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Advances on Land–Ocean Heat Fluxes Using Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 39334

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Special Issue Editors

Prof. Dr. Yunjun Yao

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Guest Editor

State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: remote sensing of evapotranspiration; drought monitoring by remotely sensed data; estimation of the terrestrial water budget; vegetation remote sensing; forest remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Gad Levy

E-Mail Website
Guest Editor

NorthWest Research Associates, Seattle, WA 98105, USA
Interests: remote sensing; weather and climate prediction and modeling; meteorology; climatology; ocean–atmosphere and air–sea interactions
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaotong Zhang

E-Mail Website
Guest Editor

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
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kun Jia

E-Mail Website
Guest Editor

State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: remote sensing of vegetation; land cover/land use; remote sensing of ecological environment; agriculture remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ayad M. Fadhil Al-Quraishi

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Guest Editor

Surveying and Geomatics Engineering Department, Faculty of Engineering, Tishk International University, Erbil 44001, Kurdistan Region, Iraq
Interests: remote sensing of land & vegetation; landcover/land use; drought; land degradation; change detection; land surface temperature
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world is currently confronted with historically unprecedented climate challenges, particularly those related to land–ocean heat fluxes. To better understand and monitor energy budget, remote sensing provides high-quality products of different bio-and geophysical variables. Though several international space agencies produce high-level land-ocean products from different satellite observations, these products have great uncertainties because of different satellite sensors and different algorithms.

Currently, an increasing number of new remote sensing techniques have been used for estimating land and ocean variables such as shortwave and longwave radiation, latent and sensible heat fluxes, vegetation leaf area index, marine chlorophyll, gross primary productivity and snow cover and precipitation. As a result, the demand for estimating land-ocean variables using these new technologies is growing in many parts of the world. This Special Issue of Remote Sensing focuses on advances on land–ocean heat fluxes using remote sensing. Potential topics include but are not limited to the following:

satellite-based land and ocean energy budget estimation;
generation and assessment of satellite land and ocean energy budget products;
satellite meteorology and oceanography;
earth observations that include satellite, climate, oceanic, and biophysical data for application;
remote sensing of vegetation and ecology;
remote sensing data fusion and mining;
machine learning for satellite modelling and application;
monitoring the long-term variation of land and ocean variables;
unmanned aerial vehicle techniques;
remote sensing of water and carbon cycles;
remote sensing application.

Prof. Dr. Yunjun Yao
Dr. Gad Levy
Dr. Xiaotong Zhang
Dr. Kun Jia
Prof. Dr. Ayad M. Fadhil Al-Quraishi
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

remote sensing
climate change
energy budget
shortwave and longwave radiation
latent and sensible heat flux
water cycle
carbon cycle
vegetation
soil, ice and snow
unmanned aerial vehicle

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Published Papers (13 papers)

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Editorial

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7 pages, 237 KiB

Open AccessEditorial

Advances in Land–Ocean Heat Fluxes Using Remote Sensing

by Yunjun Yao, Xiaotong Zhang, Gad Levy, Kun Jia and Ayad M. Fadhil Al-Quraishi

Remote Sens. 2022, 14(14), 3402; https://doi.org/10.3390/rs14143402 - 15 Jul 2022

Viewed by 1973

Abstract

Advanced remote sensing technology has provided spatially distributed variables for estimating land–ocean heat fluxes, allowing for practical applications in drought monitoring, water resources management, and climate assessment. This Special Issue includes several research studies using state-of-the-art algorithms for estimating downward longwave radiation, surface net radiation, latent heat flux, columnar atmospheric water vapor, fractional vegetation cover, and grassland aboveground biomass. This Special Issue intends to help scientists involved in global change research and practices better comprehend the strengths and disadvantages of the application of remote sensing for monitoring surface energy, water, and carbon budgets. The studies published in this Special Issue can be applied by natural resource management communities to enhance the characterization and assessment of land–ocean biophysical variables, as well as for more accurately partitioning heat flux into soil and vegetation based on the existing and forthcoming remote sensing data. Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

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17 pages, 8455 KiB

Open AccessArticle

Influence of the Nocturnal Effect on the Estimated Global CO₂ Flux

by Rui Jin, Tan Yu, Bangyi Tao, Weizeng Shao, Song Hu and Yongliang Wei

Remote Sens. 2022, 14(13), 3192; https://doi.org/10.3390/rs14133192 - 3 Jul 2022

Cited by 3 | Viewed by 1729 | Correction

Abstract

We found that significant errors occurred when diurnal data instead of diurnal–nocturnal data were used to calculate the daily sea-air CO₂ flux (F). As the errors were mainly associated with the partial pressure of CO₂ in seawater (pCO_2w) and the sea surface temperature (SST) in the control experiment, pCO_2w and SST equations were established, which are called the nocturnal effect of the CO₂ flux. The root-mean-square error between the real daily CO₂ flux (F_real) and the daily CO₂ flux corrected for the nocturnal effect (F_com) was 11.93 mmol m⁻² d⁻¹, which was significantly lower than that between the F_real value and the diurnal CO₂ flux (F_day) (46.32 mmol m⁻² d⁻¹). Thus, the errors associated with using diurnal data to calculate the CO₂ flux can be reduced by accounting for the nocturnal effect. The mean global daily CO₂ flux estimated based on the nocturnal effect and the sub-regional pCO_2w algorithm (cor_F_com) was −6.86 mol m⁻² y⁻¹ (September 2020–August 2021), which was greater by 0.75 mol m⁻² y⁻¹ than that based solely on the sub-regional pCO_2w algorithm (day_F_com = −7.61 mol m⁻² y⁻¹). That is, compared with cor_F_com, the global day_F_com value overestimated the CO₂ sink of the global ocean by 10.89%. Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

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22 pages, 36262 KiB

Open AccessArticle

A New Empirical Estimation Scheme for Daily Net Radiation at the Ocean Surface

by Jianghai Peng, Bo Jiang, Hongkai Chen, Shunlin Liang, Hui Liang, Shaopeng Li, Jiakun Han, Qiang Liu, Jie Cheng, Yunjun Yao, Kun Jia and Xiaotong Zhang

Remote Sens. 2021, 13(20), 4170; https://doi.org/10.3390/rs13204170 - 18 Oct 2021

Cited by 3 | Viewed by 2138

Abstract

Ocean surface net radiation (R_n) is significant in research on the Earth’s heat balance systems, air–sea interactions, and other applications. However, there have been few studies on R_n until now. Based on radiative and meteorological measurements collected from 66 globally [...] Read more.

R_{g}^{↓}

) when the length ratio of daytime (LRD) was greater than 0.4 but dominated by downward longwave radiation (

R_{l}^{↓}

) for the other cases (LRD ≤ 0.4). Therefore, an empirical scheme that includes two conditional models named Case 1 (LRD > 0.4) utilizing

R_{g}^{↓}

as a major input and Case 2 (LRD ≤ 0.4) utilizing

R_{l}^{↓}

as a major input for R_n estimation was successfully developed. After validation against in situ R_n, the performance of the empirical scheme was satisfactory with an overall R² value of 0.972, an RMSE of 9.768 Wm⁻², and a bias of −0.092 Wm⁻². Specifically, the accuracies of the two conditional models were also very good, with RMSEs of 9.805 and 2.824 Wm⁻² and biases of −0.095 and 0.346 Wm⁻² for the Case 1 and Case 2 models, respectively. However, due to the limited number of available samples, the performances of these new models were poor in coastal and high-latitude areas, and the models did not work when the LRD was too small (i.e., LRD < 0.3). Overall, the newly developed empirical scheme for R_n estimation has strong potential to be widely used in practical use because of its simple format and high accuracy. Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

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23 pages, 4390 KiB

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ERTFM: An Effective Model to Fuse Chinese GF-1 and MODIS Reflectance Data for Terrestrial Latent Heat Flux Estimation

by Lilin Zhang, Yunjun Yao, Xiangyi Bei, Yufu Li, Ke Shang, Junming Yang, Xiaozheng Guo, Ruiyang Yu and Zijing Xie

Remote Sens. 2021, 13(18), 3703; https://doi.org/10.3390/rs13183703 - 16 Sep 2021

Cited by 6 | Viewed by 2551

Abstract

Coarse spatial resolution sensors play a major role in capturing temporal variation, as satellite images that capture fine spatial scales have a relatively long revisit cycle. The trade-off between the revisit cycle and spatial resolution hinders the access of terrestrial latent heat flux (LE) data with both fine spatial and temporal resolution. In this paper, we firstly investigated the capability of an Extremely Randomized Trees Fusion Model (ERTFM) to reconstruct high spatiotemporal resolution reflectance data from a fusion of the Chinese GaoFen-1 (GF-1) and the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Then, based on the merged reflectance data, we used a Modified-Satellite Priestley–Taylor (MS–PT) algorithm to generate LE products at high spatial and temporal resolutions. Our results illustrated that the ERTFM-based reflectance estimates showed close similarity with observed GF-1 images and the predicted NDVI agreed well with observed NDVI at two corresponding dates (r = 0.76 and 0.86, respectively). In comparison with other four fusion methods, including the widely used spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM, ERTFM had the best performance in terms of predicting reflectance (SSIM = 0.91; r = 0.77). Further analysis revealed that LE estimates using ERTFM-based data presented more detailed spatiotemporal characteristics and provided close agreement with site-level LE observations, with an R² of 0.81 and an RMSE of 19.18 W/m². Our findings suggest that the ERTFM can be used to improve LE estimation with high frequency and high spatial resolution, meaning that it has great potential to support agricultural monitoring and irrigation management. Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

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21 pages, 5537 KiB

Open AccessArticle

Satellite-Derived Estimation of Grassland Aboveground Biomass in the Three-River Headwaters Region of China during 1982–2018

by Ruiyang Yu, Yunjun Yao, Qiao Wang, Huawei Wan, Zijing Xie, Wenjia Tang, Ziping Zhang, Junming Yang, Ke Shang, Xiaozheng Guo and Xiangyi Bei

Remote Sens. 2021, 13(15), 2993; https://doi.org/10.3390/rs13152993 - 29 Jul 2021

Cited by 21 | Viewed by 2823

Abstract

The long-term estimation of grassland aboveground biomass (AGB) is important for grassland resource management in the Three-River Headwaters Region (TRHR) of China. Due to the lack of reliable grassland AGB datasets since the 1980s, the long-term spatiotemporal variation in grassland AGB in the TRHR remains unclear. In this study, we estimated AGB in the grassland of 209,897 km² using advanced very high resolution radiometer (AVHRR), MODerate-resolution Imaging Spectroradiometer (MODIS), meteorological, ancillary data during 1982–2018, and 75 AGB ground observations in the growth period of 2009 in the TRHR. To enhance the spatial representativeness of ground observations, we firstly upscaled the grassland AGB using a gradient boosting regression tree (GBRT) model from ground observations to a 1 km spatial resolution via MODIS normalized difference vegetation index (NDVI), meteorological and ancillary data, and the model produced validation results with a coefficient of determination (R²) equal to 0.76, a relative mean square error (RMSE) equal to 88.8 g C m⁻², and a bias equal to −1.6 g C m⁻² between the ground-observed and MODIS-derived upscaled AGB. Then, we upscaled grassland AGB using the same model from a 1 km to 5 km spatial resolution via AVHRR NDVI and the same data as previously mentioned with the validation accuracy (R² = 0.74, RMSE = 57.8 g C m⁻², and bias = −0.1 g C m⁻²) between the MODIS-derived reference and AVHRR-derived upscaled AGB. The annual trend of grassland AGB in the TRHR increased by 0.37 g C m⁻² (p < 0.05) on average per year during 1982–2018, which was mainly caused by vegetation greening and increased precipitation. This study provided reliable long-term (1982–2018) grassland AGB datasets to monitor the spatiotemporal variation in grassland AGB in the TRHR. Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

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16 pages, 7489 KiB

Open AccessArticle

Evaluation of the J-OFURO3 Sea Surface Net Radiation and Inconsistency Correction

by Hongkai Chen, Bo Jiang, Xiuxia Li, Jianghai Peng, Hui Liang and Shaopeng Li

Remote Sens. 2021, 13(12), 2403; https://doi.org/10.3390/rs13122403 - 19 Jun 2021

Cited by 3 | Viewed by 2176

Abstract

A new satellite-based product containing daily sea surface net radiation (

R_{n}

) values at a spatial resolution of 0.25° from 1988 to 2013, named the Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations, version 3 (J-OFURO3), was recently [...] Read more.

A new satellite-based product containing daily sea surface net radiation (

R_{n}

) values at a spatial resolution of 0.25° from 1988 to 2013, named the Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations, version 3 (J-OFURO3), was recently generated and released. In this letter, the performance of the J-OFURO3 sea-surface

R_{n}

product was fully evaluated by using observations from 55 global moored buoy sites. The overall accuracy was satisfactory, with root-mean-square difference (RMSD) of 24.05 and 10.76 Wm⁻² at daily and monthly scales, respectively. However, an inconsistency issue was found in the long-term variations in the J-OFURO3 sea-surface

R_{n}

values in approximately 2000; this inconsistency may be due to the replacement of the input dataset. To address this issue, a simple but effective inconsistency correction method was developed and conducted in this study. The analysis results demonstrated that the variations in the corrected J-OFURO3 sea-surface

R_{n}

data were more reasonable and that its daily validation accuracy was significantly improved by decreasing the bias from 4.67 to 0.27 Wm⁻² before the year 2000. Thereby, it is suggested that the inconsistency correction method should be applied before using the J-OFURO3 sea-surface

R_{n}

data. However, the data users still should be cautious about another discontinuity issues caused by the quality of the input dataset itself. Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

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16 pages, 4416 KiB

Open AccessArticle

Fractional Vegetation Cover Estimation Algorithm for FY-3B Reflectance Data Based on Random Forest Regression Method

by Duanyang Liu, Kun Jia, Haiying Jiang, Mu Xia, Guofeng Tao, Bing Wang, Zhulin Chen, Bo Yuan and Jie Li

Remote Sens. 2021, 13(11), 2165; https://doi.org/10.3390/rs13112165 - 31 May 2021

Cited by 13 | Viewed by 3306

Abstract

As an important land surface vegetation parameter, fractional vegetation cover (FVC) has been widely used in many Earth system ecological and climate models. In particular, high-quality and reliable FVC products on the global scale are important for the Earth surface process simulation and global change studies. Recently, the FengYun-3 (FY-3) series satellites, which are the second generation of Chinese meteorological satellites, launched with the polar orbit and provide continuous land surface observations on a global scale. However, there is rare studying on the FVC estimation using FY-3 reflectance data. Therefore, the FY-3B reflectance data were selected as the representative data to develop a FVC estimation algorithm in this study, which would investigate the capability of the FY-3 reflectance data on the global FVC estimation. The spatial–temporal validation over the regional area indicated that the FVC estimations generated by the proposed algorithm had reliable continuities. Furthermore, a satisfactory accuracy performance (R² = 0.7336, RMSE = 0.1288) was achieved for the proposed algorithm based on the Earth Observation LABoratory (EOLAB) reference FVC data, which provided further evidence on the reliability and robustness of the proposed algorithm. All these results indicated that the FY-3 reflectance data were capable of generating a FVC estimation with reliable spatial–temporal continuities and accuracy. Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

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30 pages, 6983 KiB

Open AccessArticle

Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018

by Chunjie Feng, Xiaotong Zhang, Yu Wei, Weiyu Zhang, Ning Hou, Jiawen Xu, Shuyue Yang, Xianhong Xie and Bo Jiang

Remote Sens. 2021, 13(9), 1848; https://doi.org/10.3390/rs13091848 - 9 May 2021

Cited by 9 | Viewed by 3667

Abstract

It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (L_d, 4–100 μm) dataset. Although a number of global L_d datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily L_d dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (S_d), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated L_d dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily L_d dataset were 17.78 W m⁻², 0.99 W m⁻², and 0.96 (p < 0.01). Comparisons with other global land surface radiation products indicated that the generated L_d dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated L_d dataset showed an increasing trend of 1.8 W m⁻² per decade (p < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated L_d dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products. Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

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2 pages, 174 KiB

Open AccessCorrection

Correction: Jin et al. Influence of the Nocturnal Effect on the Estimated Global CO₂ Flux. Remote Sens. 2022, 14, 3192

by Rui Jin, Tan Yu, Bangyi Tao, Weizeng Shao, Song Hu and Yongliang Wei

Remote Sens. 2022, 14(24), 6403; https://doi.org/10.3390/rs14246403 - 19 Dec 2022

Viewed by 1448

Abstract

We believe that several sentences in the description of the source (sink) changes of CO₂ are prone to ambiguity and are not particularly well presented [...] Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

14 pages, 23862 KiB

Open AccessTechnical Note

Influence of Energy and Water Cycle Key Parameters on Drought in Mongolian Plateau during 1979–2020

by Jie He, Husi Letu, Yonghui Lei, Enliang Guo, Shanhu Bao, Yongqiang Zhang, Gegen Tana and Yuhai Bao

Remote Sens. 2022, 14(3), 685; https://doi.org/10.3390/rs14030685 - 31 Jan 2022

Cited by 3 | Viewed by 2376

Abstract

Drought in the Mongolian Plateau (MP) has gradually intensified in recent decades. The energy and water cycles are key factors affecting drought. However, there are few quantitative studies on the mechanism of aridity change in this region. This study uses the ERA5, Moderate Resolution Imaging Spectroradiometer (MODIS) and Himawari 8 datasets and investigated the mechanism of drought change over the MP. The aridity index (the ratio of potential evaporation and total precipitation) is employed to detect drought changes. The results showed that the annual mean of aridity index increased by 0.73% per year (increased significantly since 1999) during the period 1979–2020. Moreover, the drought was most severe in the January to April of 2016–2020, mainly concentrated in the central and western parts of the MP. The potential evaporation increased (0.72% per year) and total precipitation decreased (0.16% per year) from 1979 to 2020. However, the surface temperature continued increasing from August to December in the period 2016–2020 (1.67% per year). This may result in an increase in potential evaporation and a decrease in volumetric soil water from August to December last year. The decrease of volumetric soil water resulted in the decrease of total cloud cover (0.25% per year) and total precipitation from January to April. The surface net radiation (increased by 0.42% per year) and the potential evaporation increased, which may aggravate the drought from January to April. The evaporation paradox is studied over the MP. The results show that the variation in evaporation is consistent with that of total precipitation, and the surface temperature will promote an increase in evaporation and potential evaporation. This study reveals that global warming, desertification and increased surface net radiation contribute to the increase in potential evaporation and reduced volumetric soil water, which together contribute to drought. Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

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13 pages, 2462 KiB

Open AccessTechnical Note

Spatially Downscaling a Global Evapotranspiration Product for End User Using a Deep Neural Network: A Case Study with the GLEAM Product

by Xunjian Long and Yaokui Cui

Remote Sens. 2022, 14(3), 658; https://doi.org/10.3390/rs14030658 - 29 Jan 2022

Cited by 8 | Viewed by 2691

Abstract

High spatiotemporal resolution evapotranspiration (ET) data are very important for end users to manage water resources. The global ET product always has a high temporal resolution, but the spatial resolution is too low to meet the requirements of most end users. In this study, we developed a deep neural network (DNN)-based global ET product downscaling algorithm by combining remotely sensed and meteorological data sets as the input data. The relationship between global ET product and input data was built at a low spatial resolution using the DNN. Then, this relationship was applied at high spatial resolution to generate high spatial resolution ET derived from the input data with high spatial resolution. Taking the Global Land Evaporation Amsterdam Model (GLEAM) ET product as an example, downscaled ET was found to be highly consistent with the original GLEAM ET product, but to have high spatial resolution. Field validations showed that the overall coefficient of correlation and root mean square error (bias, Nash–Sutcliffe efficiency coefficient) of the downscaled GLEAM ET is 0.90 and 0.87 mm/d (−0.32 mm/d, 0.62), respectively, indicating high quality. The proposed method bridged the gaps between the global ET product and the requirements of local end users. This will benefit end users in charge of water resources management. Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

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17 pages, 5786 KiB

Open AccessTechnical Note

Long Term Indian Ocean Dipole (IOD) Index Prediction Used Deep Learning by convLSTM

by Chen Li, Yuan Feng, Tianying Sun and Xingzhi Zhang

Remote Sens. 2022, 14(3), 523; https://doi.org/10.3390/rs14030523 - 22 Jan 2022

Cited by 17 | Viewed by 7661

Abstract

Indian Ocean Dipole (IOD) is a large-scale physical ocean phenomenon in the Indian Ocean that plays an important role in predicting the El Nino Southern Oscillation in the tropical Pacific. Predicting the occurrence of IOD is of great significance to the study of climate change and other marine phenomena. Generally, the IOD index is calculated to judge whether the IOD occurs. In this paper, a convolutional LSTM (convLSTM) neural network is used to build the deep learning model to predict the sea surface temperature in the next seven months and calculate the IOD index. Through the analysis of marine atmospheric data with complex temporal and spatial relationships, the wind field signal knowledge of the physical ocean is introduced to predict IOD phenomenon by combining the prior knowledge of the physical ocean and deep learning. The experimental results show that the average correlation of IOD index time series to the true IOD index time series is 82.87% from 2015 to 2018, seven months ahead for IOD prediction. IOD manifests as sea surface temperature (SST) anomaly changes, and this thesis verifies that the wind field signal information has a positive impact on the prediction of IOD changes. Moreover, the convLSTM can predict this anomaly better. The IOD index line graph can generally fit the real IOD index variation trend, which has a profound impact on the study of the IOD phenomenon. Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

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15 pages, 3866 KiB

Open AccessTechnical Note

New Gridded Product for the Total Columnar Atmospheric Water Vapor over Ocean Surface Constructed from Microwave Radiometer Satellite Data

by Weifu Sun, Jin Wang, Yuheng Li, Junmin Meng, Yujia Zhao and Peiqiang Wu

Remote Sens. 2021, 13(12), 2402; https://doi.org/10.3390/rs13122402 - 19 Jun 2021

Cited by 6 | Viewed by 2271

Abstract

Based on the optimal interpolation (OI) algorithm, a daily fusion product of high-resolution global ocean columnar atmospheric water vapor with a resolution of 0.25° was generated in this study from multisource remote sensing observations. The product covers the period from 2003 to 2018, and the data represent a fusion of microwave radiometer observations, including those from the Special Sensor Microwave Imager Sounder (SSMIS), WindSat, Advanced Microwave Scanning Radiometer for Earth Observing System sensor (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), and HY-2A microwave radiometer (MR). The accuracy of this water vapor fusion product was validated using radiosonde water vapor observations. The comparative results show that the overall mean deviation (Bias) is smaller than 0.6 mm; the root mean square error (RMSE) and standard deviation (SD) are better than 3 mm, and the mean absolute deviation (MAD) and correlation coefficient (R) are better than 2 mm and 0.98, respectively. Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

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