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Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remotely Sensed Imagery II

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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 (15 January 2023) | Viewed by 24053

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Special Issue Editors

Dr. Fabio Castaldi

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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

Dr. Simone Pascucci

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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 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 multitemporal 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). Multisensor data fusion techniques allow data from different sources to be combined, enriching and enhancing EO time series and, consequently, improving multitemporal analysis.

This Special Issue will present a collection of valuable and rigorous research works that advance current knowledge on the multitemporal and multisource analysis of remotely sensed imagery.

Specific topics include but are not limited to:

Multitemporal image preprocessing and harmonization;
Implementation of multisensor and multitemporal data fusion techniques;
Multitemporal 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. 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
Multitemporal analysis
Data fusion
Time series
Long- and short-term monitoring
Multisensor
Big data
PRISMA

Published Papers (6 papers)

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Research

35 pages, 13244 KiB

Open AccessArticle

Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany

by Maninder Singh Dhillon, Thorsten Dahms, Carina Kübert-Flock, Adomas Liepa, Thomas Rummler, Joel Arnault, Ingolf Steffan-Dewenter and Tobias Ullmann

Remote Sens. 2023, 15(6), 1651; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15061651 - 18 Mar 2023

Cited by 3 | Viewed by 3675

Abstract

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km²), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R² of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R² of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R² of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R² = 0.88) and OSR (R² = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R² of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively. Full article

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

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27 pages, 5384 KiB

Open AccessArticle

Fused Thermal and RGB Imagery for Robust Detection and Classification of Dynamic Objects in Mixed Datasets via Pre-Trained High-Level CNN

by Ravit Ben-Shoushan and Anna Brook

Remote Sens. 2023, 15(3), 723; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030723 - 26 Jan 2023

Cited by 2 | Viewed by 2895

Abstract

Smart vehicles with embedded Autonomous Vehicle (AV) technologies are currently equipped with different types of mounted sensors, aiming to ensure safe movement for both passengers and other road users. The sensors’ ability to capture and gather data to be synchronically interpreted by neural networks for a clear understanding of the surroundings is influenced by lighting conditions, such as natural lighting levels, artificial lighting effects, time of day, and various weather conditions, such as rain, fog, haze, and extreme temperatures. Such changing environmental conditions are also known as complex environments. In addition, the appearance of other road users is varied and relative to the vehicle’s perspective; thus, the identification of features in a complex background is still a challenge. This paper presents a pre-processing method using multi-sensorial RGB and thermal camera data. The aim is to handle issues arising from the combined inputs of multiple sensors, such as data registration and value unification. Foreground refinement, followed by a novel statistical anomaly-based feature extraction prior to image fusion, is presented. The results met the AV challenges in CNN’s classification. The reduction of the collected data and its variation level was achieved. The unified physical value contributed to the robustness of input data, providing a better perception of the surroundings under varied environmental conditions in mixed datasets for day and night images. The method presented uses fused images, robustly enriched with texture and feature depth and reduced dependency on lighting or environmental conditions, as an input for a CNN. The CNN was capable of extracting and classifying dynamic objects as vehicles and pedestrians from the complex background in both daylight and nightlight images. Full article

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

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18 pages, 8246 KiB

Open AccessArticle

Impact of the Dates of Input Image Pairs on Spatio-Temporal Fusion for Time Series with Different Temporal Variation Patterns

by Aojie Shen, Yanchen Bo, Wenzhi Zhao and Yusha Zhang

Remote Sens. 2022, 14(10), 2431; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102431 - 19 May 2022

Cited by 1 | Viewed by 1591

Abstract

Dense time series of remote sensing images with high spatio-temporal resolution are critical for monitoring land surface dynamics in heterogeneous landscapes. Spatio-temporal fusion is an effective solution to obtaining such time series images. Many spatio-temporal fusion methods have been developed for producing high spatial resolution images at frequent intervals by blending fine spatial images and coarse spatial resolution images. Previous studies have revealed that the accuracy of fused images depends not only on the fusion algorithm, but also on the input image pairs being used. However, the impact of input images dates on the fusion accuracy for time series with different temporal variation patterns remains unknown. In this paper, the impact of input image pairs on the fusion accuracy for monotonic linear change (MLC), monotonic non-linear change (MNLC), and non-monotonic change (NMC) time periods were evaluated, respectively, and the optimal selection strategies of input image dates for different situations were proposed. The 16-day composited NDVI time series (i.e., Collection 6 MODIS NDVI product) were used to present the temporal variation patterns of land surfaces in the study areas. To obtain sufficient observation dates to evaluate the impact of input image pairs on the spatio-temporal fusion accuracy, we utilized the Harmonized Landsat-8 Sentinel-2 (HLS) data. The ESTARFM was selected as the spatio-temporal fusion method for this study. The results show that the impact of input image date on the accuracy of spatio-temporal fusion varies with the temporal variation patterns of the time periods being fused. For the MLC period, the fusion accuracy at the prediction date (PD) is linearly correlated to the time interval between the change date (CD) of the input image and the PD, but the impact of the input image date on the fusion accuracy at the PD is not very significant. For the MNLC period, the fusion accuracy at the PD is non-linearly correlated to the time interval between the CD and the PD, the impact of the time interval between the CD and the PD on the fusion accuracy is more significant for the MNLC than for the MLC periods. Given the similar change of time intervals between the CD and the PD, the increments of R² of fusion result for the MNLC is over ten times larger than those for the MLC. For the NMC period, a shorter time interval between the CD and the PD does not lead to higher fusion accuracies. On the contrary, it may lower the fusion accuracy. This study suggests that temporal variation patterns of the data must be taken into account when selecting optimal dates of input images in the fusion model. Full article

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

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22 pages, 27897 KiB

Open AccessArticle

Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data

by Nada Mzid, Fabio Castaldi, Massimo Tolomio, Simone Pascucci, Raffaele Casa and Stefano Pignatti

Remote Sens. 2022, 14(3), 714; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030714 - 02 Feb 2022

Cited by 30 | Viewed by 4678

Abstract

The PRISMA satellite is equipped with an advanced hyperspectral Earth observation technology capable of improving the accuracy of quantitative estimation of bio-geophysical variables in various Earth Science Applications and in particular for soil science. The purpose of this research was to evaluate the ability of the PRISMA hyperspectral imager to estimate topsoil properties (i.e., organic carbon, clay, sand, silt), in comparison with current satellite multispectral sensors. To investigate this expectation, a test was carried out using topsoil data collected in Italy following two approaches. Firstly, PRISMA, Sentinel-2 and Landsat 8 spectral simulated datasets were obtained from the spectral resampling of a laboratory soil library. Subsequently, bare soil reflectance data were obtained from two experimental areas in Italy, using real satellites images, at dates close to each other. The estimation models of soil properties were calibrated employing both Partial Least Square Regression and Cubist Regression algorithms. The results of the study revealed that the best accuracies in retrieving topsoil properties were obtained by PRISMA data, using both laboratory and real datasets. Indeed, the resampled spectra of the hyperspectral imager provided the best Ratio of Performance to Inter-Quartile distance (RPIQ) for clay (4.87), sand (3.80), and organic carbon (2.59) estimation, for the spectral soil library datasets. For the bare soil reflectance obtained from real satellite imagery, a higher level of prediction accuracy was obtained from PRISMA data, with RPIQ ± SE values of 2.32 ± 0.07 for clay, 3.85 ± 0.19 for silt, and 3.51 ± 0.16 for soil organic carbon. The results for the PRISMA hyperspectral satellite imagery with the Cubist Regression provided the best performance in the prediction of silt, sand, clay and SOC. The same variables were better estimated using PLSR models in the case of the resampled hyperspectral data. The statistical accuracy in the retrieval of SOC from real and resampled PRISMA data revealed the potential of the actual hyperspectral satellite. The results supported the expected good ability of the PRISMA imager to estimate topsoil properties. Full article

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

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25 pages, 6061 KiB

Open AccessArticle

Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria

by Maninder Singh Dhillon, Thorsten Dahms, Carina Kübert-Flock, Ingolf Steffan-Dewenter, Jie Zhang and Tobias Ullmann

Remote Sens. 2022, 14(3), 677; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030677 - 31 Jan 2022

Cited by 17 | Viewed by 5120

Abstract

The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R² = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R² = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R² = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R² = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R² = 0.60, RMSE = 0.05) and S-MOD13Q1 (R² = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution. Full article

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

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15 pages, 2301 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands

by Fabio Castaldi

Remote Sens. 2021, 13(17), 3345; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173345 - 24 Aug 2021

Cited by 21 | Viewed by 4687

Abstract

The spatial and temporal monitoring of soil organic carbon (SOC), and other soil properties related to soil erosion, is extremely important, both from the environmental and economic perspectives. Sentinel-2 (S2) and Landsat-8 (L8) time series increase the probability to observe bare soil fields in croplands, and thus, monitor soil properties over large regions. In this regard, this work suggests an automated pixel-based approach to select only pure soil pixels in S2 and L8 time series, and to make a synthetic bare soil image (SBSI). The SBSIs and the soil properties measured in the framework of the European LUCAS survey were used to calibrate SOC, clay, and CaCO₃ prediction models. The results highlight a high correlation between laboratory soil spectra and the SBSIs median spectra, especially for the SBSI obtained by a three-year S2 collection, which provides satisfactory results in terms of SOC prediction accuracy (RPD: 1.74). The comparison between S2 and L8 results demonstrated the higher capability of the S2 sensor in terms of SOC prediction accuracy, mainly due to the greater spatial resolution of the bands in the visible region. Whereas, neither S2 nor L8 could accurately predict the clay and CaCO₃ content. This is because of the low spectral and spatial resolution of their SWIR bands that prevent the exploitation of the narrow spectral features related to these two soil attributes. The results of this study prove that large S2 time series can estimate and monitor SOC in croplands using an automated pixel-based approach that selects pure soil pixels and retrieves reliable synthetic soil spectra. Full article

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

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