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Water vapour is an important atmospheric gas with a direct impact on climate and weather. Given the significant variations of water vapour distribution in the tridimensional and temporal space, it is of great significance to use remote sensing data to monitor it on a large scale. One of the main missions of the Medium Resolution Spectral Imager (MERSI) onboard FengYun (FY)-3A satellite is to monitor the distribution of precipitable water vapour (PWV). However, the official MERSI PWV data is not widely used due to the limitation of its accuracy. The primary objective of this study is to develop FY-3A/MERSI PWV data with higher accuracy. The study area of this paper is the continental United States (125°W–65°W, 25°–50°N), and the selected period is the year 2010. Three PWV retrieval models for three near-infrared (NIR) channels (0.905, 0.940 and 0.980 μm) of MERSI are constructed based on the matching results between MERSI data and SuomiNET PWV data. Compared with the existing semi-empirical PWV retrieval algorithms, the algorithm developed in this work has two main changes: (1) MERSI PWV retrieval models are constructed using data after removing outliers rather than all data. (2) The algorithm can identify abnormal retrieval results. The validation results based on PWV data derived from Aerosol Robotic NETwork (AERONET) indicate that these two changes both contribute positively to enhancing the accuracy of MERSI PWV data. The root mean square errors (RMSE) of MERSI PWV data developed using the three channels at 0.905, 0.940 and 0.980 μm are 0.28 cm, 0.22 cm and 0.24 cm, respectively. The relative errors (RE) of these PWV data are 0.14, 0.10 and 0.12, respectively. In comparison to the official MERSI PWV data, the RMSE of MERSI PWV data retrieved in this study is reduced by at least 44%, and RE is reduced by at least 42%. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) have transformed the field of remote sensing by providing researchers with a wealth of information about the Earth's surface. However, analyzing these images can be an overwhelming task due to the presence of overlapping areas, nested regions, and large intra-class variability. Hyperspectral image classification (HSIC) is a crucial part of identifying the various land cover classes present in hyperspectral images. In order to enhance the accuracy of HSIC, researchers utilize the potential of three-dimensional convolutional neural networks (3D-CNNs). With the ability to influence both the spectral and spatial data present in HSIs, 3D-CNNs provide a promising solution to overcome the challenges associated with HSIC. In this paper, a new method for key band selection is proposed to improve the performance of 3D-CNN model. The proposed method selects the most relevant key bands based on Walsh-Hadamard kernel strength features. These key bands are then used to extract overlapping 3D spatial patches, which serve as input to the proposed 3D-CNN model. To evaluate the performance of the 3D-CNN model six standard benchmark datasets are used. The effectiveness of the proposed method improves the performance of 3D-CNN for HSIC. [ABSTRACT FROM AUTHOR]

The overlap between edge computing and unmanned aerial systems enables Unmanned Aerial Vehicles (UAV) to quickly offload image processing tasks onto edge devices, avoiding the transmission of images over long distances. To improve the speed and efficiency of UAV image stitching in an edge computing environment, this paper proposes an adaptive UAV image parallel stitching algorithm in an edge computing environment. The algorithm incorporates both route-based parallel processing and inverted binary tree-based parallel processing, dividing the image stitching task into multiple processes and allocating them to different cores based on the CPU core count, number of flight routes, and number of images, thereby enhancing computational efficiency in edge scenarios. The experimental results indicate that, when the number of flight routes is greater than or equal to the number of CPUs, the adaptive algorithm will employ the more efficient route parallelism. Conversely, when the number of flight routes is less than the number of CPUs, the efficiency of inverted binary tree parallelism is higher. In the same experimental environment and dataset, the adaptive image stitching algorithm demonstrates an efficiency improvement of approximately 2–10 times compared to other algorithms, with no significant degradation in image quality. This demonstrates that in edge environments, the utilization of multi-threaded adaptive route and inverted binary tree-based parallel approaches can effectively harness the computing resources of edge devices, significantly improving the stitching speed of UAV images and providing technical support for rapid real-time monitoring by UAV. [ABSTRACT FROM AUTHOR]

The purpose of pan-sharpening is to generate high-resolution multispectral (HRMS) images by combining multispectral images (MS) and panchromatic images (PAN), which have become an important part of remote sensing image processing. Therefore, how to better extract complete feature information from MS images and PAN images has become the focus of our attention. In this paper, we propose a new pansharpening method, called Transformer-based Dual-path cross fusion network (TDF) for Pan-sharpening remote sensing images, which aims to extract the spatial details of PAN images while maintaining the spectral fidelity of MS images. The whole network structure can be divided into two parts: in the encoder part, we adopt the Swin-Transformer module for the downsampling operation, which expands the sensory field of the network to the feature map, and then extracts the global information. However, since the Swin-Transformer module is not good at extracting pixel-level details, we introduce the Base Feature Extraction (BFE) and the Invertible Neural Network Block (INNB) modules for the interaction between the local and the global feature information. we also introduce the Edge-Enhancement Block (EEB) to further enhance the feature extraction at multi-scales during the image fusion process. In the decoder section, we once again employ the Swin Transformer module for downsampling. After the convolution operation and activation function, we utilize the Sub-Pixel Convolutional Neural Network for upsampling to generate the ultimate high-resolution multispectral images. Simulation experiments and real experiments are conducted on QuickBird (QB) and WorldView2 (WV2) datasets, which demonstrated our method are superior to the current methods. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for automatically generating 3D models of individual shrubs from point clouds acquired by Terrestrial Laser Scanning (TLS) devices. The creation of accurate 3D models for shrubs holds significant value for ecological studies. The shrub modelling is more challenging compared to other tree modelling tasks, due to the presence of substantial noise and fuzzy branch structures. To address these challenges, our approach first partitions the input point cloud into two categories: branches and others. This point segmentation process effectively eliminates interference of leaves and noise, thereby enhancing the visibility of branch skeletons. Then, we construct a triangulation network using the branch points and establish a minimum spanning tree (MST) based on this network. Serving as the initial skeleton representation of the plant, the MST preserves the essential topological structures of the branches. Within the extracted MST, we implement a recursive trimming technique to eliminate redundant branches by merging adjacent points and edges, ultimately consolidating the skeleton structure. Finally, we employ an adaptive cylinder fitting algorithm that relies on robust principal component analysis (RPCA) to generate the model. The effectiveness and robustness of the proposed method are demonstrated via experiments on different datasets, with an average fitting error of 1.13 cm, indicating that our approach can achieve high accuracy in modelling individual shrubs. [ABSTRACT FROM AUTHOR]

Accurate and reliable cloud detection is one of the crucial preprocessing steps in satellite remote sensing. This paper adopts the spectral data in different channels observed by Himawari-8 geostationary satellite and proposes a cloud detection algorithm based on K-means++ clustering with a sharpening convolutional module (C-Kmeans++). The analysis found that the sum of the reflectance of channels 3 and 4, the brightness temperature of channel 15 and the difference of brightness temperature of channels 7 and 14 can be used as clustering features of the K-means++ algorithm. Before the clustering operation, the RS image is sharpened using the Laplacian operator to enhance the precision in the detection of thin clouds. A proper k value (k = 7) is obtained by the sum of squared error and silhouette coefficient. To evaluate the C-Kmeans++, the comparison of detection results using K-means++, the multi-spectral threshold, the k-nearest neighbour and the Vgg16+U-Net was carried out. The visible light image and Cloud-Aerosol Lidar with Orthogonal Polarization product were used as reference labels. It is found that RR, ER, HR and KSS of C-Kmeans++ are best throughout the day. [ABSTRACT FROM AUTHOR]

Maritime ship classification is essential for effectively monitoring oceanic activities but faces challenges when using heterogeneous remote sensing data. This research presents a novel dataset called Heterogeneous Ship Data, created by combining synthetic aperture radar (SAR) and optical satellite images of ships. The integration of multimodal data aims to harness the complementary advantages of SAR's all-weather imaging capabilities and optical data's higher interretability and shape discrimination. To leverage the heterogeneous dataset, this paper proposes an ensemble deep learning model called MS3Net (Multi-convolutional network using Spatial attention and Second-order pooling for Ship image classification). MS3Net integrates spatial attention mechanisms and second-order pooling. MS3Net has multiple convolutional branches, each tailored to extract distinct representations from the SAR and optical data through varied filter sizes and kernels. The spatial attention units focus the learning on the most informative parts of the image while suppressing irrelevant features. The second-order pooling captures higher-order interactions between feature elements to model complex relationships. A concatenation fusion strategy aggregates the diverse branch outputs into a joint multimodal feature representation for enhanced discrimination. Extensive experiments demonstrate MS3Net's superior classification accuracy over prior state-of-the-art approaches. The proposed heterogeneous data representation coupled with the attention-based deep ensemble learning model contributes to maritime surveillance by enabling robust ship recognition across diverse sensing modalities. [ABSTRACT FROM AUTHOR]

Sea surface windspeed is one of the important factors that affects the reflection and radiation intensity of the sea surface oil film, posing challenges for the oil spill classification via marine optical remote sensing. A new method is proposed to extract significant windspeed features from the original hyperspectral remote sensing data and apply them to the qualitative analysis and quantitative estimation of local windspeed. First, a portable push-broom hyperspectral sensor was utilized to collect the hyperspectral data under different experimental windspeed conditions. After that the data was divided into spatial-spectral hyperspectral data along the spectrum slit direction and finally converted into spatial-spectral joint heat maps (SSHMs). Incorporating a transfer learning methodology, we utilized SSHMs as input to optimize the training process of windspeed estimation. This approach yielded dependable evaluation results. Moreover, we developed an effective deep learning framework that integrates spatial and spectral analyses for the categorization of sea surface oil films. This framework utilizes hyperspectral image datasets which have already removed the influence of windspeed variables. The proposed method demonstrated outstanding performance in estimating windspeed and classifying oil films using both experimental and AVIRIS datasets. This paper introduces a feasibility study for ocean surface windspeed estimation and precise identification of oil spills using optical remote sensing technology. [ABSTRACT FROM AUTHOR]

As an important load of airborne/spaceborne passive microwave remote sensing, fully polarized microwave radiometer is widely used to the measurement of wind vector and salinity on sea surface. The retrieval of ocean surface parameters requires high accuracy data of radiometer. In recent years, the data correction scheme of fully polarized radiometer is common, but the attitude problem of radiometer is not paid enough attention. This paper establishes a full link transfer model for a fully polarized microwave radiometer, and focus on analysing the impact of aircraft attitude on various parameters. This paper aims to draw researchers' attention to attitude control of fully polarized microwave radiometer. Through the model established in this paper, the influence of attitude on fully polarized microwave radiometers is clarified and corrected, then rapidly correction can be achieved. Through simulation model, the impact of different attitudes on data can be calculated in advance before flight tests, and can provide guidance for attitude control. Using the corrected data for wind vector analysis, the deviation of wind direction is within 10°, which also verifies the accuracy of the model in this paper. The model proposed in this paper can achieve fast and accurate correction of radiometer parameters and the error prediction caused by attitude, which provides a strong guarantee for the inversion of sea surface parameters. [ABSTRACT FROM AUTHOR]

This paper examines different geographically weighted (GW) approaches for calculating spatially distributed measures of accuracy / uncertainty, consolidating current approaches and proposing 2 new ones. GW frameworks use a moving window or kernel to extract and weight data subsets, from which local (ie spatially distributed) statistics or metrics are calculated. A validation dataset with hard and soft classifications is used to illustrate the approaches. It contains observed field survey data (also commonly derived from higher resolution imagery), and predicted data from a fuzzy c-means classification. The hard classes were used to estimate spatially distributed measures of overall, user's and producer's accuracies in two ways. First, by conceptualising them as probabilities to be estimated from generalised linear regression models (GLMs), extended into Geographically Weighted GLMs. Second, by constructing local GW correspondence matrices and then calculating local accuracy measures from these. The soft classes were used to calculate per-class measures of fuzzy certainty from the absolute difference between predicted and observed fuzzy memberships. Then, a novel fuzzy certainty logic is proposed and used to create fuzzy confusion matrices and per-class measures of fuzzy omission and commission error, supporting measures of fuzzy user's and producer's certainties. These were extended to the GW case to generate spatially distributed measures. Finally, the soft classifications were conceptualised as compositional data and measures of difference were estimated using Aitchison distances. In each case, the local hard and soft accuracy and certainty measures were interpolated over a 1 km grid to estimate accuracy surfaces. The context for this review is the increasing operational use of training and validation data, often with high numbers of records, containing both hard and soft classes. The data and R code used to undertake all the analyses in this paper are provided, supporting more nuanced analyses of such data. [ABSTRACT FROM AUTHOR]

Target segmentation of remote sensing images has always been a hotspot in image processing. This paper proposes a new semantic segmentation technology for remote sensing images, which uses Unet as the backbone and combines attention mechanism and feature enhancement module. The feature enhancement module can enlarge the information of the region of interest (ROI) to improve the contrast of the image; the attention mechanism includes spatial and channel attention modules, which can obtain more detailed information of the desired target while suppressing other useless information. This paper improves the loss function of the traditional Unet. On the basis of the sparse categorical cross-entropy function, the mean squared logarithmic error function is added, which can effectively improve the accuracy of semantic segmentation. The experimental results show that the algorithm has higher computational accuracy than Unet, DeepLabV3, SegNet, PSPNet, CBAM and DAnet while having the computational speed of FCN and Unet in model testing and validation. [ABSTRACT FROM AUTHOR]

A tropical cyclone (TC) is a meteorological disaster that occurs over tropical or subtropical oceans. Automatic TC identification from a satellite image is essential to the subsequent automatic intelligent determination of TC positioning and intensity. Few recent studies have examined the automatic detection of TCs from satellite images. In this paper, wavelet (W) transformation, efficient (E) channel attention mechanism and YOLOX (Y) are combined to construct a deep learning model for TC detection, referred to as WEY-TCNet. WEY-TCNet automatically detects TCs from infrared satellite images. To improve the detection accuracy, a discrete wavelet transform is first used to decompose the infrared satellite image, after which the horizontal high-frequency components, including the TC rainband structure and inner core area, are extracted and fused with the original image. This step enhances the TC structure and brightness temperature gradient characterization. The fused image is then used as the input to WEY-TCNet. The ECANet channel attention mechanism was added to the original YOLOX-S network structure to focus on the key features of TC, such as structure and brightness temperature while suppressing invalid features and noise. This study constructed a satellite image dataset based on the infrared images of the Chinese Fengyun2D geostationary satellite. The dataset contains infrared satellite images of TCs in various life cycle stages, from generation to extinction. Our experiments showed that the WEY-TCNet model proposed in this paper achieves 91.49% detection mAP on the TC dataset, compared with the 86.69% detection mAP of the original YOLOX-S model, significantly improving it. [ABSTRACT FROM AUTHOR]

Underwater biological, optical and physical measurements have been carried out at the Acqua Alta Oceanographic Tower (AAOT) in the northern Adriatic Sea on behalf of the European project 'Coastal region long-term measurements for colour remote sensing development and validation' (COLORS). This project is aimed at studying the variability of bio-optical properties and testing existing bio-optical algorithms for the major water parameters. In particular, this paper deals with the analysis of underwater profiles of optical and physical parameters, which are aimed at studying the seasonal development of stratification in the water column. The first part of this paper describes the analysis of long-term series of physical measurements, such as temperature, salinity and conductivity, to enhance the development of stratification. The influence of stratification on the optical properties of the water column is discussed in the second part of the paper, and illustration is made of the application of the previous results to the analysis of the underwater profiles of downwelling irradiance and upwelling radiance. [ABSTRACT FROM AUTHOR]

The imaging spectrometer MOS on IRS-P3 was launched in March 1996 as the first example of a new generation of ocean colour sensors. It consists of three different spectrometers in the visible/near-infrared spectral region with 18 channels. The IRS-P3 mission is focused on the remote sensing of case 2 water, particularly the derivation of different water constituents in coastal waters. Due to the more complex spectral behaviour of case 2 water, a new methodological approach was developed which works directly with satellite measured top-of-atmosphere radiance and accounts for the correlation of the different water constituents as well as for the spectral shape. This paper gives an overview of the mission, the scientific goals and the development and improvement of the retrieval algorithms. The potential of the algorithm is demonstrated and examples of selected European coasts are shown. Derived maps of water constituents are presented. [ABSTRACT FROM AUTHOR]

This paper presents a study of the characteristics of extra-tropical oceanic Rossby waves from datasets of Sea Surface Height (SSH), Sea Surface Temperature (SST) and ocean colour. The main focus is on the propagation speed of the waves and a comparison is made between the observational results and the speeds predicted by the classical theory and by the most recent extended theory of Rossby waves. There is also discussion, with an example, of the additional information that can be derived by a comparison of the wave signatures in the different datasets. [ABSTRACT FROM AUTHOR]

The present paper proposes an automated approach to estimate the aerosol reflectance at the Advanced Very High Resolution Radiometer (AVHRR) red channel. The aerosol dominant pixels were separated through two orthogonal transforms. The aerosol reflectance ratio at these pixels was estimated through regression. The results are validated with in situ measurements. The retrieved water-leaving reflectance matched the modelled values with a relative error below 45%. The smallest error values were at the stations with the closest sampling time to image acquisition. However, a weak correlation of 16% was found between water-leaving reflectance and aerosol signals. This suggested that these errors could be attributed to the spatial and temporal variability between the two sampling methods (ship measurement and pixel reflectance). [ABSTRACT FROM AUTHOR]

Spatial injection-based pansharpening methods are prone to spatial or spectral distortions in pansharpening images due to insufficient extraction of spatial details and a mismatch between the amount of spatial detail information injected and the required amount. To this end, this paper proposes a pansharpening method that optimizes spatial detail extraction and injection. Firstly, a method to optimize the amount of spatial detail injection is proposed, that is, to extract the high-frequency information of the image through iterative filtering and determine the optimal number of iterations based on the global analysis of the method. Then, to fully extract and combine the spatial detail information of the source image, the detailed high-frequency image extracted corresponding to the optimal iterative filtering times is decomposed by non-subsampled shearlet transform (NSST), and a new multi-scale sum-modified-Laplacian (NSML) as an external stimulus to a parameter-adaptive pulse-coupled neural network model (PAPCNN). A fusion rule based on multi-scale morphological gradients is designed to extract a small amount of detailed information for the low-frequency subband. The fused spatial detail image can be obtained by combining the fused low-frequency and high-frequency subbands and inverse NSST transformation. Finally, pansharpening can be realized by combining spatial detail image, injection coefficient, and MS image. In this paper, many experiments are carried out on the QuickBird, GeoEye-1, and WorldView-4 datasets, and quantitative and qualitative comparisons are made with eight advanced methods. Experimental results show that the method proposed in this paper can achieve better fusion results. [ABSTRACT FROM AUTHOR]

With the widespread use of remote sensing images, pansharpening techniques have received increasing attention. Many pansharpening experiments have demonstrated that multiscale fusion effectively alleviates the problem of image detail loss, and the pyramidal structure is one of the manifestations of the multiscale spatial theory. This paper proposes a U-shaped network architecture based on the pyramid structure for pansharpening. We resample multispectral images and panchromatic images to generate two pyramid image sequences. The image pairs in the same sequence layer are double-merged in pixels and channels before extracting features. The pyramidal feature sequences undergo a U-shaped top-down downsampling fusion and bottom-up upsampling reconstruction to generate high-resolution multispectral images. In addition, this process also utilizes the dense network module to extract deep features and a lateral connection to shallow multiplex features. We conducted experimental tests on full-resolution and reduced-resolution datasets from WorldView-2, IKONOS, and GeoEye-1. The results confirm that the method in this paper can better trade off spectral and spatial features. The technique has better generalization in fusion results and advantages in objective and subjective aspects. [ABSTRACT FROM AUTHOR]

Infrared hyperspectral instrument is a complex optical instrument. The requirement for the application of the infrared hyperspectral interferometer is fine calibration accuracy. The accuracy of the spectral frequency of the infrared hyperspectral interferometer is a key influencing factor of the radiometric calibration accuracy. In order to meet the quantitative application, the spectral calibration accuracy is generally within 10ppm. For infrared hyperspectral instruments in geostationary orbit, the focal plane has the characteristics of large array and small detector size. When the detector size of an infrared hyperspectral interferometer is in the geostationary orbit whether the detector as a point detector will affect the spectral calibration accuracy. In this paper, we simulate and analyze the influence of the shape of the detector (square and point detector) on the spectral calibration accuracy. This paper first builds an infrared hyperspectral observation and calibration simulation model. The brightness temperature deviation between the calibration spectrum and the input spectrum is close to 0 K, which shows the rationality of building the simulation model. Then, in the process of spectral calibration, two methods, square detectors and point detectors, are adopted, respectively. The spectral calibration of the square detector needs to calculate the line shape function of the instrument according to the detector position. Regarding point detector spectral calibration, this paper proposes a spectral calibration parameter optimization algorithm based on the optimization algorithm, which does not depend on the physical parameters of the instrument, but only uses the observed simulated spectrum and the reference spectrum. The results show that the spectral calibration accuracy of the point detector based on the optimization method can be close to 0ppm, which is consistent with the spectral calibration accuracy of the square detector. [ABSTRACT FROM AUTHOR]

Finding a means to extract water body information efficiently and accurately from high-resolution remote sensing images has been an important research direction in the field of water body extraction in recent years. However, shadows from buildings and other obstacles interfere with the accuracy of water body extraction. To address this problem, this paper proposes a neural network method incorporating an attention mechanism for water body extraction. This paper is based on the U-Net convolutional neural network and adds the squeeze-and-excitation module of SENet, an attention mechanism, to the downsampling process of the U-Net network. The module weights the feature maps so that the network focuses more on the features of the water body information and thus reduces attention to the shadow features from buildings and other features, thus improving the accuracy of image segmentation. The dropout and batch normalization layers are also added to improve the generalization ability and stability of the model. In this paper, a water extraction network SE-CU-Net model is presented to overcome the shadowing effect from buildings and other features. Using GF-2 images of Jiangsu province as the data source, the recognition results of this paper are compared with Dense-Net, Res-Net, Seg-Net, U-net, SVM, and RF. Through the comparison experiments, the model of this paper can not only better overcome the influence of shadows from buildings and other features, but it also has a stronger recognition ability and recognition effect. The average ASCR, Precision, mIoU, OA, F1-Score and kappa coefficients in the three tested areas reached 98.27%, 97.17%, 89.33%, 98.2%, 89.3% and 0.883, respectively, with significantly higher accuracy than the other six classical methods, verifying the effectiveness of the model in overcoming the influence of shadows from buildings and other features in water body extraction research. [ABSTRACT FROM AUTHOR]

Change detection (CD) is a challenging task considering land surface cover changes. Recently, deep learning has been introduced into CD. However, these methods suffer from shortcomings such as insufficient utilization of shallow features and inadequate aggregation of semantic context, which result in pseudo changes and poor integrity of change objects. To address this, an enhanced shallow feature difference and semantic context network (ESDSCNet) is proposed in this study for remote sensing CD. It uses HRNet to extract multi-scale features from bi-temporal images to obtain shallow and deep features. To fully exploit the shallow features, they are input into the difference statistical texture learning (DSTL) module to extract more discriminative features. Subsequently, the features enhanced by DSTL are fed into the change object contextual representation (COCR) module along with the deep difference features extracted by HRNet to characterize the contextual information of the change object. To verify the performance of ESDSCNet in different scenes, this paper takes building change detection as a case and conducts experiments based on three datasets. The experimental results reveal that ESDSCNet outperforms six other advanced methods, regardless of the intersection over union or the harmonic mean of precision rate and recall rate, which further confirms the effectiveness of the proposed network. In addition, another speciality of this paper is that ESDSCNet can identify not only 'Where has changed' but also 'What has changed what', so it has potential application in semantic change detection besides building change detection. [ABSTRACT FROM AUTHOR]

With strong self-learning and data analysis capabilities, deep learning is essential in cloud detection. However, many high-quality samples are the key to deep learning cloud detection methods. Different satellite image cloud detection techniques need to select adequate representative and high-quality samples and make corresponding cloud masks, which not only requires professional knowledge but also consumes a lot of workforce and time. To improve the generalization ability of the deep learning cloud detection model and quickly apply it to the cloud detection of different satellite images, this paper proposes a deep learning cloud detection method based on spectral assimilation for multiple types of satellite images (SAUNetCD). Under the condition of using fewer deep learning data samples, the deep learning model is used to achieve automatic cloud detection of multiple satellite data. Taking Landsat 8 OLI, Landsat 9 OLI, GF-1 WFV, and Sentinel 2A as examples, this paper selects Landsat 8 OLI data as the source data of cloud detection. The experimental results show that the spectral assimilation method improves the generalization ability of the deep learning cloud detection model and improves the cloud detection accuracy by nearly 20%. It realizes the fast cloud detection application of different satellite images by deep learning. It provides an effective way for cloud detection of multiple types of satellite images. [ABSTRACT FROM AUTHOR]

Tea plants are evergreen broad-leaved perennial shrubs, and their spectral characteristics are very similar to those of other evergreen vegetation, making it difficult to distinguish them. Currently, the most commonly used method of tea plantation extraction is machine learning, which classifies tea plantation through various feature combinations and algorithms. The disadvantage of these methods is that they require a large number of local training samples, making it challenging to produce an accurate model applicable to a large region. Furthermore, complex feature combinations and indicators may result in over-fitting, reducing the accuracy of the results. Therefore, it is necessary to develop a new algorithm suitable for tea plantation extraction in extensive regions. This paper uses Shihe District, Henan Province, China as a case in 2019, combined with Landsat-7/8 and Sentinel-2A/B images, and develops a new phenological-based algorithm to extract the tea plantation area. Firstly, we generated an evergreen vegetation map. Secondly, based on high-quality time series curves, the tea plant growth period was divided into seven parts to extract phenological indicators for classification. Finally, the tea plantation in the study area was extracted on a per-pixel basis. The overall accuracy of the algorithm is 87.59%, and the Kappa coefficient is 0.80. This study demonstrates the potential of the phenology-algorithms in extracting tea plantation areas and provides an advanced scheme and scientific basis for extracting tea plantation in other years, and also offers a reference for identifying tea plantation in other regions. Additionally, this paper generated a map of classified phenological indicators to provide guidance for monitoring tea plants growth. [ABSTRACT FROM AUTHOR]

A combination of careful harmonic analysis and high-resolution numerical modelling of tides in altimeter data from TOPEX/Poseidon and ERS-2 satellites allows detailed calculation of seasonal and interannual variability of sea level in coastal areas of the north-east Pacific Ocean. These calculations show large changes in sea level in marginal seas during the 1997-8 El Niño and La Niña events, as well as providing information on the origin and life cycle of large anticyclonic eddies, observed to be generated off the west coast of Canada. [ABSTRACT FROM AUTHOR]

The aim of this research is to automatically detect and visualize dynamic ocean colour phenomena such as algae blooms, fronts and eddies from a sequence of cloudy satellite images. The composite front map methodology has been extended to combine feature observations from multiple ocean colour and temperature products in a single map, to explore the interaction between physical and biological oceanic processes. Sample maps showing chlorophyll, sediment and sea surface temperature fronts are presented, derived from long sequences of cloud-affected Advanced Very High Resolution Radiometer (AVHRR) and Sea viewing Wide Field of view Sensor (SeaWiFS) data. The successful detection and animation of many ocean colour features suggests a valuable application to summarize the increased multi-spectral data provided by the National Aeronautics & Space Administration's (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. [ABSTRACT FROM AUTHOR]

The development and validation of ocean colour products require the availability of in situ atmospheric and marine data. Since 1995, the Coastal Atmosphere and Sea Time Series (CoASTS) Program ensures the collection of an in situ comprehensive set of measurements taken from an oceanographic tower in the northern Adriatic Sea. An example of a validation exercise is presented through the comparison of in situ and satellite-derived aerosol optical thickness at various wavelengths in the 443-865 nm spectral interval. [ABSTRACT FROM AUTHOR]

A long-term time series of Advanced Very High Resolution Radiometer (AVHRR) (1981-1999) data has been used to assess the main physical features in the Adriatic Sea. Individual images were processed to estimate Sea Surface Temperature (SST) values, to create long-term composite fields (weekly, monthly, seasonal scales), and to derive basic statistics for the Northern, Central and Southern regions, each split again into an Eastern and a Western section. At the basin scale, an apparent general warming trend can be recognized in the time series. The linear fit to the seasonal cycles suggests an increase of about 2°C in 20 years, essentially due to a steady rise of summer values. A general north-south gradient can be found during winter, the Northern sections being colder than the Southern ones. An east-west gradient appears during summer, the Western sections being warmer then their Eastern ones. The Northern Adriatic exhibits substantial fluctuations, possibly linked to the cycle of winter cooling and summer warming in the relatively shallow sub-basin. The North Western section shows larger fluctuations than the North Eastern one, with lower winter SST, probably due to the freshwater inflow from the Po River delta. The Southern Adriatic exhibits less variability, possibly influenced by the periodic water exchanges with the Ionian Sea. The South Eastern section shows somewhat larger fluctuations than the South Western one, with higher winter SST, probably due to the inflow of warmer waters from the south. The two Central sections reveal patterns similar to the ones of the whole basin. The observed temperature patterns appear to follow the classical Adriatic cyclonic circulation scheme. [ABSTRACT FROM AUTHOR]

The bio-optical relationships between inherent and apparent optical properties, and between optical properties and phytoplankton pigment concentration ( C ) averaged in a layer (Δ Z ), were derived from analysis of data collected during the period 1996-1998 in the Gulf of Aqaba (Eilat). Parametrization of these relationships was based on radiative transfer theory, Gershun's equation, minimization of model errors by least-square fitting, and on known optical models relating underwater remote sensed reflectance ( R rsw ) with the ratio of backscattering ( b b ) to vertical attenuation coefficient ( K d ) [or to absorption coefficient ( a )]. These relationships explain a frequently used form of remote sensing algorithms for C estimation using ratio of water-leaving radiances measured at two or more wavelengths ( λ ). In this study, the possibility of using for this purpose a single wavelength in the blue range ( λ =443 nm) within the framework of in situ and remote sensing algorithms for Case 1 waters was assessed. [ABSTRACT FROM AUTHOR]

A sensor installation and evaluation programme at the Institute of Ocean Sciences, Sidney, BC, Canada, shows that the weather buoys operating round Canada's coasts can be used as important new sources of surface information for satellite sensor calibration and validation. The standard instruments on the buoys provide real-time measurements to forecasters--of wind, waves, water and air temperature and air pressure. The present programme is evaluating the addition of sensors measuring chlorophyll fluorescence, Photosynthetically Available Radiation (PAR), salinity and water-leaving radiance. Time-series of these data are also of value for a variety of fisheries, ecosystem and climate studies. Data can be viewed on the web at http://www-sci.pac.dfo-mpo.gc.ca/ecobuoys. [ABSTRACT FROM AUTHOR]

In summer 1999, an extensive survey of the tropical Atlantic ocean was operated during the EQUALANT99 cruise. Several experiments were carried out on board including, for the first time in that area, eXpendable Conductivity-Temperature-Depth (XCTD) launches. The aim of this experiment was first to evaluate XCTD temperature and salinity profiles, and secondly to check how XCTD could be used together with altimetric satellite missions in order to capture a good picture of the tropical Atlantic surface circulation. [ABSTRACT FROM AUTHOR]

The technique of vicarious calibration is used in connection with an atmospheric correction to improve the Sea viewing Wide Field of view Sensor (SeaWiFS) normalized water-leaving radiance by the first determination of mean vicarious calibration coefficients from in situ measurements in the Baltic Sea. A necessary adjustment of the SeaWiFS pre-flight calibration slope was found to be +3.5%, +0.3%, -1.7%, -0.4%, +0.8% and -1.3% for the first six SeaWiFS channels. The derived mean vicarious calibration coefficients are higher than the coefficients in the standard SeaWiFS Data Analysis System (SeaDAS) software but with similar shape and good agreement with other research results. The coefficients were used to obtain better normalized water-leaving radiance from SeaWiFS measurements in the Baltic Sea. The deviations of calculated to measured radiances in the open Baltic Sea are between 3% and 47% in the channels 412 to 670 nm, with the trend of higher deviations in the blue channels. The objective of radiance determination in all SeaWiFS channels with a maximum uncertainty of 5% in clear water regions is probably not reachable in the turbid water of the Baltic Sea. [ABSTRACT FROM AUTHOR]

A comparison of Synthetic Aperture Radar (SAR) images acquired by the ERS-2 and RADARSAT-1 satellites with Landsat Thematic Mapper (TM) images acquired close in time is presented. Similarities and differences on the kind of ocean information they provide are discussed. The images were acquired over the Brazil-Malvinas (Falkland) Confluence under different solar elevation and azimuth angles. Ocean features observed by both types of sensors are discussed. These observations show that (1) at high solar angles, sun glint prevails over upwelled water-leaving radiance resulting in optical images that tend to provide dynamical information similar to that obtained from SAR imagery and (2) at low solar zenith and azimuth angles, upwelled radiation tends to prevail over sun glint, providing typical ocean colour and water quality information that can still complement and aid in the interpretation of SAR observations. [ABSTRACT FROM AUTHOR]

Internal wave distribution maps have been compiled from more than one hundred ERS-1/2, RADARSAT and Space Shuttle SAR images in the South China Sea (SCS) from 1993 to 2000. Based on these distribution maps, most of the internal waves in the north-east part of the SCS were propagating westward. The wave crest can be as long as 200 km with an amplitude of 100 m, due to strong current from the Kuroshio branching out into the SCS. In a recent SCS internal wave study, moorings were deployed in April 1999 and 2000. Simultaneous SAR coverage from ERS-2 and RADARSAT were also collected. The ERS-2 high-resolution SAR images collected from the Taiwan ground station were processed in near real-time to coordinate the field test. SAR data are then used to compare with mooring data. SAR data and Acoustic Doppler Current Profiler (ADCP) data show internal solitons induced by the semi-diurnal tides with a wave speed of 2.4 m s -1 . [ABSTRACT FROM AUTHOR]

Satellite and in situ observations are used to test model dynamics for the California Current System (CCS). The model and data are combined to reconstruct the mesoscale ocean structure during a given three-week period. The resulting physical flow field is used to drive a 3D ecosystem model to interpret SeaWiFS and in situ chlorophyll- a (chl- a ) variations. With this approach a more complete and consistent picture of the physical and ecosystem processes of the CCS is obtained, providing the basis for addressing fundamental questions about dynamics and predictability of the coastal ocean. [ABSTRACT FROM AUTHOR]

Spectral in-water measurements of the downward irradiance, E d and the upward irradiance, E u are sufficient in order to calculate the following spectral quantities: the irradiance ratio R , the two scalar irradiances E o and E od , the two average cosines μ and μ d , the light absorption coefficient a , the backscattering coefficient b b and the vertical attenuation coefficients for the above-mentioned downward and upward irradiances. The algorithms are valid for horizontally stratified and optically deep sea waters. [ABSTRACT FROM AUTHOR]

Similar seasonal courses of pigment concentrations were found in all the regions of the Adriatic, from the Coastal Zone Color Scanner (CZCS) data. Maximum pigment concentrations were observed in the late autumn and in early winter. Individual chlorophyll measurements, from particular cruises, correlated well with the pigments retrieved from the satellite data. However, the monthly means, from longer observation periods, did not correspond well and it was found that the pigment values retrieved from CZCS were considerably higher than in situ measured chlorophyll concentrations for these months in the whole of the Adriatic. This is a result of hydro-geo-meteorological factors, namely mixing of the sediments, especially in the shallow northern Adriatic after strong winds, and the influence of coloured substances brought by the rivers. [ABSTRACT FROM AUTHOR]

The hydrographic conditions in the south-east Atlantic were investigated during the cruise of R/V Poseidon in April 1999. The area of investigation covers the two major eastern boundary currents of the Southern Atlantic and its convergence in the Angola-Benguela Frontal Zone (ABFZ). Hydrographic measurements have been carried out in combination with current measurements and are supplemented with remote sensing data of sea surface temperature (SST) and wind stress. The Angola Current transported in its surface part less saline water from the great rivers in the north towards the ABFZ. At the same time, south of the front, strong coastal upwelling in the Benguela was observed. At the beginning of April 1999 the ABFZ was found at an unusual southern position. The rapid dynamic response of the ABFZ to wind forcing is discussed on the basis of satellite SST images combined with hydrographic measurements. [ABSTRACT FROM AUTHOR]

The method used to identify sea surface thermal features for estimating surface currents is presented. Results given have been obtained from investigations made in the Central Adriatic Sea off the coast of Monte Conero in the Marche Region, Italy, using Advanced Very High Resolution Radiometer (AVHRR) observations of NOAA-14, made simultaneously with the HF coastal radar system installed there. [ABSTRACT FROM AUTHOR]

Variability of chlorophyll concentration in the ocean is one of the most important components of the primary production process on the planet. To preserve the biosphere and to make appropriate use of it, it is imperative to have a deep insight into the long-term dynamics of the primary production on the planet. To investigate the dynamics of chlorophyll concentration based on satellite data, the Institute of Biophysics (Russian Academy of Sciences, Siberian Branch) has developed a geoinformation system. It was used to detect the areas in the global ocean that are quasistationary in relation to seasonal dynamics of chlorophyll concentration. Areas such as these, found in the Indian Ocean, are described in this work. These areas form the basis for the analysis of long-term dynamics of chlorophyll concentration. In these quasistationary zones systematic monitoring of phytopigment concentration is conducted by space-borne and marine craft. The work presents long-term satellite-based data on the space distribution of anomalous deviations of chlorophyll concentration in the ocean. An anomaly criterion is proposed and maps of Pacific Ocean areas with a high probability of anomalies are presented. [ABSTRACT FROM AUTHOR]

The Space Information Laboratory (SIL) of the Tropical Center for Earth and Space Studies of the University of Puerto Rico at Mayagüez (UPRM) has been collecting and processing satellite data since December of 1996. Satellite imagery from the Advanced Very High Resolution Radiometer (AVHRR) and the Sea viewing Wide Field of view Sensor (SeaWiFS) provides us with a new understanding of phytoplankton dynamics in the Caribbean region. SeaWiFS shows the intrusion of waters into the eastern Caribbean Sea from the Orinoco River during fall and from the Amazon River during spring-summer. Strong coastal upwelling in Venezuela produced by the trade winds during winter-spring is detected with the AVHRR. The satellite data suggest that these seasonal events may play an important role in phytoplankton fertilization of the eastern Caribbean Sea. SeaWiFS and hydrological data are also combined to evaluate the impact of hurricanes on phytoplankton distribution. The development of models for estimation of ocean primary productivity using SeaWiFS and AVHRR data is now in progress. [ABSTRACT FROM AUTHOR]

A new generation of airborne salinity imager, the Salinity Temperature and Roughness Remote Scanner (STARRS), has been designed and is currently under construction with delivery expected shortly. With both significantly lower noise levels and enhanced capability to measure secondary parameters, including sea surface temperature and roughness, STARRS will serve as both an operational survey tool and as a test-bed for refinement of retrieval algorithms. [ABSTRACT FROM AUTHOR]

Various oscillatory modes of sea surface temperatures (SSTs) observed over a period of 8.8 years with the NASA Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) and for 13 years with the NOAA Advanced Very High Resolution Radiometer (AVHRR), the latter sensing in the thermal infrared band, are described for the Pacific Ocean. The various modes are isolated by a combination of techniques designed also to accommodate non-stationary phenomena. After detrending and removing the seasonal cycle from each grid map element of the data, singular value decomposition (SVD) is used to separate the data into spatial and temporal parts to facilitate the modal analysis. Empirical Mode Decomposition is then used to separate the temporal parts of the data into approximately seven intrinsic modal functions (IMFs) for the temporal parts of the first five principal components (PCs) resulting from the SVD. A filtered time sequence of SST grids is then obtained by selecting IMFs with periods longer than 1.5 years and then reconstructing the SST grid maps from the filtered PCs. The time sequence of SMMR SSTs in the Pacific Ocean shows El Niño Southern Oscillation (ENSO) oscillations not only along the Equator, but also in both the North and South Pacific, with, in fact, even larger amplitudes than along the Equator. A similar analysis was applied to the SST record from the AVHRR instrument. During the period of overlap with the SMMR record, similarities occur in the equatorial region, but the records are by no means identical. The AVHRR SSTs do not show any strong oscillations in the South Pacific. [ABSTRACT FROM AUTHOR]

The first images from MODIS (Moderate Resolution Imaging Spectroradiometer) are now being used to evaluate information on the in vivo fluorescence peak near 685 nm from chlorophyll- a , stimulated by sunlight. The Fluorescence Line Imager (FLI) airborne imaging spectrometer was used in the 1980s to demonstrate the mapping of this signal from an aircraft, showing that it gave significant rejection of confusing signals from atmospheric radiance. For imaging from space, a major limitation is sensor sensitivity, which tends to restrict imaging to relatively high concentrations under good solar illumination. Noise-equivalent chlorophyll concentrations for MODIS and MERIS (Medium Resolution Imaging Spectrometer) have been estimated as 0.07 and 0.1 mg m -3 , respectively, under zenith Sun, clear sky conditions. Although MERIS has slightly poorer sensitivity, it has the advantage of flexible band placing and presence of a band at 709 nm in the baseline band-set that allows better definition of the continuum spectrum above which fluorescence is measured. This band should also allow detection of bright plankton blooms (red-tide events) through the peak radiance near 709 nm caused by a combination of in-water scattering and absorption. [ABSTRACT FROM AUTHOR]

The Clean Seas project focused on the role that existing Earth observing satellites might play in monitoring marine pollution. Results are presented here from August 1997, for the North Sea test site, using sea surface temperature (SST), colour and synthetic aperture radar (SAR) images in conjunction with a hydrodynamic model. There was good correlation between data sources, e.g. between SST and ERS-2 SAR images. Both datasets showed the development of fine plume structures close to the Rhine outflow, apparently associated with the outflow, and possibly caused by tidal pulsing of the Rhine Plume. The model reproduced general temperature and sediment distributions well, but fine structures were not reproduced. Model sediment distribution patterns were verified using 'chlorophyll concentration' data from colour sensors, representative of sediment concentration in turbid water. In conjunction with the visible channels of the Advanced Very High Resolution Radiometer and Along-Track Scanning Radiometer, they give an uncalibrated measure of the sediment load. The model gives a more complete picture of the temporal dispersion of the Rhine Plume over time than is evident from the remotely sensed data alone. [ABSTRACT FROM AUTHOR]

Synoptic coverage of the temporal and spatial variability of aerosol distribution patterns can only be achieved with satellites. Results from the first ocean colour sensor, the Coastal Zone Color Scanner (CZCS), indicate an annual cycle of the major mineral aerosol plumes that is consistent with the published literature. Seasonality and interannual aerosol variability observed with the CZCS agrees well with that found by ground data measurements and other satellite platforms used to monitor aerosols. The successor to the CZCS--the Sea viewing Wide Field of view Sensor (SeaWiFS)--provides estimates of aerosol load and particle size, both on a global scale. Seasonal maps of both of these aerosol optical properties are in accord with well-known distribution patterns and also with independent satellite estimates. These results indicate that ocean colour sensors are capable of monitoring the variability of global aerosol loads and, more recently, with the retrieval of aerosol particle size, they can be used to characterize different aerosol events. [ABSTRACT FROM AUTHOR]

The aim of the research reported here is to evaluate Synthetic Aperture Radar (SAR) capability to estimate the wind vector and associated directional wave spectrum. Two ERS-2 SAR images of the Mediterranean Sea, one over the Sicily Channel and one over the Ligurian Sea, were selected as case studies. Wind speed was estimated using SAR calibrated backscatter response, in conjunction with empirically derived ERS scatterometer models such as CMOD4 and CMOD-IFREMER. The predictions of these models were then compared with the actual sea surface wave spectra either provided by in situ measurements or resulting from the inversion of the SAR image spectrum. SAR-detected effects of both wind and wave features, induced either by atmospheric boundary layer instability or by land shadowing, were also used as reliable indicators of wind direction. [ABSTRACT FROM AUTHOR]

The salinity of the open ocean is important for understanding ocean dynamics and for modelling energy exchange with the atmosphere. But existing data are sparse and much of the ocean is unsampled. Sea surface salinity can be measured remotely with passive microwave sensors operating near 1.4 GHz (L-band). Salinity differences have been observed from space and aircraft instruments have demonstrated that salinity can be measured with an accuracy of better than 1 psu. Sensor technology has improved sufficiently to seriously propose a satellite system to map salinity over the open oceans. [ABSTRACT FROM AUTHOR]

The Tropical Rainfall Mapping Mission Microwave Imager (TMI) instrument Sea Surface Temperature (SST) product (v1.0) is compared with in situ observations obtained in the Atlantic Ocean. The TMI SST has a mean warm bias of 0.25 K±0.7 K when compared to in situ SST at a depth of 7 m. When TMI SST are compared to in situ skin SST measurements, the bias is 0.6 K±0.5 K. A limited global comparison between TMI SST and co-incident ERS-2 Along-Track Scanning Radiometer (ATSR/2) skin SST demonstrates a bias of 0.6 K±0.6 K consistent with the result obtained using in situ observations. These results are consistent with the predicted accuracy of the TMI SST data products. Based on these results, a simple method to merge the TMI and ATSR data is proposed. [ABSTRACT FROM AUTHOR]

The 1997-1998 ENSO (El Niño-Southern Oscillation) was not only the largest event of the century but also the most comprehensively observed. Satellite data were employed for ocean colour, sea level, winds, sea surface temperature (SST), and outgoing longwave radiation (OLR) were used to describe the response of the surface marine ecosystem associated with the ENSO event. Some of the large-scale anomalies in ocean colour include elevated biological activity to the north of the Equator in the Pacific coincident with lower sea levels associated with the classic ENSO-horseshoe pattern ecosystem response to the anomalous upwelling in the eastern Indian Ocean caused by the 1997-1998 dipole event, and the dramatic eastward propagating feature in the Equatorial Pacific in response to the La Niña dynamics. Ocean general circulation model (OGCM) experiments show that capturing the high-frequency wind changes is crucial for simulating the La Niña and the coupled biological-physical model (OBGCM) runs clearly show that higher frequency winds are also important for capturing the mean upwelling and nutrient supply into the euphotic zone. Thus, the QuickSCAT winds are expected to play a major role in ecosystem modelling in the future. This study shows the utility of satellite data for understanding not only ocean circulation but also the coupled ecosystem variability. Morcover, it is also shown that spatio-temporal resolution of the satellite winds will directly affect the accuracy of oceanic and ecosystem simulations. [ABSTRACT FROM AUTHOR]

The first two years of SeaWiFS (Sea viewing Wide Field of view Sensor) data (1997-1999) are used to document the variability of large-scale surface chlorophyll patterns within the coastal region along the full latitudinal extent of each of the four major global eastern boundary currents; the California, Humboldt, Benguela and Canary Currents. Seasonal chlorophyll patterns are compared to coincident seasonal cycles of Ekman transport calculated from satellite scatterometer data. In all four regions, maximum chlorophyll concentrations are generally temporally and latitudinally coincident with the seasonal maximum in upwelling (offshore Ekman transport) over most of their latitudinal range, but exceptions are documented. Interannual differences are evident in each region, most notably in the two Pacific regions where the 1997-1998 chlorophyll seasonality was affected by El Niño conditions. Significant differences between previously published chlorophyll seasonality deduced from the relatively sparse coverage of the Coastal Zone Color Scanner (CZCS) and the more complete coverage of SeaWiFS in both Southern Hemisphere regions are evident. [ABSTRACT FROM AUTHOR]