiation
Land surface albedo
Land surface temperature
Land surface broadband emissivity
Land surface upwelling longwave radiation
Land surface downward longwave radiation
Ground flux measurements
Evapotranspiration
Sensible heat flux
Latent heat flux
Soil heat flux
Remote sensing
Earth observation

Published Papers (9 papers)

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Research

24 pages, 6685 KiB

Open AccessArticle

An Assessment of Using Remote Sensing-based Models to Estimate Ground Surface Soil Heat Flux on the Tibetan Plateau during the Freeze-thaw Process

by Cheng Yang, Tonghua Wu, Jimin Yao, Ren Li, Changwei Xie, Guojie Hu, Xiaofan Zhu, Yinghui Zhang, Jie Ni, Junming Hao, Xiangfei Li, Wensi Ma and Amin Wen

Remote Sens. 2020, 12(3), 501; https://doi.org/10.3390/rs12030501 - 04 Feb 2020

Cited by 7 | Viewed by 2901

Abstract

The ground surface soil heat flux (G₀) is very important to simulate the changes of frozen ground and the active layer thickness; in addition, the freeze-thaw cycle will also affect G₀ on the Tibetan Plateau (TP). As G₀ could not be measured directly and soil heat flux is difficult to be observed on the TP in situ due to its high altitude and cold environment, most of previous studies have directly applied existing remote sensing-based models to estimate G₀ without assessing whether the selected model is the best one of those models for those study regions. We use in-situ observation data collected at 12 sites combined with Moderate Resolution Imaging Spectroradiometer (MODIS) data (MOD13Q1, MODLT1D, MOD09CMG, and MCD15A2H) and the China meteorological forcing dataset (CMFD-SRad and CMFD-LRad) to validate the main models during the freeze-thaw process. The results show that during the three stages (complete freezing (CF), daily freeze-thaw cycle (DFT), and complete thawing (CT)) of the freeze-thaw cycle, the root mean square error (RMSE) between the models' G₀ simulated value and the corresponding G₀ "measured value" is the largest in the CT phase and smallest in the CF phase. The simulated results of the second group schemes (SEBAL, Ma, SEBAL_adj, and Ma_adj) were slightly underestimated, more stable, and closer to the measured values than the first group schemes (Choudhury, Clawson, SEBS, Choudhury_adj, Clawson_adj, and SEBS_adj). The Ma_adj scheme is the one with the smallest RMSE among all the schemes and could be directly applied across the entire TP. Then, four possible reasons leading to the errors of the main schemes were analyzed. The soil moisture affecting the ratio G₀/R_n and the phase shift between G₀ and net radiation R_n are not considered in the schemes directly; the scheme cannot completely and correctly capture the direction of G₀; and the input data of the schemes to estimate the regional G₀ maybe bring some errors into the simulated results. The results are expected to provide a basis for selecting remote sensing-based models to simulate G₀ in frozen ground dynamics and to calculate evapotranspiration on the TP during the freeze-thaw process. The scheme Ma_adj suitable for the TP was also offered in the study. We proposed several improvement directions of remote sensing-based models in order to enhance understanding of the energy exchange between the ground surface and the atmosphere. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Radiation Budget)

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22 pages, 8387 KiB

Open AccessArticle

Estimation of Surface Downward Shortwave Radiation over China from Himawari-8 AHI Data Based on Random Forest

by Ning Hou, Xiaotong Zhang, Weiyu Zhang, Yu Wei, Kun Jia, Yunjun Yao, Bo Jiang and Jie Cheng

Remote Sens. 2020, 12(1), 181; https://doi.org/10.3390/rs12010181 - 03 Jan 2020

Cited by 29 | Viewed by 4280

Abstract

Downward shortwave radiation (R_S) drives many processes related to atmosphere–surface interactions and has great influence on the earth’s climate system. However, ground-measured R_S is still insufficient to represent the land surface, so it is still critical to generate high accuracy and spatially continuous R_S data. This study tries to apply the random forest (RF) method to estimate the R_S from the Himawari-8 Advanced Himawari Imager (AHI) data from February to May 2016 with a two-km spatial resolution and a one-day temporal resolution. The ground-measured R_S at 86 stations of the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) are collected to evaluate the estimated R_S data from the RF method. The evaluation results indicate that the RF method is capable of estimating the R_S well at both the daily and monthly time scales. For the daily time scale, the evaluation results based on validation data show an overall R value of 0.92, a root mean square error (RMSE) value of 35.38 (18.40%) Wm⁻², and a mean bias error (MBE) value of 0.01 (0.01%) Wm⁻². For the estimated monthly R_S, the overall R was 0.99, the RMSE was 7.74 (4.09%) Wm⁻², and the MBE was 0.03 (0.02%) Wm⁻² at the selected stations. The comparison between the estimated R_S data over China and the Clouds and Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) R_S dataset was also conducted in this study. The comparison results indicate that the R_S estimates from the RF method have comparable accuracy with the CERES-EBAF R_S data over China but provide higher spatial and temporal resolution. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Radiation Budget)

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29 pages, 13280 KiB

Open AccessArticle

Surface Shortwave Net Radiation Estimation from Landsat TM/ETM+ Data Using Four Machine Learning Algorithms

by Yezhe Wang, Bo Jiang, Shunlin Liang, Dongdong Wang, Tao He, Qian Wang, Xiang Zhao and Jianglei Xu

Remote Sens. 2019, 11(23), 2847; https://doi.org/10.3390/rs11232847 - 29 Nov 2019

Cited by 17 | Viewed by 3524

Abstract

Surface shortwave net radiation (SSNR) flux is essential for the determination of the radiation energy balance between the atmosphere and the Earth’s surface. The satellite-derived intermediate SSNR data are strongly needed to bridge the gap between existing coarse-resolution SSNR products and point-based measurements. In this study, four different machine learning (ML) algorithms were tested to estimate the SSNR from the Landsat Thematic Mapper (TM)/ Enhanced Thematic Mapper Plus (ETM+) top-of-atmosphere (TOA) reflectance and other ancillary information (i.e., clearness index, water vapor) at instantaneous and daily scales under all sky conditions. The four ML algorithms include the multivariate adaptive regression splines (MARS), backpropagation neural network (BPNN), support vector regression (SVR), and gradient boosting regression tree (GBRT). Collected in-situ measurements were used to train the global model (using all data) and the conditional models (in which all data were divided into subsets and the models were fitted separately). The validation results indicated that the GBRT-based global model (GGM) performs the best at both the instantaneous and daily scales. For example, the GGM based on the TM data yielded a coefficient of determination value (R²) of 0.88 and 0.94, an average root mean square error (RMSE) of 73.23 W∙m^-2 (15.09%) and 18.76 W·m^-2 (11.2%), and a bias of 0.64 W·m^-2 and –1.74 W·m^-2 for instantaneous and daily SSNR, respectively. Compared to the Global LAnd Surface Satellite (GLASS) daily SSNR product, the daily TM-SSNR showed a very similar spatial distribution but with more details. Further analysis also demonstrated the robustness of the GGM for various land cover types, elevation, general atmospheric conditions, and seasons Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Radiation Budget)

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23 pages, 13641 KiB

Open AccessArticle

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

by Xiangyang Liu, Bo-Hui Tang, Guangjian Yan, Zhao-Liang Li and Shunlin Liang

Remote Sens. 2019, 11(23), 2843; https://doi.org/10.3390/rs11232843 - 29 Nov 2019

Cited by 29 | Viewed by 3260

Abstract

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. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Radiation Budget)

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20 pages, 4292 KiB

Open AccessArticle

Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data

by Hua Wu and Wangmin Ying

Remote Sens. 2019, 11(21), 2520; https://doi.org/10.3390/rs11212520 - 28 Oct 2019

Cited by 18 | Viewed by 3333

Abstract

Net surface shortwave radiation (NSSR) is one of the most important fundamental parameters in various land processes. Benefiting from its efficient nonlinear fitting ability, machine learning algorithms have a great potential in the retrieval of NSSR. However, few studies have explored the level of accuracy that machine learning algorithms can reach for different land covers on the worldwide scale and what the optimal independent variables are in the machine learning-based NSSR model. To guide the use of machine learning algorithms correctly in the retrieval of NSSR, it is necessary to give a comprehensive analysis from algorithm complexity, accuracy, and other aspects. In this study, three classic machine learning algorithms, including Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR), were built well to estimate instantaneous NSSR with optimal hyperparameters by elaborately selecting different independent variables, including top of atmosphere (TOA) channel spectral reflectance, geographic parameters, surface information, and atmosphere conditions. Global FLUXNET in situ measurements throughout 2014 were used to validate the accuracies of retrieved NSSR over various land cover types. The root mean square error (RMSE) is below 55 W/m², and the distributions of error histogram are also similar. Approximately 50% of absolute error were within 25 W/m². There was a performance difference of NSSR estimations in various surface types, and the performance of three machine learning methods in a specific surface type was also different. However, the RF method may be considered as the optimal methodology to retrieve NSSR from MODIS data, owing to its relatively better precision and concise hyperparameter-tuned process. The importance analysis of the proposed independent variables of NSSR retrieval shows that the introduction of geographic information can effectively reduce the error of NSSR retrieval, and surface information and atmosphere information are not necessary. It was also found that a combination of geographic information and blue band TOA reflectance already have a pretty good accuracy in NSSR retrieval, which implies there is a possibility to transfer our NSSR model to other satellite sensors, especially with insufficient channels. In a word, the NSSR model with machine learning algorithms would be an efficient, concise, and general method in the future. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Radiation Budget)

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25 pages, 29951 KiB

Open AccessArticle

A Split Window Algorithm for Retrieving Land Surface Temperature from FY-3D MERSI-2 Data

by Han Wang, Kebiao Mao, Fengyun Mu, Jiancheng Shi, Jun Yang, Zhaoliang Li and Zhihao Qin

Remote Sens. 2019, 11(18), 2083; https://doi.org/10.3390/rs11182083 - 05 Sep 2019

Cited by 30 | Viewed by 3785

Abstract

The thermal infrared (TIR) data from the Medium Resolution Spectral Imager II (MERSI-2) on the Chinese meteorological satellite FY-3D have high spatiotemporal resolution. Although the MERSI-2 land surface temperature (LST) products have good application prospects, there are some deviations in the TIR band radiance from MERSI-2. To accurately retrieve LSTs from MERSI-2, a method based on a cross-calibration model and split window (SW) algorithm is proposed. The method is divided into two parts: cross-calibration and LST retrieval. First, the MODTRAN program is used to simulate the radiation transfer process to obtain MERSI-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) simulation data, establish a cross-calibration model, and then calculate the actual brightness temperature (BT) of the MERSI-2 image. Second, according to the characteristics of the near-infrared (NIR) bands, the atmospheric water vapor content (WVC) is retrieved, and the atmospheric transmittance is calculated. The land surface emissivity is estimated by the NDVI-based threshold method, which ensures that both parameters (transmittance and emissivity) can be acquired simultaneously. The validation shows the following: 1) The average accuracy of our algorithm is 0.42 K when using simulation data; 2) the relative error of our algorithm is 1.37 K when compared with the MODIS LST product (MYD11A1); 3) when compared with ground-measured data, the accuracy of our algorithm is 1.23 K. Sensitivity analysis shows that the SW algorithm is not sensitive to the two main parameters (WVC and emissivity), which also proves that the estimation of LST from MERSI-2 data is feasible. In general, our algorithm exhibits good accuracy and applicability, but it still requires further improvement. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Radiation Budget)

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21 pages, 7305 KiB

Open AccessArticle

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

by Lijuan Wang, Ni Guo, Wei Wang and Hongchao Zuo

Remote Sens. 2019, 11(17), 2016; https://doi.org/10.3390/rs11172016 - 27 Aug 2019

Cited by 16 | Viewed by 2614

Abstract

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. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Radiation Budget)

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19 pages, 3636 KiB

Open AccessArticle

Integrating Latent Heat Flux Products from MODIS and Landsat Data Using Multi-Resolution Kalman Filter Method in the Midstream of Heihe River Basin of Northwest China

by Jia Xu, Yunjun Yao, Kanran Tan, Yufu Li, Shaomin Liu, Ke Shang, Kun Jia, Xiaotong Zhang, Xiaowei Chen and Xiangyi Bei

Remote Sens. 2019, 11(15), 1787; https://doi.org/10.3390/rs11151787 - 31 Jul 2019

Cited by 4 | Viewed by 2950

Abstract

An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is crucial to the management and planning of water resources for arid and semi-arid areas, for which LE estimations from different satellite sensors unfortunately often contain data gaps and are inconsistent. Many integration approaches have been implemented to overcome these limitations; however, most suffer from either the persistent bias of relying on datasets at only one resolution or the spatiotemporal inconsistency of LE products. In this study, we exhibit an integration case in the midstream of the Heihe River Basin of northwest China by using a multi-resolution Kalman filter (MKF) method to develop continuous and consistent LE maps from satellite LE datasets across different resolutions. The Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16), the Landsat-based LE product derived from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor, and ground observations of eddy covariance flux tower from June to September 2012 are used. The integrated results illustrate that data gaps of MOD16 dropped to less than 0.4% from the original 27–52%, and the root-mean-square error (RMSE) between the LE products decreased by 50.7% on average. Our findings indicate that the MKF method has excellent capacity to fill data gaps, reduce uncertainty, and improve the consistency of multiple LE datasets at different resolutions. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Radiation Budget)

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24 pages, 6116 KiB

Open AccessArticle

Evaluation of Bayesian Multimodel Estimation in Surface Incident Shortwave Radiation Simulation over High Latitude Areas

by Weiyu Zhang, Xiaotong Zhang, Wenhong Li, Ning Hou, Yu Wei, Kun Jia, Yunjun Yao and Jie Cheng

Remote Sens. 2019, 11(15), 1776; https://doi.org/10.3390/rs11151776 - 29 Jul 2019

Cited by 8 | Viewed by 3263

Abstract

Surface incident shortwave radiation (SSR) is crucial for understanding the Earth’s climate change issues. Simulations from general circulation models (GCMs) are one of the most practical ways to produce long-term global SSR products. Although previous studies have comprehensively assessed the performance of the GCMs in simulating SSR globally or regionally, studies assessing the performance of these models over high-latitude areas are sparse. This study evaluated and intercompared the SSR simulations of 48 GCMs participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) using quality-controlled SSR surface measurements at 44 radiation sites from three observation networks (GC-NET, BSRN, and GEBA) and the SSR retrievals from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF) data set over high-latitude areas from 2000 to 2005. Furthermore, this study evaluated the performance of the SSR estimations of two multimodel ensemble methods, i.e., the simple model averaging (SMA) and the Bayesian model averaging (BMA) methods. The seasonal performance of the SSR estimations of individual GCMs, the SMA method, and the BMA method were also intercompared. The evaluation results indicated that there were large deficiencies in the performance of the individual GCMs in simulating SSR, and these GCM SSR simulations did not show a tendency to overestimate the SSR over high-latitude areas. Moreover, the ensemble SSR estimations generated by the SMA and BMA methods were superior to all individual GCM SSR simulations over high-latitude areas, and the estimations of the BMA method were the best compared to individual GCM simulations and the SMA method-based estimations. Compared to the CERES EBAF SSR retrievals, the uncertainties of the SSR estimations of the GCMs, the SMA method, and the BMA method are relatively large during summer. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Radiation Budget)

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