Remote Sensing

22 pages, 12659 KB

Open AccessArticle

Spatiotemporal Dynamics and Land Cover Drivers of Herbaceous Aboveground Biomass in the Yellow River Delta from 2001 to 2022

by Shuo Zhang, Wanjuan Song, Ni Huang, Feng Tang, Yuelin Zhang, Chang Liu, Yibo Liu and Li Wang

Remote Sens. 2025, 17(20), 3418; https://doi.org/10.3390/rs17203418 (registering DOI) - 12 Oct 2025

Abstract

Frequent channel migrations of the Yellow River, coupled with increasing human disturbances, have driven significant land cover changes in the Yellow River Delta (YRD) over time. Accurate estimation of aboveground biomass (AGB) and clarification of the impact of land cover changes on AGB [...] Read more.

Frequent channel migrations of the Yellow River, coupled with increasing human disturbances, have driven significant land cover changes in the Yellow River Delta (YRD) over time. Accurate estimation of aboveground biomass (AGB) and clarification of the impact of land cover changes on AGB are crucial for monitoring vegetation dynamics and supporting ecological management. However, field-based biomass samples are often time-consuming and labor-intensive, and the quantity and quality of such samples greatly affect the accuracy of AGB estimation. This study developed a robust AGB estimation framework for the YRD by synthesizing 4717 field-measured samples from the published scientific literature and integrating two critical ecological indicators: leaf area index (LAI) and length of growing season (LGS). A random forest (RF) model was employed to estimate AGB for the YRD from 2001 to 2022, achieving high accuracy (R² = 0.74). The results revealed a continuous spatial expansion of AGB over the past two decades, with higher biomass consistently observed in western cropland and along the Yellow River, whereas lower biomass levels were concentrated in areas south of the Yellow River. AGB followed a fluctuating upward trend, reaching a minimum of 204.07 g/m² in 2007, peaking at 230.79 g/m² in 2016, and stabilizing thereafter. Spatially, western areas showed positive trends, with an average annual increase of approximately 10 g/m², whereas central and coastal zones exhibited localized declines of around 5 g/m². Among the changes in land cover, cropland and wetland changes were the main contributors to AGB increases, accounting for 54.2% and 52.67%, respectively. In contrast, grassland change exhibited limited or even suppressive effects, contributing −6.87% to the AGB change. Wetland showed the greatest volatility in the interaction between area change and biomass density change, which is the most uncertain factor in the dynamic change in AGB. Full article

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30 pages, 10475 KB

Open AccessArticle

CSESpy: A Unified Framework for Data Analysis of the Payloads on Board the CSES Satellite

by Emanuele Papini, Francesco Maria Follega, Roberto Battiston and Mirko Piersanti

Remote Sens. 2025, 17(20), 3417; https://doi.org/10.3390/rs17203417 (registering DOI) - 12 Oct 2025

Abstract

The China Seismo Electromagnetic Satellite (CSES) mission provides in situ measurements of the electromagnetic field, plasma, and charged particles in the topside ionosphere. Each CSES spacecraft carries several different scientific payloads delivering a wealth of information about the ionospheric plasma dynamics and properties, [...] Read more.

The China Seismo Electromagnetic Satellite (CSES) mission provides in situ measurements of the electromagnetic field, plasma, and charged particles in the topside ionosphere. Each CSES spacecraft carries several different scientific payloads delivering a wealth of information about the ionospheric plasma dynamics and properties, as well as measurement about energetic particles precipitating in the ionosphere. In this work, we introduce CSESpy, a Python package designed to provide an interface to CSES data products, with the aim of easing the pathway for scientists to carry out analyses of CSES data. Beyond simply being an interface to the data, CSESpy aims to provide higher-level analysis and visualization tools, as well as methods for combining concurrent measurements from different instruments, so as to allow multipayload studies in a unified framework. Moreover, CSESpy is designed to be highly flexible as such, it can be extended to interface with datasets from other sources and can be embedded in wider software ecosystems. We highlight some applications, also demonstrating that CSESpy is a powerful visualization tool for investigating complex events involving variations across multiple physical observables. Full article

(This article belongs to the Special Issue Remote Sensing in Geomatics (Second Edition))

23 pages, 23526 KB

Open AccessArticle

FANT-Det: Flow-Aligned Nested Transformer for SAR Small Ship Detection

by Hanfu Li, Dawei Wang, Jianming Hu, Xiyang Zhi and Dong Yang

Remote Sens. 2025, 17(20), 3416; https://doi.org/10.3390/rs17203416 (registering DOI) - 12 Oct 2025

Abstract

Ship detection in synthetic aperture radar (SAR) remote sensing imagery is of great significance in military and civilian applications. However, two factors limit detection performance: (1) a high prevalence of small-scale ship targets with limited information content and (2) interference affecting ship detection [...] Read more.

Ship detection in synthetic aperture radar (SAR) remote sensing imagery is of great significance in military and civilian applications. However, two factors limit detection performance: (1) a high prevalence of small-scale ship targets with limited information content and (2) interference affecting ship detection from speckle noise and land–sea clutter. To address these challenges, we propose a novel end-to-end (E2E) transformer-based SAR ship detection framework, called Flow-Aligned Nested Transformer for SAR Small Ship Detection (FANT-Det). Specifically, in the feature extraction stage, we introduce a Nested Swin Transformer Block (NSTB). The NSTB employs a two-level local self-attention mechanism to enhance fine-grained target representation, thereby enriching features of small ships. For multi-scale feature fusion, we design a Flow-Aligned Depthwise Efficient Channel Attention Network (FADEN). FADEN achieves precise alignment of features across different resolutions via semantic flow and filters background clutter through lightweight channel attention, further enhancing small-target feature quality. Moreover, we propose an Adaptive Multi-scale Contrastive Denoising (AM-CDN) training paradigm. AM-CDN constructs adaptive perturbation thresholds jointly determined by a target scale factor and a clutter factor, generating contrastive denoising samples that better match the physical characteristics of SAR ships. Finally, extensive experiments on three widely used open SAR ship datasets demonstrate that the proposed method achieves superior detection performance, outperforming current state-of-the-art (SOTA) benchmarks. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Image Object Detection and Information Extraction: Methods and Applications (Second Edition))

23 pages, 2593 KB

Open AccessArticle

High-Spatial-Resolution Estimation of XCO₂ Using a Stacked Ensemble Model

by Spurthy Maria Pais, Shrutilipi Bhattacharjee, Anand Kumar Madasamy, Vigneshkumar Balamurugan and Jia Chen

Remote Sens. 2025, 17(20), 3415; https://doi.org/10.3390/rs17203415 (registering DOI) - 12 Oct 2025

Abstract

One of the leading causes of climate change and global warming is the rise in carbon dioxide (

{CO}_{2}

) levels. For a precise assessment of

{CO}_{2}

’s impact on the climate and the creation of successful mitigation methods, it is [...] Read more.

One of the leading causes of climate change and global warming is the rise in carbon dioxide (

{CO}_{2}

) levels. For a precise assessment of

{CO}_{2}

’s impact on the climate and the creation of successful mitigation methods, it is essential to comprehend its distribution by analyzing

{CO}_{2}

sources and sinks, which is a challenging task using sparsely available ground monitoring stations and airborne platforms. Therefore, the data retrieved by the Orbiting Carbon Observatory-2 (OCO-2) satellite can be useful due to its extensive spatial and temporal coverage. Sparse and missed retrievals in the satellite make it challenging to perform a thorough analysis. This work trains machine learning models using the Orbiting Carbon Observatory-2 (OCO-2)

{XCO}_{2}

retrievals and auxiliary features to obtain a monthly, high-spatial-resolution, gap-filled

{CO}_{2}

concentration distribution. It uses a multi-source aggregated (MSD) dataset and the generalized stacked ensemble model to predict country-level high-resolution (1

{km}^{2}

)

{XCO}_{2}

. When evaluated with TCCON, this country-level model can achieve an RMSE of 1.42 ppm, a MAE of 0.84 ppm, and

R^{2}

of 0.90. Full article

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39 pages, 13725 KB

Open AccessArticle

SRTSOD-YOLO: Stronger Real-Time Small Object Detection Algorithm Based on Improved YOLO11 for UAV Imageries

by Zechao Xu, Huaici Zhao, Pengfei Liu, Liyong Wang, Guilong Zhang and Yuan Chai

Remote Sens. 2025, 17(20), 3414; https://doi.org/10.3390/rs17203414 (registering DOI) - 12 Oct 2025

Abstract

To address the challenges of small target detection in UAV aerial images—such as difficulty in feature extraction, complex background interference, high miss rates, and stringent real-time requirements—this paper proposes an innovative model series named SRTSOD-YOLO, based on YOLO11. The backbone network incorporates a [...] Read more.

To address the challenges of small target detection in UAV aerial images—such as difficulty in feature extraction, complex background interference, high miss rates, and stringent real-time requirements—this paper proposes an innovative model series named SRTSOD-YOLO, based on YOLO11. The backbone network incorporates a Multi-scale Feature Complementary Aggregation Module (MFCAM), designed to mitigate the loss of small target information as network depth increases. By integrating channel and spatial attention mechanisms with multi-scale convolutional feature extraction, MFCAM effectively locates small objects in the image. Furthermore, we introduce a novel neck architecture termed Gated Activation Convolutional Fusion Pyramid Network (GAC-FPN). This module enhances multi-scale feature fusion by emphasizing salient features while suppressing irrelevant background information. GAC-FPN employs three key strategies: adding a detection head with a small receptive field while removing the original largest one, leveraging large-scale features more effectively, and incorporating gated activation convolutional modules. To tackle the issue of positive-negative sample imbalance, we replace the conventional binary cross-entropy loss with an adaptive threshold focal loss in the detection head, accelerating network convergence. Additionally, to accommodate diverse application scenarios, we develop multiple versions of SRTSOD-YOLO by adjusting the width and depth of the network modules: a nano version (SRTSOD-YOLO-n), small (SRTSOD-YOLO-s), medium (SRTSOD-YOLO-m), and large (SRTSOD-YOLO-l). Experimental results on the VisDrone2019 and UAVDT datasets demonstrate that SRTSOD-YOLO-n improves the mAP@0.5 by 3.1% and 1.2% compared to YOLO11n, while SRTSOD-YOLO-l achieves gains of 7.9% and 3.3% over YOLO11l, respectively. Compared to other state-of-the-art methods, SRTSOD-YOLO-l attains the highest detection accuracy while maintaining real-time performance, underscoring the superiority of the proposed approach. Full article

(This article belongs to the Special Issue Advanced Image Processing Algorithms for Object Detection and Tracking in Aerial and Satellite Imagery)

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29 pages, 12119 KB

Open AccessArticle

Method for Obtaining Water-Leaving Reflectance from Unmanned Aerial Vehicle Hyperspectral Remote Sensing Based on Air–Ground Collaborative Calibration for Water Quality Monitoring

by Hong Liu, Xingsong Hou, Bingliang Hu, Tao Yu, Zhoufeng Zhang, Xiao Liu, Xueji Wang and Zhengxuan Tan

Remote Sens. 2025, 17(20), 3413; https://doi.org/10.3390/rs17203413 (registering DOI) - 12 Oct 2025

Abstract

Unmanned aerial vehicle (UAV) hyperspectral remote sensing imaging systems have demonstrated significant potential for water quality monitoring. However, accurately obtaining water-leaving reflectance from UAV imagery remains challenging due to complex atmospheric radiation transmission above water bodies. This study proposes a method for water-leaving [...] Read more.

Unmanned aerial vehicle (UAV) hyperspectral remote sensing imaging systems have demonstrated significant potential for water quality monitoring. However, accurately obtaining water-leaving reflectance from UAV imagery remains challenging due to complex atmospheric radiation transmission above water bodies. This study proposes a method for water-leaving reflectance inversion based on air–ground collaborative correction. A fully connected neural network model was developed using TensorFlow Keras to establish a non-linear mapping between UAV hyperspectral reflectance and the measured near-water and water-leaving reflectance from ground-based spectral. This approach addresses the limitations of traditional linear correction methods by enabling spatiotemporal synchronization correction of UAV remote sensing images with ground observations, thereby minimizing atmospheric interference and sensor differences on signal transmission. The retrieved water-leaving reflectance closely matched measured data within the 450–900 nm band, with the average spectral angle mapping reduced from 0.5433 to 0.1070 compared to existing techniques. Moreover, the water quality parameter inversion models for turbidity, color, total nitrogen, and total phosphorus achieved high determination coefficients (R² = 0.94, 0.93, 0.88, and 0.85, respectively). The spatial distribution maps of water quality parameters were consistent with in situ measurements. Overall, this UAV hyperspectral remote sensing method, enhanced by air–ground collaborative correction, offers a reliable approach for UAV hyperspectral water quality remote sensing and promotes the advancement of stereoscopic water environment monitoring. Full article

(This article belongs to the Special Issue Remote Sensing in Water Quality Monitoring)

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31 pages, 3416 KB

Open AccessArticle

Accurate Estimation of Forest Canopy Height Based on GEDI Transmitted Deconvolution Waveforms

by Longtao Cai, Jun Wu, Inthasone Somsack, Xuemei Zhao and Jiasheng He

Remote Sens. 2025, 17(20), 3412; https://doi.org/10.3390/rs17203412 (registering DOI) - 11 Oct 2025

Abstract

Accurate estimation of the forest canopy height is crucial in monitoring the global carbon cycle and evaluating progress toward carbon neutrality goals. The Global Ecosystem Dynamics Investigation (GEDI) mission provides an important data source for canopy height estimation at a global scale. However, [...] Read more.

Accurate estimation of the forest canopy height is crucial in monitoring the global carbon cycle and evaluating progress toward carbon neutrality goals. The Global Ecosystem Dynamics Investigation (GEDI) mission provides an important data source for canopy height estimation at a global scale. However, the non-zero half-width of the transmitted laser pulses (NHWTLP) and the influence of terrain slope can cause waveform broadening and overlap between canopy returns and ground returns in GEDI waveforms, thereby reducing the estimation accuracy. To address these limitations, we propose a canopy height retrieval method that combines the deconvolution of GEDI’s transmitted waveforms with terrain slope constraints on the ground response function. The method consists of two main components. The first is performing deconvolution on GEDI’s effective return waveforms using their corresponding transmitted waveforms to obtain the true ground response function within each GEDI footprint, thereby mitigating waveform broadening and overlap induced by NHWTLP. This process includes constructing a convolution convergence function for GEDI waveforms, denoising GEDI waveform data, transforming one-dimensional ground response functions into two dimensions, and applying amplitude difference regularization between the convolved and observed waveforms. The second is incorporating terrain slope parameters derived from a digital terrain model (DTM) as constraints in the canopy height estimation model to alleviate waveform broadening and overlap in ground response functions caused by topographic effects. The proposed approach enhances the precision of forest canopy height estimation from GEDI data, particularly in areas with complex terrain. The results demonstrate that, under various conditions—including GEDI full-power beams and coverage beams, different terrain slopes, varying canopy closures, and multiple study areas—the retrieved height (rh) model constructed from ground response functions derived via the inverse deconvolution of the transmitted waveforms (IDTW) outperforms the RH (the official height from GEDI L2A) model constructed using RH parameters from GEDI L2A data files in forest canopy height estimation. Specifically, without incorporating terrain slope, the rh model for canopy height estimation using full-power beams achieved a coefficient of determination (R²) of 0.58 and a root mean square error (RMSE) of 5.23 m, compared to the RH model, which had an R² of 0.58 and an RMSE of 5.54 m. After incorporating terrain slope, the rh_g model for full-power beams in canopy height estimation yielded an R² of 0.61 and an RMSE of 5.21 m, while the RH_g model attained an R² of 0.60 and an RMSE of 5.45 m. These findings indicate that the proposed method effectively mitigates waveform broadening and overlap in GEDI waveforms, thereby enhancing the precision of forest canopy height estimation, particularly in areas with complex terrain. This approach provides robust technical support for global-scale forest resource assessment and contributes to the accurate monitoring of carbon dynamics. Full article

(This article belongs to the Collection Feature Paper Special Issue on Forest Remote Sensing)

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31 pages, 12246 KB

Open AccessArticle

DVIF-Net: A Small-Target Detection Network for UAV Aerial Images Based on Visible and Infrared Fusion

by Xiaofeng Zhao, Hui Zhang, Chenxiao Li, Kehao Wang and Zhili Zhang

Remote Sens. 2025, 17(20), 3411; https://doi.org/10.3390/rs17203411 (registering DOI) - 11 Oct 2025

Abstract

During UAV aerial photography tasks, influenced by flight altitude and imaging mechanisms, the target in images often exhibits characteristics such as small size, complex backgrounds, and small inter-class differences. Under single optical modality, the weak and less discriminative feature representation of targets in [...] Read more.

During UAV aerial photography tasks, influenced by flight altitude and imaging mechanisms, the target in images often exhibits characteristics such as small size, complex backgrounds, and small inter-class differences. Under single optical modality, the weak and less discriminative feature representation of targets in drone-captured images makes them easily overwhelmed by complex background noise, leading to low detection accuracy, high missed-detection and false-detection rates in current object detection networks. Moreover, such methods struggle to meet all-weather and all-scenario application requirements. To address these issues, this paper proposes DVIF-Net, a visible-infrared fusion network for small-target detection in UAV aerial images, which leverages the complementary characteristics of visible and infrared images to enhance detection capability in complex environments. Firstly, a dual-branch feature extraction structure is designed based on YOLO architecture to separately extract features from visible and infrared images. Secondly, a P4-level cross-modal fusion strategy is proposed to effectively integrate features from both modalities while reducing computational complexity. Meanwhile, we design a novel dual context-guided fusion module to capture complementary features through channel attention of visible and infrared images during fusion and enhance interaction between modalities via element-wise multiplication. Finally, an edge information enhancement module based on cross stage partial structure is developed to improve sensitivity to small-target edges. Experimental results on two cross-modal datasets, DroneVehicle and VEDAI, demonstrate that DVIF-Net achieves detection accuracies of 85.8% and 62%, respectively. Compared with YOLOv10n, it has improved by 21.7% and 10.5% in visible modality, and by 7.4% and 30.5% in infrared modality, while maintaining a model parameter count of only 2.49 M. Furthermore, compared with 15 other algorithms, the proposed DVIF-Net attains SOTA performance. These results indicate that the method significantly enhances the detection capability for small targets in UAV aerial images, offering a high-precision and lightweight solution for real-time applications in complex aerial scenarios. Full article

20 pages, 5553 KB

Open AccessArticle

An Improved Instance Segmentation Approach for Solid Waste Retrieval with Precise Edge from UAV Images

by Yaohuan Huang and Zhuo Chen

Remote Sens. 2025, 17(20), 3410; https://doi.org/10.3390/rs17203410 (registering DOI) - 11 Oct 2025

Abstract

As a major contributor to environmental pollution in recent years, solid waste has become an increasingly significant concern in the realm of sustainable development. Unmanned Aerial Vehicle (UAV) imagery, known for its high spatial resolution, has become a valuable data source for solid [...] Read more.

As a major contributor to environmental pollution in recent years, solid waste has become an increasingly significant concern in the realm of sustainable development. Unmanned Aerial Vehicle (UAV) imagery, known for its high spatial resolution, has become a valuable data source for solid waste detection. However, manually interpreting solid waste in UAV images is inefficient, and object detection methods encounter serious challenges due to the patchy distribution, varied textures and colors, and fragmented edges of solid waste. In this study, we proposed an improved instance segmentation approach called Watershed Mask Network for Solid Waste (WMNet-SW) to accurately retrieve solid waste with precise edges from UAV images. This approach combined the well-established Mask R-CNN segmentation framework with the watershed transform edge detection algorithm. The benchmark Mask R-CNN was improved by optimizing the anchor size and Region of Interest (RoI) and integrating a new mask head of Layer Feature Aggregation (LFA) to initially detect solid waste. Subsequently, edges of the detected solid waste were precisely adjusted by overlaying the segments generated by the watershed transform algorithm. Experimental results show that WMNet-SW significantly enhances the performance of Mask R-CNN in solid waste retrieval, increasing the average precision from 36.91% to 58.10%, F1-score from 0.5 to 0.65, and AP from 63.04% to 64.42%. Furthermore, our method efficiently detects the details of solid waste edges, even overcoming the limitations of training Ground Truth (GT). This study provides a solution for retrieving solid waste with precise edges from UAV images, thereby contributing to the protection of the regional environment and ecosystem health. Full article

(This article belongs to the Section Environmental Remote Sensing)

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32 pages, 20411 KB

Open AccessArticle

High-Throughput Identification and Prediction of Early Stress Markers in Soybean Under Progressive Water Regimes via Hyperspectral Spectroscopy and Machine Learning

by Caio Almeida de Oliveira, Nicole Ghinzelli Vedana, Weslei Augusto Mendonça, João Vitor Ferreira Gonçalves, Dheynne Heyre Silva de Matos, Renato Herrig Furlanetto, Luis Guilherme Teixeira Crusiol, Amanda Silveira Reis, Werner Camargos Antunes, Roney Berti de Oliveira, Marcelo Luiz Chicati, José Alexandre M. Demattê, Marcos Rafael Nanni and Renan Falcioni

Remote Sens. 2025, 17(20), 3409; https://doi.org/10.3390/rs17203409 (registering DOI) - 11 Oct 2025

Abstract

The soybean Glycine max (L.) Merrill is a key crop in Brazil’s agricultural sector and is essential for both domestic food security and international trade. However, water stress severely impacts its productivity. In this study, we examined the physiological and biochemical responses of [...] Read more.

The soybean Glycine max (L.) Merrill is a key crop in Brazil’s agricultural sector and is essential for both domestic food security and international trade. However, water stress severely impacts its productivity. In this study, we examined the physiological and biochemical responses of soybean plants to various water regimes via hyperspectral reflectance (350–2500 nm) and machine learning (ML) models. The plants were subjected to eleven distinct water regimes, ranging from 100% to 0% field capacity, over 14 days. Seventeen key physiological parameters, including chlorophyll, carotenoids, flavonoids, proline, stress markers and water content, and hyperspectral data were measured to capture changes induced by water deficit. Principal component analysis (PCA) revealed significant spectral differences between the water treatments, with the first two principal components explaining 88% of the variance. Hyperspectral indices and reflectance patterns in the visible (VIS), near-infrared (NIR), and shortwave-infrared (SWIR) regions are linked to specific stress markers, such as pigment degradation and osmotic adjustment. Machine learning classifiers, including random forest and gradient boosting, achieved over 95% accuracy in predicting drought-induced stress. Notably, a minimal set of 12 spectral bands (including red-edge and SWIR features) was used to predict both stress levels and biochemical changes with comparable accuracy to traditional laboratory assays. These findings demonstrate that spectroscopy by hyperspectral sensors, when combined with ML techniques, provides a nondestructive, field-deployable solution for early drought detection and precision irrigation in soybean cultivation. Full article

(This article belongs to the Special Issue Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods)

26 pages, 12268 KB

Open AccessArticle

Downscaling Method for Crop Yield Statistical Data Based on the Standardized Deviation from the Mean of the Comprehensive Crop Condition Index

by Ke Luo, Jianqiang Ren, Xiangxin Bu and Hongwei Zhao

Remote Sens. 2025, 17(20), 3408; https://doi.org/10.3390/rs17203408 (registering DOI) - 11 Oct 2025

Abstract

Spatializing crop yield statistical data with administrative divisions as the basic unit helps reveal the spatial distribution characteristics of crop yield and provides necessary spatial information to support field management and government decision-making. However, owing to an insufficient understanding of the factors affecting [...] Read more.

Spatializing crop yield statistical data with administrative divisions as the basic unit helps reveal the spatial distribution characteristics of crop yield and provides necessary spatial information to support field management and government decision-making. However, owing to an insufficient understanding of the factors affecting yield, accurately depicting its spatial differences remains challenging. Taking Hailun city, Heilongjiang Province, as an example, this study proposes a yield downscaling method based on the standardized deviation from the mean of the comprehensive crop condition index (CCCI) during key phenological periods of the growing season. First, Sentinel-2 remote sensing data were used to retrieve crop condition parameters during key phenological periods, and the CCCI was constructed using the correlation between crop condition parameters in key phenological periods and statistical yield as the weight. Subsequently, regression analysis and the entropy weight method were applied to determine the spatiotemporal dynamic weights of the CCCI during key phenological stages and to calculate the standardized deviation from the mean. By combining these two components, the comprehensive spatial difference index of the crop growth condition (CSDICGC) was derived, which offered a new way to characterize the discrepancies between the pixel-level yield and statistical yield, thereby downscaling the yield statistical data from the administrative unit to the pixel scale. The results indicated that this method achieved a regional accuracy close to 100%, with a strong fit at the pixel scale. Pixel-level accuracy validation against ground-truth maize yield data resulted in an R² of 0.82 and a mean relative error (MRE) of 4.75%. The novelty of this study was characterized by the integration of multistage crop condition parameters with dynamic spatiotemporal weighting to overcome the limitations of single-index methods. The crop yield statistical data downscaling spatialization method proposed in this paper is simple and efficient and has the potential to be popularized and applied over relatively large regions. Full article

(This article belongs to the Special Issue Near Real-Time (NRT) Agriculture Monitoring)

23 pages, 11528 KB

Open AccessArticle

Spring Dust Intensity Monitoring at Hourly Intervals Using Himawari-8 Satellite Images and Artificial Intelligence Method

by Jiafu Zhao, Pengfei Chen and Xiaolong Sun

Remote Sens. 2025, 17(20), 3407; https://doi.org/10.3390/rs17203407 (registering DOI) - 11 Oct 2025

Abstract

To achieve accurate monitoring of dust intensity, this study developed a coupled model based on a convolutional neural network (CNN) and a bidirectional long short-term memory network (Bi-LSTM) to monitor dust intensity in a 24 h dynamic pattern. During this process, progressive dust [...] Read more.

To achieve accurate monitoring of dust intensity, this study developed a coupled model based on a convolutional neural network (CNN) and a bidirectional long short-term memory network (Bi-LSTM) to monitor dust intensity in a 24 h dynamic pattern. During this process, progressive dust temporal (PDT) features reflecting the temporal dynamics of dust events, including clear-sky state values, adjacent observation state values, and current observation state values for spectral indices and brightness temperatures, were first designed. Then, a PCBNet model combining CNN and Bi-LSTM was established and compared with PCLNet (CNN and LSTM), random forest (RF), and support vector machine (SVM) using only single-time observations, as well as PDT-RF and PDT-SVM, which used PDT features as inputs. Finally, a dust intensity product was generated by the optimal model, and its relationship with PM10 concentrations at air quality stations was examined. Furthermore, a dust storm event in April 2021 was analyzed to evaluate the ability of the products to capture event dynamics. The results indicate that PCBNet achieved the highest accuracy among all models on the validation dataset. Predicted dust intensity levels were well correlated with PM10 concentrations, and the monitoring product effectively tracked the spatiotemporal evolution of dust event. Full article

6 pages, 163 KB

Open AccessEditorial

Editorial for Special Issue “Remote Sensing of Precipitation Extremes”

by Ehsan Sharifi, Silas Michaelides and Vincenzo Levizzani

Remote Sens. 2025, 17(20), 3406; https://doi.org/10.3390/rs17203406 (registering DOI) - 11 Oct 2025

Abstract

Recent years have seen tremendous advancements in the field of extreme precipitation monitoring, particularly through the application of remote sensing technologies [...] Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation Extremes)

27 pages, 4080 KB

Open AccessReview

A Review of Recent Development of Geosynchronous Synthetic Aperture Radar Technique

by Jinwei Li, Caipin Li, Xiaomin Tan, Dong You, Chongdi Duan, Sheng Zhang, Hongxing Dang, Guangting Li and Qingjun Zhang

Remote Sens. 2025, 17(20), 3405; https://doi.org/10.3390/rs17203405 (registering DOI) - 11 Oct 2025

Abstract

As the world’s first geosynchronous (GEO) orbit synthetic aperture radar (SAR) satellite, LuTan-4 was successfully launched on 13 August 2023. It was developed by the China Academy of Space Technology, with which the authors are affiliated. This study presents a comprehensive review of [...] Read more.

As the world’s first geosynchronous (GEO) orbit synthetic aperture radar (SAR) satellite, LuTan-4 was successfully launched on 13 August 2023. It was developed by the China Academy of Space Technology, with which the authors are affiliated. This study presents a comprehensive review of the recent advancements in GEO SAR technology. The review first begins by summarizing key considerations in GEO SAR system design, including orbital parameters and synthetic aperture time, transmit power and antenna aperture, two-dimensional beam-steering, imaging parameters and non-ideal factors. In terms of GEO SAR signal processing, the article focuses on two fundamental models, i.e., the high-order slant-range model and the coupled space-variant signal model. It also introduces the current GEO SAR imaging algorithms. Furthermore, the study presents an analysis between GEO SAR and low-Earth orbit SAR systems, highlighting the superior capability of GEO SAR for large-scale surface dynamic processes. Finally, the paper outlines the future development directions and potential applications of GEO SAR technology. Full article

(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)

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21 pages, 12150 KB

Open AccessArticle

A Registration Method for ULS-MLS Data in High-Canopy-Density Forests Based on Feature Deviation Metric

by Houyu Liang, Xiang Zhou, Tingting Lv, Qingwang Liu, Zui Tao and Hongming Zhang

Remote Sens. 2025, 17(20), 3403; https://doi.org/10.3390/rs17203403 (registering DOI) - 11 Oct 2025

Abstract

The integration of unmanned aerial vehicle-based laser scanning (ULS) and mobile laser scanning (MLS) enables the detection of forest three-dimensional structure in high-density canopy areas and has become an important tool for monitoring and managing forest ecosystems. However, MLS faces difficulties in positioning [...] Read more.

The integration of unmanned aerial vehicle-based laser scanning (ULS) and mobile laser scanning (MLS) enables the detection of forest three-dimensional structure in high-density canopy areas and has become an important tool for monitoring and managing forest ecosystems. However, MLS faces difficulties in positioning due to canopy occlusion, making integration challenging. Due to the variations in observation platforms, ULS and MLS point clouds exhibit significant structural discrepancies and limited overlapping areas, necessitating effective methods for feature extraction and correspondence establishment between these features to achieve high-precision registration and integration. Therefore, we propose a registration algorithm that introduces a Feature Deviation Metric to enable feature extraction and correspondence construction for forest point clouds in complex regional environments. The algorithm first extracts surface point clouds using the hidden point algorithm. Then, it applies the proposed dual-threshold method to cluster individual tree features in ULS, using cylindrical detection to construct a Feature Deviation Metric from the feature points and surface point clouds. Finally, an optimization algorithm is employed to match the optimal Feature Deviation Metric for registration. Experiments were conducted in 8 stratified mixed tropical rainforest plots with complex mixed-species canopies in Malaysia and 6 structurally simple, high-canopy-density pure forest plots in anorthern China. Our algorithm achieved an average RMSE of 0.17 m in eight tropical rainforest plots with an average canopy density of 0.93, and an RMSE of 0.05 m in six northern forest plots in China with an average canopy density of 0.75, demonstrating high registration capability. Additionally, we also conducted comparative and adaptability analyses, and the results indicate that the proposed model exhibits high accuracy, efficiency, and stability in high-canopy-density forest areas. Moreover, it shows promise for high-precision ULS-MLS registration in a wider range of forest types in the future. Full article

28 pages, 65254 KB

Open AccessArticle

SAM-Based Few-Shot Learning for Coastal Vegetation Segmentation in UAV Imagery via Cross-Matching and Self-Matching

by Yunfan Wei, Zhiyou Guo, Conghui Li, Weiran Li and Shengke Wang

Remote Sens. 2025, 17(20), 3404; https://doi.org/10.3390/rs17203404 - 10 Oct 2025

Abstract

Coastal zones, as critical intersections of ecosystems, resource utilization, and socioeconomic activities, exhibit complex and diverse land cover types with frequent changes. Acquiring large-scale, high-quality annotated data in these areas is costly and time-consuming, which makes rule-based segmentation methods reliant on extensive annotations [...] Read more.

Coastal zones, as critical intersections of ecosystems, resource utilization, and socioeconomic activities, exhibit complex and diverse land cover types with frequent changes. Acquiring large-scale, high-quality annotated data in these areas is costly and time-consuming, which makes rule-based segmentation methods reliant on extensive annotations impractical. Few-shot semantic segmentation, which enables effective generalization from limited labeled samples, thus becomes essential for coastal region analysis. In this work, we propose an optimized few-shot segmentation method based on the Segment Anything Model (SAM) with a frozen-parameter segmentation backbone to improve generalization. To address the high visual similarity among coastal vegetation classes, we design a cross-matching module integrated with a hyper-correlation pyramid to enhance fine-grained visual correspondence. Additionally, a self-matching module is introduced to mitigate scale variations caused by UAV altitude changes. Furthermore, we construct a novel few-shot segmentation dataset, OUC-UAV-SEG-

2^{i}

, based on the OUC-UAV-SEG dataset, to alleviate data scarcity. In quantitative experiments, the suggested approach outperforms existing models in mIoU and FB-IoU under ResNet50/101 (e.g., ResNet50’s 1-shot/5-shot mIoU rises by 4.69% and 4.50% vs. SOTA), and an ablation study shows adding CMM, SMM, and SAM boosts Mean mIoU by 4.69% over the original HSNet, significantly improving few-shot semantic segmentation performance. Full article

(This article belongs to the Special Issue Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience (Second Edition))

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32 pages, 5368 KB

Open AccessArticle

Next-Generation Drought Forecasting: Hybrid AI Models for Climate Resilience

by Jinping Liu, Tie Liu, Lei Huang, Yanqun Ren and Panxing He

Remote Sens. 2025, 17(20), 3402; https://doi.org/10.3390/rs17203402 - 10 Oct 2025

Abstract

Droughts are increasingly threatening ecological balance, agricultural productivity, and socio-economic resilience—especially in semi-arid regions like the Inner Mongolia segment of China’s Yellow River Basin. This study presents a hybrid drought forecasting framework integrating machine learning (ML) and deep learning (DL) models with high-resolution [...] Read more.

Droughts are increasingly threatening ecological balance, agricultural productivity, and socio-economic resilience—especially in semi-arid regions like the Inner Mongolia segment of China’s Yellow River Basin. This study presents a hybrid drought forecasting framework integrating machine learning (ML) and deep learning (DL) models with high-resolution historical and downscaled future climate data. TerraClimate observations (1985–2014) and bias-corrected CMIP6 projections (2030–2050) under SSP2-4.5 and SSP5-8.5 scenarios were utilized to develop and evaluate the models. Among the tested ML algorithms, Random Forest (RF) demonstrated the best trade-off between accuracy and interpretability and was selected for feature importance analysis. The top-ranked predictors—precipitation, solar radiation, and maximum temperature—were used to train a Long Short-Term Memory (LSTM) network. The LSTM outperformed all ML models, achieving high predictive skill (R² = 0.766, CC = 0.880, RMSE = 0.885). Scenario-based projections revealed increasing drought severity and variability under SSP5-8.5, with mean PDSI values dropping below −3 after 2040 and deepening toward −4 by 2049. The high-emission scenario also exhibited broader uncertainty bands and amplified interannual anomalies. These findings highlight the value of hybrid AI–climate modeling approaches in capturing complex drought dynamics and supporting anticipatory water resource planning in vulnerable dryland environments. Full article

(This article belongs to the Section Environmental Remote Sensing)

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27 pages, 37439 KB

Open AccessArticle

Structural Health Monitoring of Anaerobic Lagoon Floating Covers Using UAV-Based LiDAR and Photogrammetry

by Benjamin Steven Vien, Thomas Kuen, Louis Raymond Francis Rose and Wing Kong Chiu

Remote Sens. 2025, 17(20), 3401; https://doi.org/10.3390/rs17203401 (registering DOI) - 10 Oct 2025

Abstract

There has been significant interest in deploying unmanned aerial vehicles (UAVs) for their ability to perform precise and rapid remote mapping and inspection of critical environmental assets for structural health monitoring. This case study investigates the use of UAV-based LiDAR and photogrammetry at [...] Read more.

There has been significant interest in deploying unmanned aerial vehicles (UAVs) for their ability to perform precise and rapid remote mapping and inspection of critical environmental assets for structural health monitoring. This case study investigates the use of UAV-based LiDAR and photogrammetry at Melbourne Water’s Western Treatment Plant (WTP) to routinely monitor high-density polyethylene floating covers on anaerobic lagoons. The proposed approach integrates LiDAR and photogrammetry data to enhance the accuracy and efficiency of generating digital elevation models (DEMs) and orthomosaics by leveraging the strengths of both methods. Specifically, the photogrammetric images were orthorectified onto LiDAR-derived DEMs as the projection plane to construct the corresponding orthomosaic. This method captures precise elevation points directly from LiDAR, forming a robust foundation dataset for DEM construction. This streamlines the workflow without compromising detail, as it eliminates the need for time-intensive photogrammetry processes, such as dense cloud and depth map generation. This integration accelerates dataset production by up to four times compared to photogrammetry alone, while achieving centimetre-level accuracy. The LiDAR-derived DEM achieved higher elevation accuracy with a root mean square error (RMSE) of 56.1 mm, while the photogrammetry-derived DEM achieved higher in-plane accuracy with an RMSE of up to 35.4 mm. An analysis of cover deformation revealed that the floating cover had elevated rapidly within the first two years post-installation before showing lateral displacement around the sixth year, which was also evident from a significant increase in wrinkling. This approach delivers valuable insights into cover condition that, in turn, clarifies scum accumulation and movement, thereby enhancing structural integrity management and supporting environmental sustainability at WTP by safeguarding methane-rich biogas for renewable-energy generation and controlling odours. The findings support the ongoing collaborative industry research between Monash University and Melbourne Water, aimed at achieving comprehensive structural and prognostic health assessments of these high-value assets. Full article

(This article belongs to the Special Issue Combination of LiDAR and UAS Data for Geological and Environmental Applications)

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18 pages, 7157 KB

Open AccessArticle

Perspective Back-Projection Algorithm: Interface Imaging for Airborne Ice Detection

by Yingge Wang, Jinbiao Zhu, Jie Pan and Yuquan Liu

Remote Sens. 2025, 17(20), 3400; https://doi.org/10.3390/rs17203400 - 10 Oct 2025

Abstract

The deployment of traditional ground-penetrating radar (GPR) systems for ice detection on steep terrain presents substantial safety challenges for ground crews due to inaccessibility and hazardous working conditions. However, airborne GPR (AGPR) and radio echo sounding (RES) provide solutions to these difficulties. Assuming [...] Read more.

The deployment of traditional ground-penetrating radar (GPR) systems for ice detection on steep terrain presents substantial safety challenges for ground crews due to inaccessibility and hazardous working conditions. However, airborne GPR (AGPR) and radio echo sounding (RES) provide solutions to these difficulties. Assuming that ice is homogeneous, we introduce a perspective back-projection algorithm designed to process AGPR or RES data that directly searches for unobstructed refracted electromagnetic (EM) wave paths and focuses EM energy below the surface by computing path-specific travel times. The results from the 2D and 3D imaging tests indicate that the perspective back-projection algorithm can accurately image the ice–rock interface. However, Snell’s Law suggests that part of the energy may fail to propagate through the air–ice interface and reach either the ice–rock interface or the receivers in scenarios where the incident angle of an EM wave exceeds a certain threshold. This energy deficit can hinder the perspective back-projection algorithm from accurately imaging such ice–rock interfaces. Despite these limitations, the perspective back-projection algorithm remains a promising tool for imaging sub-ice interfaces in AGPR and RES ice detection. Full article

(This article belongs to the Special Issue Electromagnetic Modeling of Geophysical Prospecting in Remote Sensing)

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30 pages, 11330 KB

Open AccessArticle

Distance Transform-Based Spatiotemporal Model for Approximating Missing NDVI from Satellite Data

by Amirhossein Mirtabatabaeipour, Lakin Wecker, Majid Amirfakhrian and Faramarz F. Samavati

Remote Sens. 2025, 17(20), 3399; https://doi.org/10.3390/rs17203399 - 10 Oct 2025

Abstract

One widely used method for analyzing vegetation growth from satellite imagery is the Normalized Difference Vegetation Index (NDVI), a key metric for assessing vegetation dynamics. NDVI varies not only spatially but also temporally, which is essential for analyzing vegetation health and growth patterns [...] Read more.

One widely used method for analyzing vegetation growth from satellite imagery is the Normalized Difference Vegetation Index (NDVI), a key metric for assessing vegetation dynamics. NDVI varies not only spatially but also temporally, which is essential for analyzing vegetation health and growth patterns over time. High-resolution, cloud-free satellite images, particularly from publicly available sources like Sentinel, are ideal for this analysis. However, such images are not always available due to cloud and shadow contamination. To address this limitation, we propose a model that integrates both the temporal and spatial aspects of the data to approximate the missing or contaminated regions. In this method, we separately approximate NDVI using spatial and temporal components of the time-varying satellite data. Spatial approximation near the boundary of the missing data is expected to be more accurate, while temporal approximation becomes more reliable for regions further from the boundary. Therefore, we propose a model that leverages the distance transform to combine these two methods into a single, weighted model, which is more accurate than either method alone. We introduce a new decay function to control this transition. We evaluate our spatiotemporal model for approximating NDVI across 16 farm fields in Western Canada from 2018 to 2023. We empirically determined the best parameters for the decay function and distance-transform-based model. The results show a significant improvement compared to using only spatial or temporal approximations alone (up to a 263% improvement as measured by RMSE relative to the baseline). Furthermore, our model demonstrates a notable improvement compared to simple combination (up to 51% improvement as measured by RMSE) and Spatiotemporal Kriging (up to 28% improvement as measured by RMSE). Finally, we apply our spatiotemporal model in a case study related to improving the specification of the peak green day for numerous fields. Full article

(This article belongs to the Special Issue Big Geo-Spatial Data and Advanced 3D Modelling in GIS and Satellite)

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21 pages, 14964 KB

Open AccessArticle

An Automated Framework for Abnormal Target Segmentation in Levee Scenarios Using Fusion of UAV-Based Infrared and Visible Imagery

by Jiyuan Zhang, Zhonggen Wang, Jing Chen, Fei Wang and Lyuzhou Gao

Remote Sens. 2025, 17(20), 3398; https://doi.org/10.3390/rs17203398 - 10 Oct 2025

Abstract

Levees are critical for flood defence, but their integrity is threatened by hazards such as piping and seepage, especially during high-water-level periods. Traditional manual inspections for these hazards and associated emergency response elements, such as personnel and assets, are inefficient and often impractical. [...] Read more.

Levees are critical for flood defence, but their integrity is threatened by hazards such as piping and seepage, especially during high-water-level periods. Traditional manual inspections for these hazards and associated emergency response elements, such as personnel and assets, are inefficient and often impractical. While UAV-based remote sensing offers a promising alternative, the effective fusion of multi-modal data and the scarcity of labelled data for supervised model training remain significant challenges. To overcome these limitations, this paper reframes levee monitoring as an unsupervised anomaly detection task. We propose a novel, fully automated framework that unifies geophysical hazards and emergency response elements into a single analytical category of “abnormal targets” for comprehensive situational awareness. The framework consists of three key modules: (1) a state-of-the-art registration algorithm to precisely align infrared and visible images; (2) a generative adversarial network to fuse the thermal information from IR images with the textural details from visible images; and (3) an adaptive, unsupervised segmentation module where a mean-shift clustering algorithm, with its hyperparameters automatically tuned by Bayesian optimization, delineates the targets. We validated our framework on a real-world dataset collected from a levee on the Pajiang River, China. The proposed method demonstrates superior performance over all baselines, achieving an Intersection over Union of 0.348 and a macro F1-Score of 0.479. This work provides a practical, training-free solution for comprehensive levee monitoring and demonstrates the synergistic potential of multi-modal fusion and automated machine learning for disaster management. Full article

(This article belongs to the Special Issue Development and Implementation of Early Detection and Warning Methods for Natural Hazards Utilizing Multi-Source Remote Sensing Data)

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22 pages, 4487 KB

Open AccessArticle

A Trajectory Estimation Method Based on Microwave Three-Point Ranging for Sparse 3D Radar Imaging

by Changyu Lou, Jingcheng Zhao, Xingli Wu, Zongkai Yang, Jungang Miao and Tao Hong

Remote Sens. 2025, 17(20), 3397; https://doi.org/10.3390/rs17203397 - 10 Oct 2025

Abstract

Precise estimate of antenna location is essential for high-quality three-dimensional (3D) radar imaging, especially under sparse sampling schemes. In scenarios involving synchronized scanning and rotational motion, small deviations in the radar’s transmitting position can lead to significant phase errors, thereby degrading image fidelity [...] Read more.

Precise estimate of antenna location is essential for high-quality three-dimensional (3D) radar imaging, especially under sparse sampling schemes. In scenarios involving synchronized scanning and rotational motion, small deviations in the radar’s transmitting position can lead to significant phase errors, thereby degrading image fidelity or even causing image failure. To address this challenge, we propose a novel trajectory estimation method based on microwave three-point ranging. The method utilizes three fixed microwave-reflective calibration spheres positioned outside the imaging scene. By measuring the one-dimensional radial distances between the radar and each of the three spheres, and geometrically constructing three intersecting spheres in space, the radar’s spatial position can be uniquely determined at each sampling moment. This external reference-based localization scheme significantly reduces positioning errors without requiring precise synchronization control between scanning and rotation. Furthermore, the proposed approach enhances the robustness and flexibility of sparse sampling strategies in near-field radar imaging. Beyond ground-based setups, the method also holds promise for drone-borne 3D imaging applications, enabling accurate localization of onboard radar systems during flight. Simulation results and error analysis demonstrate that the proposed method improves trajectory accuracy and supports high-fidelity 3D reconstruction under non-ideal sampling conditions. Full article

(This article belongs to the Section Engineering Remote Sensing)

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19 pages, 2742 KB

Open AccessArticle

Cloud-Based Solutions for Monitoring Coastal Ecosystems and the Prioritization of Restoration Efforts Across Belize

by Christine Evans, Lauren Carey, Florencia Guerra, Emil A. Cherrington, Edgar Correa and Diego Quintero

Remote Sens. 2025, 17(20), 3396; https://doi.org/10.3390/rs17203396 - 10 Oct 2025

Abstract

In recent years, the availability of automated change detection algorithms in Google Earth Engine has permitted the cloud-based processing of large quantities of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of [...] Read more.

In recent years, the availability of automated change detection algorithms in Google Earth Engine has permitted the cloud-based processing of large quantities of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) allow users to exploit decades of Earth Observations (EOs), leveraging the Landsat archive and data from other sensors to detect disturbances in forest ecosystems. Despite the wide adoption of these methods, robust documentation, and a growing community of users, little research has systematically detailed their tuning process in mangrove environments. This work aims to identify the best practices for applying these models to monitor changes within mangrove forest cover, which has been declining gradually in Belize the last several decades. Partnering directly with the Belizean Forest Department, our team developed a replicable, efficient methodology to annually update the country’s mangrove extent, employing EO-based change detection. We ran a series of model variations in both CCDC-SMA and LandTrendr to identify the parameterizations best suited to identifying change in Belizean mangroves. Applying the best performing model run to the starting 2017 mangrove extent, we estimated a total loss of 540 hectares in mangrove coverage by 2024. Overall accuracy across thirty variations in model runs of LandTrendr and CCDC-SMA ranged from 0.67 to 0.75. While CCDC-SMA generally detected more disturbances and had higher precision for true changes, LandTrendr runs tended to have higher recall. Our results suggest LandTrendr offered more flexibility in balancing precision and recall for true changes compared to CCDC-SMA, due to its greater variety of adjustable parameters. Full article

(This article belongs to the Special Issue Remote Sensing in Mangroves IV)

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