Remote Sensing

Open AccessArticle

Precise Orbit Determination and Accuracy Analysis for BDS-3 Satellites Using SLR Observations

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Remote Sens. 2023, 15(7), 1833; https://doi.org/10.3390/rs15071833 (registering DOI) - 29 Mar 2023

Abstract

Satellite laser ranging (SLR) is the space geodetic technique with the highest degree of range, measuring precision and distances right down to the millimeter level. Thanks to the improvement of SLR station layouts and the advance of SLR technology, in recent years, more [...] Read more.

Satellite laser ranging (SLR) is the space geodetic technique with the highest degree of range, measuring precision and distances right down to the millimeter level. Thanks to the improvement of SLR station layouts and the advance of SLR technology, in recent years, more research has been conducted to determine Global Navigation Satellite System (GNSS) satellite orbits using SLR data. The primary goal of this contribution is to investigate the accuracy of BeiDou Navigation-3 (BDS-3) Satellite precise orbit determination (POD) using solely SLR data, as well as explore the impact of various factors on that accuracy. Firstly, we used actual SLR data to make the POD for BDS-3 satellites, and the POD accuracy was positively connected with the orbital arc lengths. The 9-day median root mean square (RMS) in radial (R), along-track (T), and cross-track (N) directions were estimated at 4.7–8.2, 22.1–35.2, and 27.4–43.8 cm, respectively, for comparison with WUM precise orbits. Then, we explored the impact of SLR observations and stations on POD accuracy. For 9-day orbital arc lengths, five station or 20 observation arcs may offer an orbit with a 1 m precision. Six to eight stations or 30–35 observation arcs allow an improved orbit accuracy up to approximately 0.5 m. Furthermore, we examined how measurement errors and orbit modeling errors affect the SLR-only POD accuracy using simulated SLR data. For orbital arc lengths of 9 days, each cm of random error leads to a 9.3–11.0 cm decrease in orbit accuracy. The accuracy of an orbit is reduced by 10.1–15.0 cm for every 1 cm of systematic error. Moreover, for solar radiation pressure (SRP) errors, the effect of POD accuracy is 20.5–45.1 cm, respectively. Full article

(This article belongs to the Special Issue Precision Orbit Determination of Satellites)

Open AccessArticle

Centimeter-Level Orbit Determination of GRACE-C Using IGS-RTS Data

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Duoduo Li

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Xuhua Zhou

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Kai Li

Remote Sens. 2023, 15(7), 1832; https://doi.org/10.3390/rs15071832 (registering DOI) - 29 Mar 2023

Abstract

GNSS real-time applications greatly benefit from the International GNSS Service’s (IGS) real-time service (RTS). This service does more than provide for terrestrial precise point positioning (PPP); it also brings more possibilities for space-borne technology. With this service, the State-Space Representation (SSR) product, which [...] Read more.

GNSS real-time applications greatly benefit from the International GNSS Service’s (IGS) real-time service (RTS). This service does more than provide for terrestrial precise point positioning (PPP); it also brings more possibilities for space-borne technology. With this service, the State-Space Representation (SSR) product, which includes orbit corrections and clock corrections, is finally available to users. In this paper, the GPS real-time orbit and clock corrections provided by 11 analysis centers (ACs) from the day of the year (DOY) 144 to 153 of 2022 are discussed from 3 perspectives: integrity, continuity, and accuracy. Moreover, actual observation data from the GRACE-C satellite are processed, along with SSR corrections from different ACs. The following can be concluded: (1) In terms of integrity and continuity, the products provided by CNE, ESA, and GMV perform better. (2) CNE, ESA, and WHU are the most accurate, with values of about 5 cm for the satellite orbit and 20 ps for the satellite clock. Additionally, the clock accuracy is related to the Block. Block IIR and Block IIR-M are slightly worse than Block IIF and Block IIIA. (3) The accuracy of post-processing reduced-dynamic precise orbit determination (POD) and kinematic POD are at the centimeter level in radius, and the reduced-dynamic POD is more accurate and robust than the kinematic POD. Full article

(This article belongs to the Special Issue Space-Geodetic Techniques II)

Open AccessArticle

A Real-Time Linear Prediction Algorithm for Detecting Abnormal BDS-2/BDS-3 Satellite Clock Offsets

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Remote Sens. 2023, 15(7), 1831; https://doi.org/10.3390/rs15071831 (registering DOI) - 29 Mar 2023

Abstract

Due to space environment interference, imperfect data processing model, and the performance of atomic clocks, real-time satellite clock products often contain outliers or irregular biases. We propose a real-time linear moving short-term prediction algorithm to predict clock offsets and detect abnormalities. The proposed [...] Read more.

Due to space environment interference, imperfect data processing model, and the performance of atomic clocks, real-time satellite clock products often contain outliers or irregular biases. We propose a real-time linear moving short-term prediction algorithm to predict clock offsets and detect abnormalities. The proposed algorithm mainly includes phase/frequency anomaly detection and real-time prediction part. Both the phase and frequency domains are used to detect abnormal clock offsets with previous epochs for building the clock prediction model accurately. The real-time moving prediction module utilizes the high short-term prediction performance to check the clock abnormality. The performance of the algorithm is then evaluated for all satellites with real-time estimated satellite clock offsets. To verify the feasibility and effectiveness of the proposed linear moving model and algorithm, the results of the grey model GM(1,1) and the ARIMA model are also compared. The experimental results indicated that the algorithm can detect clock outliers, frequency modulation, and phase jumps, and the linear model has a better clock performance improvement. After the abnormalities are removed with the proposed algorithm, the average STD accuracy of the real-time clock offsets for all satellites is improved by 15.5%, compared to an improvement of 11.4% by the GM(1,1) model and 11.5% by the ARIMA model. The PPP results demonstrate that the proposed clock prediction algorithm improves the positioning accuracy by 8.1%, 13.3%, and 16.9% in the east, north, and up components, respectively. Full article

(This article belongs to the Topic Recent Advances in PNT Technology with GNSS as the Core and Its Application in Emerging Fields)

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Open AccessArticle

Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany

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Maninder Singh Dhillon

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Ingolf Steffan-Dewenter

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Tobias Ullmann

Remote Sens. 2023, 15(7), 1830; https://doi.org/10.3390/rs15071830 (registering DOI) - 29 Mar 2023

Abstract

The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to [...] Read more.

The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km²) for the year 2019. For WW and OSR, the synthetic products’ high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R² = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R² = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R² = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R² = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world. Full article

(This article belongs to the Special Issue Monitoring Crops and Rangelands Using Remote Sensing)

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Open AccessCommunication

LDANet: A Lightweight Dynamic Addition Network for Rural Road Extraction from Remote Sensing Images

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Remote Sens. 2023, 15(7), 1829; https://doi.org/10.3390/rs15071829 (registering DOI) - 29 Mar 2023

Abstract

Automatic road extraction from remote sensing images has an important impact on road maintenance and land management. While significant deep-learning-based approaches have been developed in recent years, achieving a suitable trade-off between extraction accuracy, inference speed and model size remains a fundamental and [...] Read more.

Automatic road extraction from remote sensing images has an important impact on road maintenance and land management. While significant deep-learning-based approaches have been developed in recent years, achieving a suitable trade-off between extraction accuracy, inference speed and model size remains a fundamental and challenging issue for real-time road extraction applications, especially for rural roads. For this purpose, we developed a lightweight dynamic addition network (LDANet) to exploit rural road extraction. Specifically, considering the narrow, complex and diverse nature of rural roads, we introduce an improved Asymmetric Convolution Block (ACB)-based Inception structure to extend the low-level features in the feature extraction layer. In the deep feature association module, the depth-wise separable convolution (DSC) is introduced to reduce the computational complexity of the model, and an adaptation-weighted overlay is designed to capture the salient features. Moreover, we utilize a dynamic weighted combined loss, which can better solve the sample imbalance and boosts segmentation accuracy. In addition, we constructed a typical remote sensing dataset of rural roads based on the Deep Globe Land Cover Classification Challenge dataset. Our experiments demonstrate that LDANet performs well in road extraction with fewer model parameters (<1 MB) and that the accuracy and the mean Intersection over Union reach 98.74% and 76.21% on the test dataset, respectively. Therefore, LDANet has potential to rapidly extract and monitor rural roads from remote sensing images. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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Open AccessArticle

A Comprehensive Correction Method for Radiation Distortion of Multi-Strip Airborne Hyperspectral Images

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Remote Sens. 2023, 15(7), 1828; https://doi.org/10.3390/rs15071828 (registering DOI) - 29 Mar 2023

Abstract

Airborne hyperspectral imaging plays an increasingly important role in environmental monitoring. However, due to the limitations of the acquisition conditions, there are uneven radiation and chromatic aberrations in the mosaic data. Accurate preprocessing of the original data is the premise of qualitative and [...] Read more.

Airborne hyperspectral imaging plays an increasingly important role in environmental monitoring. However, due to the limitations of the acquisition conditions, there are uneven radiation and chromatic aberrations in the mosaic data. Accurate preprocessing of the original data is the premise of qualitative and quantitative remote sensing. In this study, we proposed a comprehensive radiation distortion correction method that integrates radiation attenuation difference correction, topographic correction, and multi-strip images consistency adjustment (RA-TOC-CA). First, the radiation attenuation equation was constructed by combining the viewing geometry, terrain, and the elevation difference between the UAV and the ground to eliminate the radiation attenuation difference of pixels acquired at the different instantaneous field of view (IFOV). Second, an improved kernel-driven BRDF model was built combining terrain information and illumination-viewing (flight attitude and sensor IFOV) geometry to eliminate the radiation unevenness and BRDF distortion caused by topography. Third, adjusting the reflectance of multi-strip images according to the homonymous points’ reflectance of adjacent strips should be equal, eliminating the radiation differences between multiple strips. Based on multi-strip airborne hyperspectral images collected in the Shaanxi province of China, the correction results of the RA-TOC-CA method were compared with those of the SCS+C and Minnaert+SCS methods regarding various evaluation criteria. The results showed that SCS+C and Minnaert+SCS can reduce the topographic effect but cannot eliminate the reflectance difference at the edges of adjacent images, and SCS+C overcorrects the reflectance. RA-TOC-CA weakened the topographic effects and brightness gradient, which was physically stable and generalizable. Compared with previous studies, RA-TOC-CA provided a complete radiation distortion correction method for airborne hyperspectral images and had a solid theoretical basis. This study introduces an effective method for radiation distortion correction of airborne hyperspectral images and provides technical support for large-scale applications of hyperspectral images. Full article

Open AccessArticle

Combining Discrete and Continuous Representation: Scale-Arbitrary Super-Resolution for Satellite Images

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Remote Sens. 2023, 15(7), 1827; https://doi.org/10.3390/rs15071827 (registering DOI) - 29 Mar 2023

Abstract

The advancements in image super-resolution technology have led to its widespread use in remote sensing applications. However, there is currently a lack of a general solution for the reconstruction of satellite images at arbitrary resolutions. The existing scale-arbitrary super-resolution methods are primarily predicated [...] Read more.

The advancements in image super-resolution technology have led to its widespread use in remote sensing applications. However, there is currently a lack of a general solution for the reconstruction of satellite images at arbitrary resolutions. The existing scale-arbitrary super-resolution methods are primarily predicated on learning either a discrete representation (DR) or a continuous representation (CR) of the image, with DR retaining the sensitivity to resolution and CR guaranteeing the generalization of the model. In this paper, we propose a novel image representation that combines the discrete and continuous representation, known as CDCR, which enables the extension of images to any desired resolution in a plug-and-play manner. CDCR consists of two components: a CR-based dense prediction that gathers more available information and a DR-based resolution-specific refinement that adjusts the predicted values of local pixels. Furthermore, we introduce a scale cumulative ascent (SCA) method, which enhances the performance of the dense prediction and improves the accuracy of the generated images at ultra-high magnifications. The efficacy and dependability of CDCR are substantiated by extensive experiments conducted on multiple remote sensing datasets, providing strong support for scenarios that require accurate images. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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Open AccessArticle

Raking over the Ashes—The Analysis of the LBA Ashmounds from NE Romania

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Casandra Brașoveanu

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Andrei Asăndulesei

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Radu-Alexandru Brunchi

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Felix-Adrian Tencariu

Remote Sens. 2023, 15(7), 1826; https://doi.org/10.3390/rs15071826 (registering DOI) - 29 Mar 2023

Abstract

During the end of the Bronze Age, the territory of present-day eastern Romania was occupied by Noua communities, belonging to the Noua-Sabatinovka-Coslogeni (NSC) cultural complex. Although these communities have left us a large number of archaeological sites, this period is rather poorly known [...] Read more.

During the end of the Bronze Age, the territory of present-day eastern Romania was occupied by Noua communities, belonging to the Noua-Sabatinovka-Coslogeni (NSC) cultural complex. Although these communities have left us a large number of archaeological sites, this period is rather poorly known and understood, mostly because the investigation of Late Bronze Age (LBA) sites is very rare, usually consisting of small test trenches or fieldwalks. The main characteristic of these communities and the subject of our study is represented by the so-called ashmounds (grey, quasi-circular spots, visible on the soil surface, with small elevations and diameters of 25–30 m), present inside most settlements. Our paper aims at highlighting the spatial characteristics of these sites, using GIS (Geographic Information System) tools, as well as aerial photographs, LiDAR (Light Detection and Ranging) measurements, magnetometry and geo-electrical methods, in order to identify the relationship existing between Noua communities and the inhabited environment, in the area known as the Jijia River catchment. Thus, our approach was able to outline the way in which the geographical peculiarities determined the establishment of new settlements, revealing that the human groups from the end of the Bronze Age preferred low terrains with smooth slopes, located in the immediate vicinity of the most important watercourse of the inhabited micro-area. Additionally, our geophysical studies allowed us to confirm the lack of ash located within the ashmound, as well as to signal the possibility that these features have become visible on the soil surface only due to the irreversible damage caused by intensive agricultural processes. Despite the small number of excavations, to this day an important number of studies have been dedicated to the communities and features in question; however, no analysis has yet been performed that unites the tools specific to GIS software with the usage of non-invasive methods (such as aerial photographs, LiDAR measurements and geophysical techniques). Full article

(This article belongs to the Special Issue High-Resolution Digital Elevation Models, GIS and Remote Sensing in Support of Landscape Archaeology Reconstruction, Dynamics and Management)

Open AccessTechnical Note

An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model

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Remote Sens. 2023, 15(7), 1825; https://doi.org/10.3390/rs15071825 (registering DOI) - 29 Mar 2023

Abstract

The crop model data assimilation approach has been acknowledged as an effective tool for monitoring crop growth and estimating yield. However, the choice of assimilated variables and the mismatch in scale between remotely sensed observations and crop model-simulated state variables have various effects [...] Read more.

The crop model data assimilation approach has been acknowledged as an effective tool for monitoring crop growth and estimating yield. However, the choice of assimilated variables and the mismatch in scale between remotely sensed observations and crop model-simulated state variables have various effects on the performance of yield estimation. This study aims to examine the accuracy of crop yield estimation through the joint assimilation of leaf area index (LAI) and soil moisture (SM) and to examine the scale effect between remotely sensed data and crop model simulations. To address these issues, we proposed an improved crop data-model assimilation (CDMA) framework, which integrates LAI and SM, as retrieved from remotely sensed data, into the World Food Studies (WOFOST) model using the ensemble Kalman filter (EnKF) approach for winter wheat yield estimation. The results showed that the yield estimation at a 10 m grid size outperformed that at a 500 m grid size, using the same assimilation strategy. Additionally, the winter wheat yield estimation accuracy was higher when using the bivariate data assimilation method (R² = 0.46, RMSE = 756 kg/ha) compared to the univariate method. In conclusion, our study highlights the advantages of joint assimilating LAI and SM for crop yield estimation and emphasizes the importance of finer spatial resolution in remotely sensed observations for crop yield estimation using the CDMA framework. The proposed approach would help to develop a high-accuracy crop yield monitoring system using optical and SAR retrieved parameters. Full article

(This article belongs to the Special Issue Crop Quantitative Monitoring with Remote Sensing)

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Open AccessArticle

On Doppler Shifts of Breaking Waves

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Yury Yu. Yurovsky

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Vladimir N. Kudryavtsev

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Semyon A. Grodsky

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Bertrand Chapron

Remote Sens. 2023, 15(7), 1824; https://doi.org/10.3390/rs15071824 (registering DOI) - 29 Mar 2023

Abstract

Field-tower-based observations were used to estimate the Doppler velocity of deep water plunging breaking waves. About 1000 breaking wave events observed by a synchronized video camera and dual-polarization Doppler continuous-wave Ka-band radar at incidence angles varying from 25 to 55 degrees and various [...] Read more.

Field-tower-based observations were used to estimate the Doppler velocity of deep water plunging breaking waves. About 1000 breaking wave events observed by a synchronized video camera and dual-polarization Doppler continuous-wave Ka-band radar at incidence angles varying from 25 to 55 degrees and various azimuths were analyzed using computer vision methods. Doppler velocities (DVs) associated with breaking waves were, for the first time, directly compared to whitecap optical velocities measured as the line-of-sight projection of the whitecap velocity vector (LOV). The DV and LOV were found correlated; however, the DV was systematically less than the LOV with the ratio dependent on the incidence angle and azimuth. The largest DVs observed at up-wave and down-wave directions were accompanied by an increase of the cross-section polarization ratio, HH/VV, up to 1, indicating a non-polarized backscattering mechanism. The observed DV was qualitatively reproduced in terms of a combination of fast specular (coherent) and slow non-specular (incoherent) returns from two planar sides of an asymmetric wedge-shaped breaker. The difference in roughness and tilt between breaker sides (the front face was rougher than the rear face) explained the observed DV asymmetry and was consistent with previously reported mean sea surface Doppler centroid data and normalized radar cross-section measurements. Full article

(This article belongs to the Special Issue Recent Advancements in Remote Sensing for Ocean Current)

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Open AccessArticle

Evaluation of Four Satellite Precipitation Products over Mainland China Using Spatial Correlation Analysis

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Remote Sens. 2023, 15(7), 1823; https://doi.org/10.3390/rs15071823 (registering DOI) - 29 Mar 2023

Abstract

The accuracy and reliability of satellite precipitation products (SPPs) are important for their applications. In this study, four recently presented SPPs, namely, GSMaP_Gauge, GSMaP_NRT, IMERG, and MSWEP, were evaluated against daily observations from 2344 gauges of mainland China from 2001 to 2018. Bivariate [...] Read more.

The accuracy and reliability of satellite precipitation products (SPPs) are important for their applications. In this study, four recently presented SPPs, namely, GSMaP_Gauge, GSMaP_NRT, IMERG, and MSWEP, were evaluated against daily observations from 2344 gauges of mainland China from 2001 to 2018. Bivariate Moran’s I (BMI), a method that has demonstrated high applicability in characterizing spatial correlation and dependence, was first used in research to assess their spatial correlations with gauge observations. Results from four conventional indices indicate that MSWEP exhibited the best performance, with a correlation coefficient of 0.78, an absolute deviation of 1.6, a relative bias of −5%, and a root mean square error of 5. Six precipitation indices were selected to further evaluate the spatial correlation between the SPPs and gauge observations. MSWEP demonstrated the best spatial correlation in annual total precipitation, annual precipitation days, continuous wet days, continuous dry days, and very wet day precipitation with global BMI of 0.95, 0.78, 0.78, 0.78, and 0.87, respectively. Meanwhile, IMERG showed superiority in terms of maximum daily precipitation with a global BMI value of 0.91. IMERG also exhibited superior performance in quantifying the annual count days that experience precipitation events exceeding 25 mm and 50 mm, with a global BMI of 0.96, 0.92. In four sub-regions, these products exhibited significant regional characteristics. MSWEP demonstrated the highest spatial correlation with gauge observations in terms of total and persistent indices in the four sub-regions, while IMERG had the highest global BMI for extreme indices. In general, global BMI can quantitatively compare the spatial correlation between SPPs and gauge observations. The Local Indicator of Spatial Association (LISA) cluster map provides clear visual representation of areas that are significantly overestimated or underestimated. These advantages make BMI a suitable method for SPPs assessment. Full article

(This article belongs to the Special Issue Effects of Stratosphere-Troposphere-Land-Ocean Interaction on the Atmospheric Environment and Ecosystem II)

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Open AccessArticle

Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes

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Gabriel Caballero

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Alejandro Pezzola

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Cristina Winschel

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Paolo Sanchez Angonova

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Alejandra Casella

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Luciano Orden

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Matías Salinero-Delgado

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Remote Sens. 2023, 15(7), 1822; https://doi.org/10.3390/rs15071822 (registering DOI) - 29 Mar 2023

Abstract

Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to [...] Read more.

Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical domain background and the all-weather imagery provided by radar systems, we propose a data fusion approach focused on the cross-correlation between radar and optical data streams. To do so, we analyzed several multiple-output Gaussian processes (MOGP) models and their ability to fuse efficiently Sentinel-1 (S1) Radar Vegetation Index (RVI) and Sentinel-2 (S2) vegetation water content (VWC) time series over a dry agri-environment in southern Argentina. MOGP models not only exploit the auto-correlations of S1 and S2 data streams independently but also the inter-channel cross-correlations. The S1 RVI and S2 VWC time series at the selected study sites being the inputs of the MOGP models proved to be closely correlated. Regarding the set of assessed models, the Convolutional Gaussian model (CONV) delivered noteworthy accurate data fusion results over winter wheat croplands belonging to the 2020 and 2021 campaigns (

N R M S E_{w h e a t 2020}

= 16.1%;

N R M S E_{w h e a t 2021}

= 10.1%). Posteriorly, we removed S2 observations from the S1 & S2 dataset corresponding to the complete phenological cycles of winter wheat from September to the end of December to simulate the presence of clouds in the scenes and applied the CONV model at the pixel level to reconstruct spatiotemporally-latent VWC maps. After applying the fusion strategy, the phenology of winter wheat was successfully recovered in the absence of optical data. Strong correlations were obtained between S2 VWC and S1 & S2 MOGP VWC reconstructed maps for the assessment dates (

{\bar{R^{2}}}_{w h e a t - 2020}

= 0.95,

{\bar{R^{2}}}_{w h e a t - 2021}

= 0.96). Altogether, the fusion of S1 SAR and S2 optical EO data streams with MOGP offers a powerful innovative approach for cropland trait monitoring over cloudy high-latitude regions. Full article

(This article belongs to the Special Issue Cropland Phenology Monitoring Based on Cloud-Computing Platforms)

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Open AccessReview

Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation

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Rafik Ghali

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Moulay A. Akhloufi

Remote Sens. 2023, 15(7), 1821; https://doi.org/10.3390/rs15071821 (registering DOI) - 29 Mar 2023

Abstract

The world has seen an increase in the number of wildland fires in recent years due to various factors. Experts warn that the number of wildland fires will continue to increase in the coming years, mainly because of climate change. Numerous safety mechanisms [...] Read more.

The world has seen an increase in the number of wildland fires in recent years due to various factors. Experts warn that the number of wildland fires will continue to increase in the coming years, mainly because of climate change. Numerous safety mechanisms such as remote fire detection systems based on deep learning models and vision transformers have been developed recently, showing promising solutions for these tasks. To the best of our knowledge, there are a limited number of published studies in the literature, which address the implementation of deep learning models for wildland fire classification, detection, and segmentation tasks. As such, in this paper, we present an up-to-date and comprehensive review and analysis of these vision methods and their performances. First, previous works related to wildland fire classification, detection, and segmentation based on deep learning including vision transformers are reviewed. Then, the most popular and public datasets used for these tasks are presented. Finally, this review discusses the challenges present in existing works. Our analysis shows how deep learning approaches outperform traditional machine learning methods and can significantly improve the performance in detecting, segmenting, and classifying wildfires. In addition, we present the main research gaps and future directions for researchers to develop more accurate models in these fields. Full article

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Open AccessArticle

Shadow Enhancement Using 2D Dynamic Stochastic Resonance for Hyperspectral Image Classification

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Qiuyue Liu

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Min Fu

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Xuefeng Liu

Remote Sens. 2023, 15(7), 1820; https://doi.org/10.3390/rs15071820 (registering DOI) - 29 Mar 2023

Abstract

With the development of remote sensing technology, classification has become a meaningful way to explore the rich information in hyperspectral images (HSIs). However, various environmental factors may cause noise and shadow areas in HSIs, resulting in weak signals and difficulties in fully utilizing [...] Read more.

With the development of remote sensing technology, classification has become a meaningful way to explore the rich information in hyperspectral images (HSIs). However, various environmental factors may cause noise and shadow areas in HSIs, resulting in weak signals and difficulties in fully utilizing information. In addition, classification methods based on deep learning have made considerable progress, but features extracted from most networks have much redundancy. Therefore, a method based on two-dimensional dynamic stochastic resonance (2D DSR) shadow enhancement and convolutional neural network (CNN) classification combined with an attention mechanism (AM) for HSIs is proposed in this paper. Firstly, to protect the spatial correlation of HSIs, an iterative equation of 2D DSR based on the pixel neighborhood relationship was derived, which made it possible to perform matrix SR in the spatial dimension of the image, instead of one-dimensional vector resonance. Secondly, by using the noise in the shadow area to generate resonance, 2D DSR can help increase the signals in the shadow regions by preserving the spatial characteristics, and enhanced HSIs can be obtained. Then, a 3DCNN embedded with two efficient channel attention (ECA) modules and one convolutional block attention module (CBAM) was designed to make the most of critical features that significantly affect the classification accuracy by giving different weights. Finally, the performance of the proposed method was evaluated on a real-world HSI, and comparative studies were carried out. The experimental results showed that the proposed approach has promising prospects in HSIs’ shadow enhancement and information mining. Full article

(This article belongs to the Special Issue Feature Extraction and Data Classification in Hyperspectral Imaging II)

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Open AccessArticle

A New Method for Hour-by-Hour Bias Adjustment of Satellite Precipitation Estimates over Mainland China

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Remote Sens. 2023, 15(7), 1819; https://doi.org/10.3390/rs15071819 (registering DOI) - 29 Mar 2023

Abstract

Highly accurate near-real-time satellite precipitation estimates (SPEs) are important for hydrological forecasting and disaster warning. The near-real quantitative precipitation estimates (REGC) of the recently developed Chinese geostationary meteorological satellite Fengyun 4A (FY4A) have the advantage of high spatial and temporal resolution, but there [...] Read more.

Highly accurate near-real-time satellite precipitation estimates (SPEs) are important for hydrological forecasting and disaster warning. The near-real quantitative precipitation estimates (REGC) of the recently developed Chinese geostationary meteorological satellite Fengyun 4A (FY4A) have the advantage of high spatial and temporal resolution, but there are errors and uncertainties to some extent. In this paper, a self-adaptive ill-posed least squares scheme based on sequential processing (SISP) is proposed and practiced in mainland China to correct the real-time biases of REGC hour by hour. Specifically, the scheme adaptively acquires sample data by setting temporal and spatial windows and constructs an error-correction model based on the ill-posed least squares method from the perspectives of climate regions, topography, and rainfall intensity. The model adopts the sequential idea to update satellite precipitation data within time windows on an hour-by-hour basis and can correct the biases of real-time satellite precipitation data using dynamically changing parameters, fully taking into account the influence of precipitation spatial and temporal variability. Only short-term historical data are needed to accurately rate the parameters. The results show that the SISP algorithm can significantly reduce the biases of the original REGC, in which the values of relative bias (RB) in mainland China are reduced from 11.2% to 3.3%, and the values of root mean square error (RMSE) are also reduced by about 17%. The SISP algorithm has a better correction in humid and semi-humid regions than in arid and semi-arid regions and is effective in reducing the negative biases of precipitation in each climate region. In terms of rain intensity, the SISP algorithm can improve the overestimation of satellite precipitation estimates for low rain intensity (0.2–1 mm/h), but the correction for high rain intensity (>1 mm/h) needs further improvement. The error component analysis shows that the SISP algorithm can effectively correct the hit bias. This study serves as a valuable reference for real-time bias correction using short-term accumulated precipitation data. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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Open AccessArticle

Exploring the Use of Orthophotos in Google Earth Engine for Very High-Resolution Mapping of Impervious Surfaces: A Data Fusion Approach in Wuppertal, Germany

by

Jan-Philipp Langenkamp

and

Andreas Rienow

Remote Sens. 2023, 15(7), 1818; https://doi.org/10.3390/rs15071818 (registering DOI) - 29 Mar 2023

Abstract

Germany aims to reduce soil sealing to under 30 hectares per day by 2030 to address negative environmental impacts from the expansion of impervious surfaces. As cities adapt to climate change, spatially explicit very high-resolution information about the distribution of impervious surfaces is [...] Read more.

Germany aims to reduce soil sealing to under 30 hectares per day by 2030 to address negative environmental impacts from the expansion of impervious surfaces. As cities adapt to climate change, spatially explicit very high-resolution information about the distribution of impervious surfaces is becoming increasingly important for urban planning and decision-making. This study proposes a method for mapping impervious surfaces in Google Earth Engine (GEE) using a data fusion approach of 0.9 m colour-infrared true orthophotos, digital elevation models, and vector data. We conducted a pixel-based random forest (RF) classification utilizing spectral indices, Grey-Level Co-occurrence Matrix texture features, and topographic features. Impervious surfaces were mapped with 0.9 m precision resulting in an Overall Accuracy of 92.31% and Kappa-Coefficient of 84.62%. To address challenges posed by high-resolution imagery, we superimposed the RF classification results with land use data from Germany’s Authoritative Real Estate Cadastre Information System (ALKIS). The results show that 25.26% of the city of Wuppertal is covered by impervious surfaces coinciding with a government-funded study from 2020 based on Sentinel-2 Copernicus data that defined a proportion of 25.22% as built-up area. This demonstrates the effectiveness of our method for semi-automated mapping of impervious surfaces in GEE to support urban planning on a local to regional scale. Full article

(This article belongs to the Special Issue Urban Planning Supported by Remote Sensing Technology)

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Open AccessArticle

Segmentation of Sandplain Lupin Weeds from Morphologically Similar Narrow-Leafed Lupins in the Field

by

,

,

,

,

,

and

Remote Sens. 2023, 15(7), 1817; https://doi.org/10.3390/rs15071817 (registering DOI) - 29 Mar 2023

Abstract

Narrow-leafed lupin (Lupinus angustifolius) is an important dryland crop, providing a protein source in global grain markets. While agronomic practices have successfully controlled many dicot weeds among narrow-leafed lupins, the closely related sandplain lupin (Lupinus cosentinii) has proven difficult [...] Read more.

Narrow-leafed lupin (Lupinus angustifolius) is an important dryland crop, providing a protein source in global grain markets. While agronomic practices have successfully controlled many dicot weeds among narrow-leafed lupins, the closely related sandplain lupin (Lupinus cosentinii) has proven difficult to control, reducing yield and harvest quality. Here, we successfully trained a segmentation model to detect sandplain lupins and differentiate them from narrow-leafed lupins under field conditions. The deep learning model was trained using 9171 images collected from a field site in the Western Australian grain belt. Images were collected using an unoccupied aerial vehicle at heights of 4, 10, and 20 m. The dataset was supplemented with images sourced from the WeedAI database, which were collected at 1.5 m. The resultant model had an average precision of 0.86, intersection over union of 0.60, and F1 score of 0.70 for segmenting the narrow-leafed and sandplain lupins across the multiple datasets. Images collected at a closer range and showing plants at an early developmental stage had significantly higher precision and recall scores (p-value < 0.05), indicating image collection methods and plant developmental stages play a substantial role in the model performance. Nonetheless, the model identified 80.3% of the sandplain lupins on average, with a low variation (±6.13%) in performance across the 5 datasets. The results presented in this study contribute to the development of precision weed management systems within morphologically similar crops, particularly for sandplain lupin detection, supporting future narrow-leafed lupin grain yield and quality. Full article

(This article belongs to the Special Issue Advances in Agricultural Remote Sensing and Artificial Intelligence)

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Open AccessArticle

Long-Term Changes in Water Body Area Dynamic and Driving Factors in the Middle-Lower Yangtze Plain Based on Multi-Source Remote Sensing Data

by

,

,

and

Remote Sens. 2023, 15(7), 1816; https://doi.org/10.3390/rs15071816 - 29 Mar 2023

Abstract

The accurate monitoring of long-term spatial and temporal changes in open-surface water bodies offers important guidance for water resource security and management. In the middle and lower reaches of the Yangtze River, the monitoring of water body changes is especially critical due to [...] Read more.

The accurate monitoring of long-term spatial and temporal changes in open-surface water bodies offers important guidance for water resource security and management. In the middle and lower reaches of the Yangtze River, the monitoring of water body changes is especially critical due to the dense population and drastic climate change. Due to the complexity of the physical environment in which the water bodies are located, the advantages and disadvantages of various water body detection rules can vary in large-scale areas. In this paper, we use Landsat 5/7/8 data to extract the area of water bodies in the study area and analyze their spatial and temporal trends from 1984 to 2020 using the Google Earth Engine (GEE) platform. We propose an improved water body extraction rule based on an existing multi-indicator water body algorithm that combines impervious surface data and digital elevation model data. In this study, the performance of the improved algorithm was cross-validated using seven other water body indicator algorithms, and the results showed the following: (1) the rule accurately retained information about the water body while minimizing the interference of shadows on the extracted water body. (2) On the annual scale from 1984 to 2020, the open-surface water body dataset extracted using this improved rule showed that the turning point for the area of each water body type was 2011, with an overall decreasing trend in area before 2011 and an increasing trend in area after 2011, with the exception of special years, such as 1998. (3) The driving mechanism analysis showed that, overall, precipitation was positively correlated with the water body area and temperature was negatively correlated with the water body area. Additionally, human activities can have an impact on surface water dynamics. The key influencing factors are diverse for each water body type; it was found that seasonal water bodies were correlated with precipitation and paddy fields and permanent water bodies were correlated with temperature and urban construction. The accurate monitoring of the spatial and temporal dynamics of open-surface water performed in this study can shed light on the sustainable development of water resources and the environment. Full article

(This article belongs to the Special Issue Remote Sensing for Mapping Global Land Surface Parameters)

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Open AccessArticle

Ground Displacements in NY Using Persistent Scatterer Interferometric Synthetic Aperture Radar and Comparison of X- and C-Band Data

by

Yusuf Eshqi Molan

,

Rowena Lohman

and

Matthew Pritchard

Remote Sens. 2023, 15(7), 1815; https://doi.org/10.3390/rs15071815 - 29 Mar 2023

Abstract

In this study, we investigated the quality of Interferometric Synthetic Aperture Radar (InSAR) data to measure surface displacements in upstate New York, an area with dense vegetation, snowy winters, and strong seasonal signals. We used data from the German Space Agency’s TerraSAR-X and [...] Read more.

In this study, we investigated the quality of Interferometric Synthetic Aperture Radar (InSAR) data to measure surface displacements in upstate New York, an area with dense vegetation, snowy winters, and strong seasonal signals. We used data from the German Space Agency’s TerraSAR-X and TanDEM-X satellites (X-band, 3.1 cm radar wavelength) as well as the European Space Agency’s Sentinel-1 satellite (C-band, 5.6 cm radar wavelength); both datasets covered a ~3-year time period from 2018 to 2021. Using persistent scatterer interferometry (PSI), we were able to observe several deforming features in the region with sub-centimeter/year deformation rates. We also examined a version of the X-band data that we spatially averaged to the same pixel size as the Sentinel-1 imagery in order to separate out the effects of wavelength and pixel size on PSI accuracy and coverage. Overall, the largest number of stable PS points was found in the full-resolution X-band data, which was followed by the C-band data and then by the downsampled X-band data. Our analysis also included a subset of snow-free imagery so that we could assess the effect that snow-covered images had on the distribution and accuracy of PS points and the resulting time series. This analysis revealed that PS populations increased by 50–60% for the snow-free data when compared with analyses using the full datasets. The average deformation rates inferred from the time series generated using only snow-free images were nearly identical to those estimated from the full time series. We assessed the accuracy of the inferred rates through comparisons between the results of different datasets and with limited ground survey data. We found that all of the inferred deformation rates from each of the datasets agreed with in situ measurements in an area of known ground subsidence above an underground salt mine in Lansing, NY. The S1 datasets, however, had higher levels of noise. Full article

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