Application Of Imagery Research Articles

Application of Sentinel-2 imagery for total suspended solids mapping off the Bodri River, Kendal Regency, Indonesia

Total suspended solids (TSS) is one of the key parameters in water quality monitoring. TSS dynamics off the seas of Bodri River can be observed using Sentinel-2. This research validates Wirasatriya and Laili's algorithm and redevelops a TSS retrieval algorithm based on in situ TSS and Sentinel-2 reflectance (Rrs). During the satellite's passing time, water sampling for TSS was conducted at 64 stations at a depth of 0.5m in October 2023. Bottom of Atmosphere (BoA) Rrs data were obtained from SNAP software, which had previously performed image cropping and resampling. Rrs data from all bands were initially correlated with in situ TSS. The validation results of Wirasatriya and Laili's algorithms obtained very high of RMSE values reaching 272.23 mg/L and 33.08 mg//L. While the results of generating the algorithm produced better the performance of the algorithmic model with RMSE = 7.34 mg/L, MAPE = 14.24%, and bias = 0.99. The development of the algorithms in the study based on the green band (B3) resulted in the equation: TSS (mg/L) = 4.6698exp10.609∗B3, which was further used for temporal mapping. High TSS was found in nearshore areas and river mouths influenced by shallow waters. Temporally, high TSS was found in October (55.65 mg/L) and low in May (11 mg/L). The temporal variation pattern is influenced by precipitation and tides. The findings of this algorithm are very important for analyzing the evolution of land accretion in the Bodri River estuary and as a review for land management in upland areas and can be used to monitor other shallow areas.

Advancing primate surveillance with image recognition techniques from unmanned aerial vehicles.

Using unmanned aerial vehicles (UAVs) for surveys on thermostatic animals has gained prominence due to their ability to provide practical and precise dynamic censuses, contributing to developing and refining conservation strategies. However, the practical application of UAVs for animal monitoring necessitates the automation of image interpretation to enhance their effectiveness. Based on our past experiences, we present the Sichuan snub-nosed monkey (Rhinopithecus roxellana) as a case study to illustrate the effective use of thermal cameras mounted on UAVs for monitoring monkey populations in Qinling, a region characterized by magnificent biodiversity. We used the local contrast method for a small infrared target detection algorithm to collect the total population size. Through the experimental group, we determined the average optimal grayscale threshold, while the validation group confirmed that this threshold enables automatic detection and counting of target animals in similar datasets. The precision rate obtained from the experiments ranged from 85.14% to 97.60%. Our findings reveal a negative correlation between the minimum average distance between thermal spots and the count of detected individuals, indicating higher interference in images with closer thermal spots. We propose a formula for adjusting primate population estimates based on detection rates obtained from UAV surveys. Our results demonstrate the practical application of UAV-based thermal imagery and automated detection algorithms for primate monitoring, albeit with consideration of environmental factors and the need for data preprocessing. This study contributes to advancing the application of UAV technology in wildlife monitoring, with implications for conservation management and research.

High-resolution mapping of urban Aedes aegypti immature abundance through breeding site detection based on satellite and street view imagery

Identification of Aedes aegypti breeding hotspots is essential for the implementation of targeted vector control strategies and thus the prevention of several mosquito-borne diseases worldwide. Training computer vision models on satellite and street view imagery in the municipality of Rio de Janeiro, we analyzed the correlation between the density of common breeding grounds and Aedes aegypti infestation measured by ovitraps on a monthly basis between 2019 and 2022. Our findings emphasized the significance (p ≤ 0.05) of micro-habitat proxies generated through object detection, allowing to explain high spatial variance in urban abundance of Aedes aegypti immatures. Water tanks, non-mounted car tires, plastic bags, potted plants, and storm drains positively correlated with Aedes aegypti egg and larva counts considering a 1000 m mosquito flight range buffer around 2700 ovitrap locations, while dumpsters, small trash bins, and large trash bins exhibited a negative association. This complementary application of satellite and street view imagery opens the pathway for high-resolution interpolation of entomological surveillance data and has the potential to optimize vector control strategies. Consequently it supports the mitigation of emerging infectious diseases transmitted by Aedes aegypti, such as dengue, chikungunya, and Zika, which cause thousands of deaths each year.

Predicting Grapevine Physiological Parameters Using Hyperspectral Remote Sensing Integrated with Hybrid Convolutional Neural Network and Ensemble Stacked Regression

Grapevine rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhance scion physiology. Direct leaf-level physiological parameters like net assimilation rate, stomatal conductance to water vapor, quantum yield of PSII, and transpiration can illuminate the rootstock effect on scion physiology. However, these measures are time-consuming and limited to leaf-level analysis. This study used different rootstocks to investigate the potential application of aerial hyperspectral imagery in the estimation of canopy level measurements. A statistical framework was developed as an ensemble stacked regression (REGST) that aggregated five different individual machine learning algorithms: Least absolute shrinkage and selection operator (Lasso), Partial least squares regression (PLSR), Ridge regression (RR), Elastic net (ENET), and Principal component regression (PCR) to optimize high-throughput assessment of vine physiology. In addition, a Convolutional Neural Network (CNN) algorithm was integrated into an existing REGST, forming a hybrid CNN-REGST model with the aim of capturing patterns from the hyperspectral signal. Based on the findings, the performance of individual base models exhibited variable prediction accuracies. In most cases, Ridge Regression (RR) demonstrated the lowest test Root Mean Squared Error (RMSE). The ensemble stacked regression model (REGST) outperformed the individual machine learning algorithms with an increase in R2 by (0.03 to 0.1). The performances of CNN-REGST and REGST were similar in estimating the four different traits. Overall, these models were able to explain approximately 55–67% of the variation in the actual ground-truth data. This study suggests that hyperspectral features integrated with powerful AI approaches show great potential in tracing functional traits in grapevines.

The application of Sentinel-2 satellite imagery to construct a model to estimate the concentration of Chlorophyll-a in surface water in the Hinh River basin, Vietnam

The application of Sentinel-2 satellite imagery to construct a model to estimate the concentration of Chlorophyll-a in surface water in the Hinh River basin, Vietnam

Application of Multi-Temporal and Multisource Satellite Imagery in the Study of Irrigated Landscapes in Arid Climates

The study of ancient irrigation is crucial in the archaeological research of arid regions. It covers a wide range of topics, with the Near East being the focus for decades. However, political instability and limited data have posed challenges to these studies. The primary objective is to establish a standardised method applicable to different arid environments using the Google Earth Engine platform, considering local relief of terrain and seasonal differences in vegetation. This study integrates multispectral data from LANDSAT 5, Sentinel-2, SAR imagery from Sentinel 1, and TanDEM-X (12 m and 30 m) DSMs. Using these datasets, calculations of selected vegetation indices such as the SMTVI and NDVSI, spectral decomposition methods such as TCT and PCA, and topography-based methods such as the MSRM contribute to a comprehensive understanding of landscape irrigation. This paper investigates the influence of modern environmental conditions on the visibility of features like levees and palaeo-channels by testing different methods and parameters. This study aims to identify the most effective approach for each case study and explore the possibility of applying a consistent method across all areas. Optimal results are achieved by combining several methods, adjusting seasonal parameters, and conducting a comparative analysis of visible features.

Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model

Abstract With the continuous development of Earth Observation technology, resolution of imagery and gridded data has significantly increased, leading to a rapid increase in data volume. To efficiently acquire and analyze these vast amounts of imagery and gridded data, image tiling technology has been developed to effectively access data of interested areas. Tiling technology divides large-scale image data into smaller tiles, providing fast, accurate, and efficient access support for imagery and gridded data. The spatial grid model, as the foundational framework of the new generation of geographic spatial information, plays a critical role in the retrieval, integration, services, and applications of imagery and gridded data resources. When tiling image data based on the spatial grid model, it always generates both complete and incomplete tiles. Particularly, when conducting image tile retrieval using the same rule-based grid in the retrieval area, incomplete tile phenomena along the boundary regions of the retrieved images often occur, resulting in gaps within the retrieval area. To tackle this issue, in this study, we present a new topological model called the Grid Six-Intersection Model (G-6IM), specifically designed for regular rectangular grids, to accurately represent boundary issues in image tiling. Through a practical case study, we demonstrate the effectiveness and practical application potential of the proposed G-6IM model, providing new insights and guidance for the improvement and optimization of imagery and gridded data tiling technology.

Drone-Acquired Short-Wave Infrared (SWIR) Imagery in Landscape Archaeology: An Experimental Approach

Many rocks, minerals, and soil types reflect short-wave infrared (SWIR) imagery (900–2500 nm) in distinct ways, and geologists have long relied on this property to aid in the mapping of differing surface lithologies. Although surface archaeological features including artifacts, anthrosols, or structural remains also likely reflect SWIR wavelengths of light in unique ways, archaeological applications of SWIR imagery are rare, largely due to the low spatial resolution and high acquisition costs of these data. Fortunately, a new generation of compact, drone-deployable sensors now enables the collection of ultra-high-resolution (<10 cm), hyperspectral (>100 bands) SWIR imagery using a consumer-grade drone, while the analysis of these complex datasets is now facilitated by powerful imagery-processing software packages. This paper presents an experimental effort to develop a methodology that would allow archaeologists to collect SWIR imagery using a drone, locate surface artifacts in the resultant data, and identify different artifact types in the imagery based on their reflectance values across the 900–1700 nm spectrum. Our results illustrate both the potential of this novel approach to exploring the archaeological record, as we successfully locate and characterize many surface artifacts in our experimental study, while also highlighting challenges in successful data collection and analysis, largely related to current limitations in sensor and drone technology. These findings show that as underlying hardware sees continued improvements in the coming years, drone-acquired SWIR imagery can become a powerful tool for the discovery, documentation, and analysis of archaeological landscapes.

Advancements in Remote Sensing Imagery Applications for Precision Management in Olive Growing: A Systematic Review

This systematic review explores the role of remote sensing technology in addressing the requirements of sustainable olive growing, set against the backdrop of growing global food demands and contemporary environmental constraints in agriculture. The critical analysis presented in this document assesses different remote sensing platforms (satellites, manned aircraft vehicles, unmanned aerial vehicles and terrestrial equipment) and sensors (RGB, multispectral, thermal, hyperspectral and LiDAR), emphasizing their strategic selection based on specific study aims and geographical scales. Focusing on olive growing, particularly prominent in the Mediterranean region, this article analyzes the diverse applications of remote sensing, including the management of inventory and irrigation; detection/monitoring of diseases and phenology; and estimation of crucial parameters regarding biophysical parameters, water stress indicators, crop evapotranspiration and yield. Through a global perspective and insights from studies conducted in diverse olive-growing regions, this review underscores the potential benefits of remote sensing in shaping and improving sustainable agricultural practices, mitigating environmental impacts and ensuring the economic viability of olive trees.

Application of satellite imagery for vector-borne disease monitoring in sub-Saharan Africa: An overview

This work presents an overview of the application of satellite imagery or remote sensing (RS) data for vector-borne disease (VBD) monitoring in sub-Saharan Africa. We discussed the different vector-borne disease conditions that are prevalent and endemic in parts of sub-Saharan Africa and how satellite imagery (RS data) can be used in monitoring these conditions. Key disease conditions considered include malaria, human African trypanosomiasis, leishmaniasis, lymphatic filariasis, loa loa filariasis, rift valley fever, dengue, yellow fever, and rickettsioses. Furthermore, we explored some of the current ways remote sensing data and geographical information systems (GIS) are being applied to monitoring these diseases. We discuss the efficacy of using strong spatial modelling techniques combined with RS data to enhance our comprehension of the role that environmental conditions play in influencing VBD vectors and transmission alongside the utilization of GIS/RS technologies for vector-borne illness surveillance, prevention, and control. Finally, we discussed how drone technology and new remote sensing platforms can provide improved monitoring and the impact of such improvements from a population health standpoint.

Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning

Multi-domain learning (MDL) aims to train a model with minimal average risk across multiple overlapping but non-identical domains. To tackle the challenges of dataset bias and domain domination, numerous MDL approaches have been proposed from the perspectives of seeking commonalities by aligning distributions to reduce domain gap or reserving differences by implementing domain-specific towers, gates, and even experts. MDL models are becoming more and more complex with sophisticated network architectures or loss functions, introducing extra parameters and enlarging computation costs. In this paper, we propose a frustratingly easy and hyperparameter-free multi-domain learning method named Decoupled Training (D-Train). D-Train is a tri-phase general-to-specific training strategy that first pre-trains on all domains to warm up a root model, then post-trains on each domain by splitting into multi-heads, and finally fine-tunes the heads by fixing the backbone, enabling decouple training to achieve domain independence. Despite its extraordinary simplicity and efficiency, D-Train performs remarkably well in extensive evaluations of various datasets from standard benchmarks to applications of satellite imagery and recommender systems.

The application of PRISMA hyperspectral satellite imagery in the delineation of distinct hydrothermal alteration zones in the Chilean Andes: The Marimaca IOCG and the Río Blanco-Los Bronces Cu-Mo porphyry districts

This study provides an overview of the application of hyperspectral imagery acquired by the Italian satellite mission “PRecursore IperSpettrale della Missione Applicativa” (PRISMA), for mapping hydrothermal and supergene mineral alteration zones associated with different Cu deposits in the Chilean Andes. Study areas include the Marimaca Copper Project in the Naguayán district (Antofagasta Province), hosting Late Jurassic – Early Cretaceous iron oxide-copper–gold mineralizations and the Río Blanco-Los Bronces district (Santiago Region) known for its Late Miocene – Early Pliocene copper-molybdenum porphyry deposits. These mineral systems exhibit hydrothermal alteration haloes extending over several kilometers, with upward and outward mineral zonation.To characterize the surface-exposed alteration, the satellite imagery was processed using a multiple feature extraction workflow targeting the relative abundances and compositions of specific supergene and hydrothermal alteration minerals, including Fe-oxides and hydroxides (hematite-goethite), phyllosilicates (micas-kaolinite-chlorite), hydroxyl-bearing sulfates (alunite-natroalunite-jarosite) and epidote. The results reveal a zonation from proximal sulfates (natroalunite to alunite) and Al-rich white mica (ranging from 0.3 km-wide for the area identified to the east of the Marimaca deposit to 1 km-wide in correspondence of the Los Sulfatos deposit, respectively) to distal Al-poor white mica (0.2 to 0.5 km-wide, respectively) and an outer chlorite-epidote zone. In the Marimaca Project area, the structurally-controlled alteration evolves west to east from chlorite-rich to phengitic white mica (Al-poor). A NNE-SSW trending supergene leached cap covers a 6 km-long and up to 2 km-wide area from north to south. Our study documents how spaceborne hyperspectral imaging spectroscopy can support mineral exploration by enabling non-invasive reconnaissance mapping of the outcropping rocks, providing specific targeting information for follow-up field surveys and drilling campaigns.

A two-stage transformer based network for motor imagery classification

Brain-computer interfaces (BCIs) are used to understand brain functioning and develop therapies for neurological and neurodegenerative disorders. Therefore, BCIs are crucial in rehabilitating motor dysfunction and advancing motor imagery applications. For motor imagery, electroencephalogram (EEG) signals are used to classify the subject's intention of moving a body part without actually moving it. This paper presents a two-stage transformer-based architecture that employs handcrafted features and deep learning techniques to enhance the classification performance on benchmarked EEG signals. Stage-1 is built on parallel convolution based EEGNet, multi-head attention, and separable temporal convolution networks for spatiotemporal feature extraction. Further, for enhanced classification, in stage-2, additional features and embeddings extracted from stage-1 are used to train TabNet. In addition, a novel channel cluster swapping data augmentation technique is also developed to handle the issue of limited samples for training deep learning architectures. The developed two-stage architecture offered an average classification accuracy of 88.5 % and 88.3 % on the BCI Competition IV-2a and IV-2b datasets, respectively, which is approximately 3.0 % superior over similar recent reported works.

Picture this! Suggested instructions for guiding the Neuroscience of action imagery: A commentary on Krüger et al. (2022).

Our commentary expands the multisensory and modulating factors proposed by Kruger et al.'s (2023) internal models of action imagery and sensory crossovers. We will discuss the essence of imagery experiences as conceptual intersections among sensory, movement and affective properties that require further neuro-anatomical-contextual mapping to better understand the practical application of imagery. Accordingly, we will propose alternative ideas of daisy-chaining and motor imagery systems. The role of imagery speed, and other properties of movement for refining movement and self-regulation will be considered along with sex as a modulating factor in intra-individual abilities to image movement.

SEEK+: Securing vehicle GPS via a sequential dashcam-based vehicle localization framework

Nowadays, the Global Positioning System (GPS) plays an critical role in providing navigational services for transportation and a variety of other location-dependent applications. However, the emergent threat of GPS spoofing attacks compromises the safety and reliability of these systems. In response, this study introduces a cutting-edge computer vision-based methodology, the SEquential dashcam-based vEhicle localization frameworK Plus (SEEK+), designed to counteract GPS spoofing. By analyzing dashcam footage to ascertain a vehicle’s actual location, SEEK+ scrutinizes the authenticity of reported GPS data, effectively identifying spoofing incidents. The application of dashcam imagery for localization, however, presents inherent obstacles, such as adverse lighting and weather conditions, seasonal and temporal image variations, obstructions within the camera’s field of view, and fluctuating vehicle velocities. To overcome these issues, SEEK+ integrates innovative strategies within its framework, demonstrating superior efficacy over existing approaches with a notable detection accuracy rate of up to 94%.

Application of Multi-Temporal Landsat Imagery and GIS in Analyzing Land Use/Cover Changes in Abakaliki Local Government Area, Ebonyi State, Nigeria From 2000 to 2022

Land use land cover (LULC) change analysis is critical for understanding the effects of human activities on the environment. This study applied object-based image classification to High-Resolution Multi-Temporal Landsat imagery to analyse the LULC patterns in Abakaliki Local Government Area, Ebonyi State, Nigeria between 2000 and 2022. . Classification accuracies were validated using ground-reference data, yielding overall accuracy exceeding 95% for both time periods. Results revealed significant alterations in LULC composition over the 22-year interval. Specifically, vegetation cover declined substantially from 65.1% to 25.54% as bare land and built-up area expanded dramatically, increasing their coverage by over 25% each. These quantified shifts provide clear evidence of intensive urbanization and associated deforestation impacts. The high-fidelity LULC maps produced establish an empirical baseline for ongoing monitoring of environmental changes in the study area. Discriminating four classes with high classification performance (user's/producer's accuracy 87-100%) confirms the robustness of the object-based methodology. Key recommendations stemming from this research include leveraging the spatial datasets to model ecological effects and inform conservation planning through evidence-based strategies. Regular repetition of the mapping process is also advised to continuously track landscape transformations, assess policy interventions and guide development initiatives amid ongoing urban growth pressures across Ebonyi State, Nigeria.
 
 Received: 10 January 2023 / Accepted: 25 February 2024 / Published: 6 March 2024

Vegetation indices monitoring by using copernicus data in the old-growth forests of the Republic of Srpska/Bosnia and Herzegovina

IntroductionOld-growth forests (OGFs) are vital for global biodiversity, acting as irreplaceable carbon sinks, fostering ecosystem stability, and preserving unique ecological and cultural heritage. There are three old-growth forests in the Republic of Srpska/Bosnia and Herzegovina (Lom, Janj, and Perućica). They are dominated by mixed forests of beech, fir, and spruce (Piceo-Abieti-Fagetim illyricum). The satellite imagery results confirm that the vegetational indices are crucial for monitoring old-growth forests. Vegetation indices provide significant information for monitoring forest ecosystems. Based on Copernicus Sentinel-2 images, remote sensing is enabled, providing data in various spatial and spectral resolutions. The analysis of images related to old-growth forests allows for monitoring the spatial dynamics of changes in vitality and the health status of forests. This research aims to characterize old-growth forests from space through vegetation indices to obtain a more in-depth analysis of the ecological condition of these forests.MethodsThe research methodology includes the application of Sentinel 2A satellite imagery, which is taken from the European Space Agency (ESA) database. The Sentinel 2 mission consists of two identical satellites (Sentinel 2A and Sentinel 2B) that have multispectral sensors with 13 spectral channels (bands) at spatial resolutions of 10, 20, and 60 m. The analysis included the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and visible atmospheric resistance index (VARI). For determining the significance of differences in vegetation index magnitudes between old-growth forests, a one-way analysis of variance with equal repetitions was employed.Results and discussionThe parts of old-growth forests are located in different stages of development, and the trees in them are characterized by different degrees of vitality and have different values of vegetation indices. It is assumed that parts of old-growth forests in the terminal phase of development with a large number of diseased and rotting trees have the smallest sizes of vegetation indices. This research aims to characterize old-growth forests from space through vegetation indices to obtain a more in-depth analysis of the ecological condition of these forests. Using satellite imagery and their analysis of the obtained sizes of vegetation indices, it is observed that there is a significant difference in their sizes between old-growth forests. The Janj old-growth forest stands out as the area with the highest NDVI values, followed by the Lom old-growth forest, while the Perućica old-growth forest has the lowest NDVI values. However, the Perućica old-growth forest has significantly higher values of EVI and SAVI vegetation indices, while the other two old-growth forests have approximately similar values. The VARI index values are highest in the Lom old-growth forest, slightly lower in the Perućica old-growth forest, and lowest in the Janj old-growth forest. The results of these studies indicate statistically significant differences in vegetation index magnitudes between old-growth forests. The application of Copernicus satellite data enables the monitoring of changes in vegetation index values, which is of great assistance in spatially defining the developmental stages in old-growth forests (OGFs). Considering the relatively small number of sample areas covered by these studies, similar research needs to be conducted on a larger number of sample areas (replications), especially during the vegetation period.

The individual difference of motor imagery ability evoked by visual stimulus and its personality manifestation

Motor imagery has been commonly studied as a means of motor rehabilitation but, the individual differences limit its practical application. Visually evoked motor imagery has been widely highlighted by researchers because of its vivid stimulus. However, this modality is still not applicable to all persons. In this study, we studied the different performances of the visually evoked motor imagery between subjects and tried to explore the personality manifestation which can result in this performance. We found that conscientiousness and openness have negative connections with the performance of visually evoked motor imagery. To compare with spontaneous motor imagery, the visually evoked motor imagery reflects less personality difference between subjects with good and bad performances on motor imagery. This indicate that visually stimulus may increase the pervasive application of motor imagery. This study may provide benefits to predict the rehabilitation effect and to rapidly select the suitable motor rehabilitation methods.

Street view imagery: AI-based analysis method and application

Street view imagery is an emerging form of geographic big data. It presents urban visual environments from the perspective of urban residents and also contains non-visual environment of cities, such as urban human activities and socio-economic development. However, traditional digital image processing has its limitations, and the continuous development of artificial intelligence, especially computer vision and deep learning, provides strong technical support for exploring the rich semantic information in street view imagery. This paper reviews the related research on street view imagery and its artificial intelligence analysis methods and applications. It outlines the acquisition, storage, and common data sources of street view imagery. Then it introduces computer vision, deep learning, and commonly used open-source datasets in street view imagery analysis. It also detailed three aspects of AI-based street view imagery applications, namely quantification of the physical space, urban perception, and spatial semantic speculation. Finally, issues like data acquisition, domain adaption and deep learning black box are discussed. The hotspots and prospects for the development of this research topic are also prospected.

Effect of high-resolution satellite and UAV imagery plot pixel resolution in wheat crop yield prediction

ABSTRACT Accurate crop performance assessment and yield prediction in plant breeding programmes can aid decision-making to improve productivity and product quality during crop selection and management. Grain yield is a complex trait, which is a function of the genotype-environment interaction. While using digital remote sensing traits to assess crop performance and predict yield, the characteristics of the sensing tools and approaches can influence prediction performance. In this study, two sensing scales, an unmanned aerial vehicle (UAV) equipped with a ten-band multispectral camera and high-resolution (~0.31 m) WorldView-3 satellite imagery, were used to monitor spring and winter wheat breeding trails in two growing seasons (2020 and 2021). The breeding plots were planted in three different plot sizes (about 1.5 × 5.0 m, 3.0 × 11.0 m, and 4.5 × 11.0 m in spring wheat, and about 1.5 × 3.0 m, 3.0 × 7.3 m, and 4.5 × 7.3 m in winter wheat), with each having 12 varieties and three replications per variety. The spectral and vegetation indices (VI) were extracted from the datasets, and machine learning models for yield prediction (partial least squares regression, least absolute shrinkage selector operator regression, and random forest regression) were evaluated. With multiscale approaches, a moderate to strong correlation of VI data between high-resolution satellite and UAV data (0.42 ≤ r ≤ 0.99, p < 0.01) was found in most cases. The yield prediction accuracies using the extracted data from the high-resolution satellite (6.26 ≤ RMSE% ≤ 25.49; 5.11 ≤ MAE% ≤ 20.95; 0.17 ≤ r ≤ 0.78) and UAV imagery (5.53 ≤ RMSE% ≤ 17.20; 4.28 ≤ MAE% ≤ 14.20; 0.43 ≤ r ≤ 0.92) were also comparable. These findings inform the applications of high-resolution satellite imagery in breeding programmes, considering that the plot size would influence yield prediction accuracies.