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Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery

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

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 46747

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Special Issue Editors

Dr. Fabio Castaldi

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Guest Editor

Institute of BioEconomy (IBE), National Research Council of Italy (CNR), Via Caproni 8, 50145 Firenze, Italy
Interests: soil imaging spectroscopy; multi- and hyperspectral remote sensing; precision agriculture; earth observation; geostatistics; sustainable agriculture; soil mapping; SOC; data fusion
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Anne Gobin

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Co-Guest Editor

1. Remote Sensing Unit, Flemish Institute for Technological Research, Boeretang 200, 2400 Mol, Belgium
2. Department of Earth & Environmental Sciences, Faculty of BioScience Engineering, Celestijnenlaan 200E, 3001 Heverlee, Belgium
Interests: water–soil–crop modelling; remote sensing; data mining
Special Issues, Collections and Topics in MDPI journals

Dr. Simone Pascucci

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Co-Guest Editor

Institute of Methodologies for Environmental Analysis (IMAA)-National Council of Research (CNR), C. da S. Loja, 85050 Tito Scalo, Italy
Interests: multi- and hyperspectral remote sensing for environmental and agricultural applications; imaging spectroscopy; airborne flight campaigns; sensor calibration and validation; ground segment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing amount of freely available satellite data is attracting new users outside the scientific community. Currently, we are experiencing a democratization of the Earth Observation (EO) data, which is largely due to the Copernicus Sentinel and NASA Landsat missions, as well as to the recent and rapid development of unmanned aircraft systems. At the same time, this plethora of satellite data offers new possibilities and challenges for EO scientists and experts. On one hand, the short revisit time (high temporal resolution) of the new generation satellite imagers allows the enhancement of multi-temporal analysis; on the other hand, the large variability of remote sensing data raises the issue of the implementation of data fusion techniques for big data. This variability concerns the type of sensor (optical, SAR, thermal, LIDAR, etc.), as well as the platform on which the sensor is placed (spaceborne, airborne, UAV). Multi-sensor data fusion techniques allow data from different sources to be combined, enriching and enhancing EO time series and, consequently, improving multi-temporal analysis.

This Special Issue will present a collection of valuable and rigorous research works that advance current knowledge on the multi-temporal and multi-source analysis of remote sensed imagery.

Specific topics include, but are not limited to

Multi-temporal image pre-processing and harmonization;
Implementation of multi-sensor and multi-temporal data fusion techniques;
Multi-temporal image analysis for the monitoring of dynamic factors, trend analysis, classification, clustering, and regression.

The above-listed topics can be applied to several dynamic applications (agriculture, geomorphology, soil, marine and freshwater environments, forest, land use change, biodiversity, climate change, environmental disasters, etc.). Any kind of sensor data (optical, SAR, LIDAR, TIR, etc.), as well as any kind of spectral, radiometric, spatial, or temporal resolution can be considered. The choice of papers for publication will be based on quality, soundness, and rigor of research.

Dr. Fabio Castaldi
Dr. Anne Gobin
Dr. Simone Pascucci
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Sentinel
Landsat
UAV
multi-temporal analysis
data fusion
time series
long- and short-term monitoring
multi-sensor
big data

Published Papers (12 papers)

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Research

23 pages, 4387 KiB

Open AccessArticle

Mapping the Distribution of Coffee Plantations from Multi-Resolution, Multi-Temporal, and Multi-Sensor Data Using a Random Forest Algorithm

by Anggun Tridawati, Ketut Wikantika, Tri Muji Susantoro, Agung Budi Harto, Soni Darmawan, Lissa Fajri Yayusman and Mochamad Firman Ghazali

Remote Sens. 2020, 12(23), 3933; https://doi.org/10.3390/rs12233933 - 01 Dec 2020

Cited by 23 | Viewed by 4743

Abstract

Indonesia is the world’s fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system. Full article

(This article belongs to the Special Issue Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery)

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21 pages, 11406 KiB

Open AccessArticle

A Hybrid Spatio-Temporal Prediction Model for Solar Photovoltaic Generation Using Numerical Weather Data and Satellite Images

by Bowoo Kim and Dongjun Suh

Remote Sens. 2020, 12(22), 3706; https://doi.org/10.3390/rs12223706 - 12 Nov 2020

Cited by 10 | Viewed by 2399

Abstract

Precise and accurate prediction of solar photovoltaic (PV) generation plays a major role in developing plans for the supply and demand of power grid systems. Most previous studies on the prediction of solar PV generation employed only weather data composed of numerical text data. The numerical text weather data can reflect temporal factors, however, they cannot consider the movement features related to the wind direction of the spatial characteristics, which include the amount of both clouds and particulate matter (PM) among other weather features. This study aims developing a hybrid spatio-temporal prediction model by combining general weather data and data extracted from satellite images having spatial characteristics. A model for hourly prediction of solar PV generation is proposed using data collected from a solar PV power plant in Incheon, South Korea. To evaluate the performance of the prediction model, we compared and performed ARIMAX analysis, which is a traditional statistical time-series analysis method, and SVR, ANN, and DNN, which are based on machine learning algorithms. The models that reflect the temporal and spatial characteristics exhibited better performance than those using only the general weather numerical data or the satellite image data. Full article

(This article belongs to the Special Issue Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery)

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18 pages, 7156 KiB

Open AccessArticle

An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFM

by Mengxue Liu, Xiangnan Liu, Xiaobin Dong, Bingyu Zhao, Xinyu Zou, Ling Wu and Hejie Wei

Remote Sens. 2020, 12(21), 3673; https://doi.org/10.3390/rs12213673 - 09 Nov 2020

Cited by 15 | Viewed by 3115

Abstract

The use of the spatiotemporal data fusion method as an effective data interpolation method has received extensive attention in remote sensing (RS) academia. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) is one of the most famous spatiotemporal data fusion methods, as it is widely used to generate synthetic data. However, the ESTARFM algorithm uses moving windows with a fixed size to get the information around the central pixel, which hampers the efficiency and precision of spatiotemporal data fusion. In this paper, a modified ESTARFM data fusion algorithm that integrated the surface spatial information via a statistical method was developed. In the modified algorithm, the local variance of pixels around the central one was used as an index to adaptively determine the window size. Satellite images from two regions were acquired by employing the ESTARFM and modified algorithm. Results showed that the images predicted using the modified algorithm obtained more details than ESTARFM, as the frequency of pixels with the absolute difference of mean value of six bands’ reflectance between true observed image and predicted between 0 and 0.04 were 78% by ESTARFM and 85% by modified algorithm, respectively. In addition, the efficiency of the modified algorithm improved and the verification test showed the robustness of the modified algorithm. These promising results demonstrated the superiority of the modified algorithm to provide synthetic images compared with ESTARFM. Our research enriches the spatiotemporal data fusion method, and the automatic selection of moving window strategy lays the foundation of automatic processing of spatiotemporal data fusion on a large scale. Full article

(This article belongs to the Special Issue Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery)

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23 pages, 8355 KiB

Open AccessArticle

A New Approach of Ensemble Learning Technique to Resolve the Uncertainties of Paddy Area through Image Classification

by Tsu Chiang Lei, Shiuan Wan, Shih-Chieh Wu and Hsin-Ping Wang

Remote Sens. 2020, 12(21), 3666; https://doi.org/10.3390/rs12213666 - 09 Nov 2020

Cited by 6 | Viewed by 2402

Abstract

Remote sensing technology has rendered lots of information in agriculture. It has usually been used to monitor paddy growing ecosystems in the past few decades. However, there are uncertainties in data fusion techniques which can be resolved in image classification on paddy rice. In this study, a series of learning concepts integrated by a probability progress Fuzzy Dempster-Shafer (FDS) analysis is presented to upgrade various models and different types of image data which is the goal of this study. More specifically, the study utilized the FDS to generate a series of probability models in the classification of the system. In addition, Logistic Regression (LR), Support Vector Machine (SVM), and Neural Network (NN) approaches are employed into the developed FDS system. Furthermore, two different image types are Satellite Image and Aerial Photo used as the analysis material. The overall classification accuracy has been improved to 97.27%, and the kappa value is 0.93. The overall accuracy of the paddy field image classification for a multi-period of mid-scale satellite images is between 85% and 90%. The overall accuracy of the classification using multi-spectral numerical aerial photos can be between 91% and 95%. The FDS improves the accuracy of the above image classification results. Full article

(This article belongs to the Special Issue Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery)

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28 pages, 11072 KiB

Open AccessArticle

Object-Based Multi-Temporal and Multi-Source Land Cover Mapping Leveraging Hierarchical Class Relationships

by Yawogan Jean Eudes Gbodjo, Dino Ienco, Louise Leroux, Roberto Interdonato, Raffaele Gaetano and Babacar Ndao

Remote Sens. 2020, 12(17), 2814; https://doi.org/10.3390/rs12172814 - 30 Aug 2020

Cited by 21 | Viewed by 3883

Abstract

European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at high spatial resolution and high revisit time, respectively, radar and optical images that support a wide range of Earth surface monitoring tasks, such as Land Use/Land Cover mapping. A long-standing challenge in the remote sensing community is about how to efficiently exploit multiple sources of information and leverage their complementarity, in order to obtain the most out of radar and optical data. In this work, we propose to deal with land cover mapping in an object-based image analysis (OBIA) setting via a deep learning framework designed to leverage the multi-source complementarity provided by radar and optical satellite image time series (SITS). The proposed architecture is based on an extension of Recurrent Neural Network (RNN) enriched via a modified attention mechanism capable to fit the specificity of SITS data. Our framework also integrates a pretraining strategy that allows to exploit specific domain knowledge, shaped as hierarchy over the set of land cover classes, to guide the model training. Thorough experimental evaluations, involving several competitive approaches were conducted on two study sites, namely the Reunion island and a part of the Senegalese groundnut basin. Classification results, 79% of global accuracy on the Reunion island and 90% on the Senegalese site, respectively, have demonstrated the suitability of the proposal. Full article

(This article belongs to the Special Issue Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery)

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24 pages, 5193 KiB

Open AccessArticle

Modeling and Multi-Temporal Characterization of Total Suspended Matter by the Combined Use of Sentinel 2-MSI and Landsat 8-OLI Data: The Pertusillo Lake Case Study (Italy)

by Emanuele Ciancia, Alessandra Campanelli, Teodosio Lacava, Angelo Palombo, Simone Pascucci, Nicola Pergola, Stefano Pignatti, Valeria Satriano and Valerio Tramutoli

Remote Sens. 2020, 12(13), 2147; https://doi.org/10.3390/rs12132147 - 04 Jul 2020

Cited by 26 | Viewed by 3798

Abstract

The total suspended matter (TSM) variability plays a crucial role in a lake’s ecological functioning and its biogeochemical cycle. Sentinel-2A MultiSpectral Instrument (MSI) and Landsat 8 Operational Land Instrument (OLI) data offer unique opportunities for investigating certain in-water constituents (e.g., TSM and chlorophyll-a) owing to their spatial resolution (10–60 m). In this framework, we assessed the potential of MSI–OLI combined data in characterizing the multi-temporal (2014–2018) TSM variability in Pertusillo Lake (Basilicata region, Southern Italy). We developed and validated a customized MSI-based TSM model (R² = 0.81) by exploiting ground measurements acquired during specific measurement campaigns. The model was then exported as OLI data through an intercalibration procedure (R² = 0.87), allowing for the generation of a TSM multi-temporal MSI–OLI merged dataset. The analysis of the derived multi-year TSM monthly maps showed the influence of hydrological factors on the TSM seasonal dynamics over two sub-regions of the lake, the west and east areas. The western side is more influenced by inflowing rivers and water level fluctuations, the effects of which tend to longitudinally decrease, leading to less sediment within the eastern sub-area. The achieved results can be exploited by regional authorities for better management of inland water quality and monitoring systems. Full article

(This article belongs to the Special Issue Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery)

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16 pages, 4787 KiB

Open AccessArticle

ImaACor: A Physically Based Tool for Combined Atmospheric and Topographic Corrections of Remote Sensing Images

by Angelo Palombo and Federico Santini

Remote Sens. 2020, 12(13), 2076; https://doi.org/10.3390/rs12132076 - 28 Jun 2020

Cited by 7 | Viewed by 2591 | Correction

Abstract

ImaACor is a tool for the simultaneous correction of remotely sensed data from atmospheric and topographic effects, including second-order corrections, such as adjacency effects. The implemented approach is physically based and uses MODTRAN for computation of the main radiometric quantities. A user-friendly, comprehensive, and exhaustive graphic interface allows the user to choose from different correction levels. The various panels allow one to set all the parameters to correctly characterize the atmosphere and define the acquisition and illumination geometries. The tool provides a number of facilities to easily manage the correction process for a wide range of sensor data, including the ability to process multiple data in batch mode, which is very useful for dealing with temporal series. Under the inclusion of topographic correction, this tool allows the user to select a digital elevation model that is automatically resampled to the image resolution and processed to obtain the parameters for radiometric transfer modeling and the subsequent harmonization of the surface with the model inversion. This tool also includes utilities for the pre-processing of PRISMA data. Full article

(This article belongs to the Special Issue Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery)

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28 pages, 7957 KiB

Open AccessFeature PaperArticle

Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany

by Maninder Singh Dhillon, Thorsten Dahms, Carina Kuebert-Flock, Erik Borg, Christopher Conrad and Tobias Ullmann

Remote Sens. 2020, 12(11), 1819; https://doi.org/10.3390/rs12111819 - 04 Jun 2020

Cited by 19 | Viewed by 4734

Abstract

This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R² (>0.82), low RMSE (<600 g/m²) and significant p-value (<0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R² (<0.68) and high RMSE (>600 g/m²). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat). Full article

(This article belongs to the Special Issue Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery)

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21 pages, 3599 KiB

Open AccessArticle

Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2

by Elahe Akbari, Ali Darvishi Boloorani, Najmeh Neysani Samany, Saeid Hamzeh, Saeid Soufizadeh and Stefano Pignatti

Remote Sens. 2020, 12(9), 1449; https://doi.org/10.3390/rs12091449 - 03 May 2020

Cited by 40 | Viewed by 4869

Abstract

Timely and accurate information on crop mapping and monitoring is necessary for agricultural resources management. Accordingly, the applicability of the proposed classification-feature selection ensemble procedure with different feature sets for crop mapping is investigated. Here, we produced various feature sets including spectral bands, spectral indices, variation of spectral index, texture, and combinations of features to map different types of crops. By using various feature sets and the random forest (RF) classifier, the crop maps were created. In aiming to determine the most relevant and distinctive features, the particle swarm optimization (PSO) and RF-variable importance measure feature selection methods were examined. The classification-feature selection ensemble procedure was adapted to combine the outputs of different feature sets from the better feature selection method using majority votes. Multi-temporal Sentinel-2 data has been used in Ghale-Nou county of Tehran, Iran. The performance of RF was efficient in crop mapping especially by spectral bands and texture in combination with other feature sets. Our results showed that the PSO-based feature selection leads to a more accurate classification than the RF-variable importance measure, in almost all feature sets for all crop types. The RF classifier-PSO ensemble procedure for crop mapping outperformed the RF classifier in each feature set with regard to the class-wise and overall accuracies (OA) (of about 2.7–7.4% increases in OA and 0.48–3.68% (silage maize), 0–1.61% (rice), 2.82–15.43% (alfalfa), and 10.96–41.13% (vegetables) improvement in F-scores for all feature sets). The proposed method could mainly be useful to differentiate between heterogeneous crop fields (e.g., vegetables in this study) due to their more obtained omission/commission errors reduction. Full article

(This article belongs to the Special Issue Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery)

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16 pages, 3793 KiB

Open AccessArticle

Generating High Resolution LAI Based on a Modified FSDAF Model

by Huan Zhai, Fang Huang and Hang Qi

Remote Sens. 2020, 12(1), 150; https://doi.org/10.3390/rs12010150 - 02 Jan 2020

Cited by 16 | Viewed by 3519

Abstract

Leaf area index (LAI) is an important parameter for monitoring the physical and biological processes of vegetation canopy. Due to the constraints of cloud contamination, snowfall, and instrument conditions, most of the current satellite remote sensing LAI products have lower resolution that cannot satisfy the needs of vegetation remote sensing application in areas of high heterogeneity. We proposed a new model to generate high resolution LAI, by combining linear pixel unmixing and the Flexible Spatiotemporal Data Fusion (FSDAF) method. This method derived the input data of FSDAF by downscaling the MODIS (Moderate Resolution Imaging Spectroradiometer) data with a linear spectral mixture model. Through the improved input parameters of the algorithm, the fusion of MODIS LAI and LAI at Landsat spatial resolution estimated by Support Vector Regression model was realized. The fusion accuracy of generated LAI data was validated based on Sentinel-2 LAI products. The results showed that strong correlation between predicted LAI and Sentinel-2 LAI in sample sites was observed with higher correlation coefficients and lower Root Mean Square Error. Compared to the simulation results of FSDAF, the modified FSDAF model showed higher accuracy and reflected more spatial details in the boundary areas of different land cover types. Full article

(This article belongs to the Special Issue Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery)

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27 pages, 18435 KiB

Open AccessArticle

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

by Maolin Liu, Yinghai Ke, Qi Yin, Xiuwan Chen and Jungho Im

Remote Sens. 2019, 11(22), 2612; https://doi.org/10.3390/rs11222612 - 07 Nov 2019

Cited by 41 | Viewed by 4336

Abstract

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications. Full article

(This article belongs to the Special Issue Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery)

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17 pages, 20902 KiB

Open AccessArticle

Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery

by Hanna Huryna, Yafit Cohen, Arnon Karnieli, Natalya Panov, William P. Kustas and Nurit Agam

Remote Sens. 2019, 11(19), 2304; https://doi.org/10.3390/rs11192304 - 03 Oct 2019

Cited by 27 | Viewed by 4877

Abstract

A spatially distributed land surface temperature is important for many studies. The recent launch of the Sentinel satellite programs paves the way for an abundance of opportunities for both large area and long-term investigations. However, the spatial resolution of Sentinel-3 thermal images is not suitable for monitoring small fragmented fields. Thermal sharpening is one of the primary methods used to obtain thermal images at finer spatial resolution at a daily revisit time. In the current study, the utility of the TsHARP method to sharpen the low resolution of Sentinel-3 thermal data was examined using Sentinel-2 visible-near infrared imagery. Compared to Landsat 8 fine thermal images, the sharpening resulted in mean absolute errors of ~1 °C, with errors increasing as the difference between the native and the target resolutions increases. Part of the error is attributed to the discrepancy between the thermal images acquired by the two platforms. Further research is due to test additional sites and conditions, and potentially additional sharpening methods, applied to the Sentinel platforms. Full article

(This article belongs to the Special Issue Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery)

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