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Fusion of High-Level Remote Sensing Products

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

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 39980

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Prof. Dr. Yanchen Bo

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Guest Editor

State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: spatial and temporal statistics; spatio-temporal fusion of remote sensing products; up-scaling and downscaling of spatial information; continours monitoring using time series remote sensing data

Prof. Dr. Jie Cheng

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Guest Editor

Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: thermal infrared remote sensing; atmospheric radiation and surface energy balance
Special Issues, Collections and Topics in MDPI journals

Dr. Xin Tao

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Guest Editor

Department of Geography, University at Buffalo, Buffalo, NY 14261-0055, USA
Interests: forest disturbance mapping; the estimation of biogeophysical variables from satellite data; data fusion of satellite products; scaling effect and scale transformation of biogeophysical variables

Special Issue Information

Dear Colleagues,

With the advancement of remote sensing technology and the stimulus of strong application demands, the number of Earth observation (EO) satellites is increasing rapidly, producing big EO data. Moreover, various high-level products are generated from big EO data. Due to sensor malfunctions, cloud contamination, atmospheric effects, retrieval algorithm defects, etc., high-level remote sensing products derived from single sensors are suspected to have spatial incompleteness, temporal discontinuity, and inconsistent quality; on the other hand, the same product derived from multisensor observations might be inconsistent both in the values and physical meaning. However, products derived from different remote sensors or by different algorithms are somewhat complementary in accuracy and spatiotemporal completeness. Data fusion or integration can combine the advantages of different data sources (remote sensing, in situ observations, model outputs, etc.), thus providing continuous spatial and temporal coverage on the one hand and reducing the uncertainty of fused or integrated data on the other hand. In light of the merits mentioned above, data fusion or integration has become a promising means of obtaining high-quality spatiotemporally complete remote sensing products. With recent advances and innovations in computing, such as cloud computing and high-performance computing, computing power and storage capacity are no longer obstacles, and it is possible to generate high-level remote sensing products at the regional and global scales using data fusion or integration technologies. In this context, reviewing the achieved progress on data fusion or integration and looking forward to future development hold great relevance.

In this Special Issue, we will compile the state-of-the-art data fusion or integration methods that address various aspects of generating spatiotemporally complete, high-level remote sensing products. Potential topics include but are not limited to the following:

Scale effects in remote sensing products;
Uncertainty quantification methods for fusing remote sensing products;
Downscaling and upscaling methods;
Evaluation or validation of remote sensing products;
Data reconstruction l statistics for spatiotemporal data;
Integration of multisource data;
Fusion of multiscale remote sensing products;
Data assimilation
Machine learning;
Evaluation or validation of fused remote sensing products.

Prof. Yanchen Bo
Dr. Jie Cheng
Dr. Xin Tao
Guest Editors

Manuscript Submission Information

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Keywords

scale
downscaling and upscaling
reconstruction
integration
fusion
merging
evaluation
validation
spatiotemporal geostatistics
uncertainty

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Published Papers (10 papers)

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22 pages, 90116 KiB

Open AccessArticle

A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution

by Shuo Xu, Jie Cheng and Quan Zhang

Remote Sens. 2021, 13(11), 2211; https://doi.org/10.3390/rs13112211 - 5 Jun 2021

Cited by 25 | Viewed by 4329

Abstract

Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained. Full article

(This article belongs to the Special Issue Fusion of High-Level Remote Sensing Products)

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23 pages, 30304 KiB

Open AccessArticle

Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

by Yanxing Hu, Tao Che, Liyun Dai and Lin Xiao

Remote Sens. 2021, 13(7), 1250; https://doi.org/10.3390/rs13071250 - 25 Mar 2021

Cited by 24 | Viewed by 4366

Abstract

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R² of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions. Full article

(This article belongs to the Special Issue Fusion of High-Level Remote Sensing Products)

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29 pages, 14833 KiB

Open AccessArticle

Synthesizing a Regional Territorial Evapotranspiration Dataset for Northern China

by Linjiang Wang, Bingfang Wu, Abdelrazek Elnashar, Hongwei Zeng, Weiwei Zhu and Nana Yan

Remote Sens. 2021, 13(6), 1076; https://doi.org/10.3390/rs13061076 - 12 Mar 2021

Cited by 15 | Viewed by 3313

Abstract

As a vital role in the processes of the energy balance and hydrological cycles, actual evapotranspiration (ET) is relevant to many agricultural, ecological and water resource management studies. The available global or regional ET products provide ET estimations with various temporal ranges, spatial resolutions and calculation methods (algorithms, inputs and parameterization, etc.), leading to varying degrees of introduced uncertainty. Northern China is the main agriculturally productive region supporting the whole country; thus, understanding the spatial and temporal changes in ET is essential to ensure water resource and food security. We developed a synthesis ET dataset for Northern China at a 1000 m spatial resolution, with a monthly temporal resolution covering a period ranging from 1982 to 2017, using an in-depth assessment of several ET products. Specifically, assessments were performed using in situ measured ET from eddy covariance (EC) observation towers at the site-pixel scale over interannual months under the conditions of different land cover types, climatic zones and elevation levels to select the most optimally performing ET products to be used in the synthesized ET dataset. Eight indicators under 21 conditions were involved in the assessment sheet, while the statistics of the different ET product occurrences and corresponding ratios were analyzed to select the best-performing ET products to build the synthesis ET dataset using the weighted mean method. The weights were determined by the Taylor skill score (TSS), calculated with ET products and EC ET observation data. Based on the assessment results, the Penman–Monteith–Leuning (PML_v2), ETWatch and Operational Simplified Surface Energy Balance (SSEBop) datasets were selected for implementation in the synthesis ET dataset from 2003 to 2017, while Global Land Evaporation Amsterdam Model (GLEAM) v3.3a, complementary relationship (CR) ET, and Numerical Terradynamic Simulation Group (NTSG) datasets were chosen for the synthesis ET dataset from 1982 to 2002. The weighted mean synthesized results from 2003 to 2017 performed well when compared to the in situ measured EC ET values produced under all of the above conditions, while the synthesized results from 1982 to 2002 performed well through the water balance method in Heihe River Basin. These results can provide more stable ET estimations for Northern China, which can contribute to relevant agricultural, ecological and hydrological studies. Full article

(This article belongs to the Special Issue Fusion of High-Level Remote Sensing Products)

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20 pages, 7068 KiB

Open AccessArticle

Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

by Tee-Ann Teo and Yu-Ju Fu

Remote Sens. 2021, 13(4), 606; https://doi.org/10.3390/rs13040606 - 8 Feb 2021

Cited by 9 | Viewed by 3022

Abstract

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion. Full article

(This article belongs to the Special Issue Fusion of High-Level Remote Sensing Products)

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18 pages, 4905 KiB

Open AccessArticle

Developing a New Method to Identify Flowering Dynamics of Rapeseed Using Landsat 8 and Sentinel-1/2

by Jichong Han, Zhao Zhang and Juan Cao

Remote Sens. 2021, 13(1), 105; https://doi.org/10.3390/rs13010105 - 30 Dec 2020

Cited by 27 | Viewed by 4625

Abstract

Identifying the rapeseed (Brassica napus L.) flowering dates are important for planting area estimation, growth monitoring, and yield estimation. However, there is currently a lack of data on rapeseed flowering dates at the parcel scale. In this study, a new spectral index (Normalized Rapeseed Flowering Index, NRFI) is proposed to detect rapeseed flowering dates from time series data generated from Landsat 8 OLI and Sentinel-2 sensors. This study also analyzed the feasibility of using the backscattering coefficients (VV, VH, and VV/VH) of Sentinel-1 to detect the flowering dates of rapeseed at the parcel scale. Based on the spectral and polarization characteristics of 718 rapeseed parcels collected in 2018, we developed a method to automatically identify peak flowering dates by the local maximum of NRFI series and the local minimum of VH and VV, along with the maximum of VV/VH. The results show that most of the peak flowering dates derived from Sentinel-1 and Sentinel-2 can be confirmed by the in-situ phenological observations at the Deutscher Wetterdienst (DWD) stations in Germany. The NRFI outperforms the Normalized Difference Yellow Index (NDYI) in identifying the peak flowering dates from Landsat 8. The derived medians of peak flowering dates by NRFI, NDYI (Sentinel-2), and VH are similar, while a systematic delay is observed by NDYI (Landsat 8). The method with the spectrum and backscattering coefficients will be a potential tool to identify crop flowering dynamics and map crop planting area. Full article

(This article belongs to the Special Issue Fusion of High-Level Remote Sensing Products)

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31 pages, 11409 KiB

Open AccessArticle

Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016

by Lin Xiao, Tao Che and Liyun Dai

Remote Sens. 2020, 12(19), 3253; https://doi.org/10.3390/rs12193253 - 7 Oct 2020

Cited by 19 | Viewed by 4144

Abstract

Snow cover is a key parameter of the climate system and its significant seasonal and annual variability have significant impacts on the surface energy balance and global water circulation. However, current snow depth datasets show large inconsistencies and uncertainties, which limit their applications in climate change projections and hydrological processes simulations. In this study, a comprehensive assessment of five hemispheric snow depth datasets was carried out against ground observations from 43,391 stations. The five snow depth datasets included three remote sensing datasets, i.e., Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer-2 (AMSR2), Global Snow Monitoring for Climate Research (GlobSnow), and two reanalysis datasets, i.e., ERA-Interim and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Assessment results imply that the spatial distribution of GlobSnow and ERA-Interim exhibit overall better agreements with ground observations than other datasets. GlobSnow and ERA-Interim exhibit less uncertainty during the snow accumulation and ablation periods, respectively. In plain and forested regions, GlobSnow, ERA-Interim and MERRA-2 show better performances, while in mountain and forested mountain areas, GlobSnow exhibits the best performance. AMSR-E and AMSR2 agree better with ground observations in shallow snow condition (0–10 cm), while MERRA-2 shows more satisfying performance when snow depth exceeds 50 cm. These systematic and integrated understanding of the five representative snow depth datasets provides information on data selection and data refinement, as well as data fusion, which is our next work of interest. Full article

(This article belongs to the Special Issue Fusion of High-Level Remote Sensing Products)

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19 pages, 9673 KiB

Open AccessArticle

Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data

by Zemeng Fan, Saibo Li and Haiyan Fang

Remote Sens. 2020, 12(19), 3170; https://doi.org/10.3390/rs12193170 - 27 Sep 2020

Cited by 24 | Viewed by 3870

Abstract

Explicitly identifying the desertification changes and causes has been a hot issue of eco-environment sustainable development in the China–Mongolia–Russia Economic Corridor (CMREC) area. In this paper, the desertification change patterns between 2000 and 2015 were identified by operating the classification and regression tree (CART) method with multisource remote sensing datasets on Google Earth Engine (GEE), which has the higher overall accuracy (85%) than three other methods, namely support vector machine (SVM), random forest (RF) and Albedo-normalized difference vegetation index (NDVI) models. A contribution index of climate change and human activities on desertification was introduced to quantitatively explicate the driving mechanisms of desertification change based on the temporal datasets and net primary productivity (NPP). The results show that the area of slight desertification land had increased from 719,700 km² to 948,000 km² between 2000 and 2015. The area of severe desertification land decreased from 82,400 km² to 71,200 km². The area of desertification increased by 9.68%, in which 69.68% was mainly caused by human activities. Climate change and human activities accounted for 68.8% and 27.36%, respectively, in the area of desertification restoration. In general, the degree of desertification showed a decreasing trend, and climate change was the major driving factor in the CMREC area between 2000 and 2015. Full article

(This article belongs to the Special Issue Fusion of High-Level Remote Sensing Products)

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18 pages, 5010 KiB

Open AccessArticle

Generating a Spatio-Temporal Complete 30 m Leaf Area Index from Field and Remote Sensing Data

by Hongmin Zhou, Changjing Wang, Guodong Zhang, Huazhu Xue, Jingdi Wang and Huawei Wan

Remote Sens. 2020, 12(15), 2394; https://doi.org/10.3390/rs12152394 - 25 Jul 2020

Cited by 5 | Viewed by 2967

Abstract

The leaf area index (LAI) is an important parameter for vegetation monitoring and land surface ecosystem research. Although a variety of LAI products have been generated, the moderate to coarse spatial resolution and low temporal resolution of these products are insufficient for regional-scale analysis. In this study, a modified ensemble Kalman filter model (MEnKF) was proposed to generate spatio-temporal complete 30 m LAI data. High-quality, filtered historical Moderate-resolution Imaging Spectroradiometer (MODIS) LAI data were used to obtain the LAI background, and an LAI temporal dynamic model was constructed based on it. An improved back-propagation (BP) neural network based on a simulated annealing algorithm (SA-BP) was constructed with paired Landsat surface reflectance data and field LAI data to generate a 30 m LAI. The MEnKF was used to estimate the spatio-temporal complete LAI beginning from the LAI peak value position where Landsat observations were available. The spatio-temporal 30 m LAI was estimated in farmland (Pshenichne), grassland (Zhangbei), and woodland (Genhe) sites. The results indicate that the MEnKF-estimated LAI is consistent with the field measurements for all sites (the coefficient of determination (

R^{2}

) = 0.70; root mean squared error (RMSE) = 0.40) and is better than that of the conventional sequence data assimilation algorithm (

R^{2}

= 0.40; RMSE = 0.78). The regional LAI captures the vegetation growth pattern and is consistent with the Landsat LAI, with an

R^{2}

larger than 0.65 and an RMSE less than 0.51. The proposed MEnKF algorithm, which effectively avoids error accumulation in the data assimilation scheme, is an efficient method for spatio-temporal complete 30 m LAI estimation. Full article

(This article belongs to the Special Issue Fusion of High-Level Remote Sensing Products)

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21 pages, 7401 KiB

Open AccessArticle

An Effective and Efficient Enhanced Fixed Rank Smoothing Method for the Spatiotemporal Fusion of Multiple-Satellite Aerosol Optical Depth Products

by Bin Zou, Ning Liu, Wei Wang, Huihui Feng, Xiangping Liu and Yan Lin

Remote Sens. 2020, 12(7), 1102; https://doi.org/10.3390/rs12071102 - 30 Mar 2020

Cited by 8 | Viewed by 2952

Abstract

Current reported spatiotemporal solutions for fusing multisensor aerosol optical depth (AOD) products used to recover gaps either suffer from unacceptable accuracy levels, i.e., fixed rank smooth (FRS), or high time costs, i.e., Bayesian maximum entropy (BME). This problem is generally more serious when dealing with multiple AOD products in a long time series or over large geographic areas. This study proposes a new, effective, and efficient enhanced FRS method (FRS-EE) to fuse satellite AOD products with uncertainty constraints. AOD products used in the fusion experiment include Moderate Resolution Imaging SpectroRadiometer (MODIS) DB/DT_DB_Combined AOD and Multiangle Imaging SpectroRadiometer (MISR) AOD across mainland China from 2016 to 2017. Results show that the average completeness of original, initial FRS fused, and FRS-EE fused AODs with uncertainty constraints are 22.80%, 95.18%, and 65.84%, respectively. Although the correlation coefficient (R = 0.77), root mean square error (RMSE = 0.30), and mean bias (Bias = 0.023) of the initial FRS fused AODs are relatively lower than those of original AODs compared to Aerosol Robotic Network (AERONET) AOD records, the accuracy of FRS-EE fused AODs, which are R = 0.88, RMSE = 0.20, and Bias = 0.022, is obviously improved. More importantly, in regions with fully missing original AODs, the accuracy of FRS-EE fused AODs is close to that of original AODs in regions with valid retrievals. Meanwhile, the time cost of FRS-EE for AOD fusion was only 2.91 h; obviously lower than the 30.46 months taken for BME. Full article

(This article belongs to the Special Issue Fusion of High-Level Remote Sensing Products)

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Review

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21 pages, 2455 KiB

Open AccessReview

Resolution Enhancement of Remotely Sensed Land Surface Temperature: Current Status and Perspectives

by Qi Mao, Jian Peng and Yanglin Wang

Remote Sens. 2021, 13(7), 1306; https://doi.org/10.3390/rs13071306 - 29 Mar 2021

Cited by 28 | Viewed by 4830

Abstract

Remotely sensed land surface temperature (LST) distribution has played a valuable role in land surface processes studies from local to global scales. However, it is still difficult to acquire concurrently high spatiotemporal resolution LST data due to the trade-off between spatial and temporal resolutions in thermal remote sensing. To address this problem, various methods have been proposed to enhance the resolutions of LST data, and substantial progress in this field has been achieved in recent years. Therefore, this study reviewed the current status of resolution enhancement methods for LST data. First, three groups of enhancement methods—spatial resolution enhancement, temporal resolution enhancement, and simultaneous spatiotemporal resolution enhancement—were comprehensively investigated and analyzed. Then, the quality assessment strategies for LST resolution enhancement methods and their advantages and disadvantages were specifically discussed. Finally, key directions for future studies in this field were suggested, i.e., synergy between process-driven and data-driven methods, cross-comparison among different methods, and improvement in localization strategy. Full article

(This article belongs to the Special Issue Fusion of High-Level Remote Sensing Products)

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