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Quantitative Inversion and Validation of Satellite Remote Sensing Products

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 26 October 2024 | Viewed by 7940

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Special Issue Editors

Prof. Dr. Xingfa Gu

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing technology and application; information extraction and engineering; quantitative remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jian Yang

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing imagery processing and analyzing

Dr. Xiangqin Wei

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing of vegetation; remote sensing of ecological environment; agriculture remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Hailiang Gao

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: calibration and validation; sensors; satellite; remote sensing

Dr. Fernando Camacho

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Guest Editor

Earth Observation Laboratory (EOLAB), Parc Cientific University of Valencia, C/ Catedràtic Agustín Escardino, 9, 46980 Paterna, Valencia, Spain
Interests: retrieval of biophysical variables from satellite data; validation of satellite-derived global land products; fiducial reference measurements; cal/val field campaigns; climate data records of terrestrial ECVs; agriculture monitoring; climate change awareness
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing data and information products have become indispensable strategic resources with the rapid development of global remote sensing satellites and the growing demand for applications. High-quality remote sensing satellite information products are an important part of global and regional monitoring and analysis. The validation of quantitative products and the evaluation of algorithm efficiency can effectively improve the continuous optimization of remote sensing products and inversion algorithms for the overall level of the quantitative application of remote sensing. It also plays an important role in serving national economic and social development applications, such as agriculture, disasters, and ecological monitoring, supporting the implementation of major regional development strategies, and promoting global sustainable development.

At present, a large number of quantitative product developments have been carried out in the field of remote sensing science and technology, forming the inversion technologies and tools for various basic key products including geometry, radiation, land surface, vegetation, atmosphere, water body, etc., which have become the necessary support for various thematic applications such as environmental monitoring, resource investigation, crop yield estimation, disaster analysis, urban construction, etc. Meanwhile, the development of validation technology is tending toward infrastructure, forming the ground truth observation and sample generation capabilities of multiple elements, including space-time continuity, multi-scale, and sky-ground integration, which provides comprehensive support for the verification and optimization of product technology and becomes an important means to develop the basic theory and technology of remote sensing. This ensures the quality of remote sensing products and promotes the in-depth development of remote sensing applications. For example, the “National Civil Space Infrastructure Common Application Support Platform for Land Observation Satellite” (Website: http://caplos.aircas.ac.cn) has formed a large number of original data sets and technical achievements. The achievements and experiences of its construction and application can provide more data and resources for research in this field.

The Special Issue focuses on “Quantitative Inversion and Validation of Satellite Remote Sensing Products”. Potential topics include but are not limited to the following:

Design and application of a multi-source and full-spectrum remote sensing quantitative product classification system, and a comparative analysis of typical satellite product systems;
Key technologies of processing, analyzing, and application of massive multi-source remote sensing quantitative products;
Layout planning, collaborative observation, and application service system design of large-scale validation test site network;
Geometric remote sensing quantitative product algorithm and validation technology;
Radiation remote sensing quantitative product algorithm and validation technology;
Land surface remote sensing quantitative product algorithm and validation technology;
Vegetation remote sensing quantitative product algorithm and validation technology;
Atmospheric remote sensing quantitative product algorithm and validation technology;
Water remote sensing quantitative product algorithm and validation technology;
The algorithm and validation technology of thematic quantitative products in industry application;
The application effect and product evaluation of remote sensing quantitative products in typical industries and regions;
The technical method and application of remote sensing quantitative product validation in the application of “The Belt and Road” initiative and global sustainable development;
Key issues and potential value-added application in the construction of a global validation test site network.

Prof. Dr. Xingfa Gu
Prof. Dr. Jian Yang
Dr. Xiangqin Wei
Dr. Hailiang Gao
Dr. Fernando Camacho
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

remote sensing products
quantitative inversion
validation
space infrastructure
remote sensing applications

Published Papers (8 papers)

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Research

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20 pages, 7311 KiB

Open AccessArticle

A Novel Multi-Scale Feature Map Fusion for Oil Spill Detection of SAR Remote Sensing

by Chunshan Li, Yushuai Yang, Xiaofei Yang, Dianhui Chu and Weijia Cao

Remote Sens. 2024, 16(10), 1684; https://doi.org/10.3390/rs16101684 - 9 May 2024

Viewed by 316

Abstract

The efficient and timely identification of oil spill areas is crucial for ocean environmental protection. Synthetic aperture radar (SAR) is widely used in oil spill detection due to its all-weather monitoring capability. Meanwhile, existing deep learning-based oil spill detection methods mainly rely on the classical U-Net framework and have achieved impressive results. However, SAR images exhibit high noise, blurry boundaries, and irregular shapes of target areas, as well as speckles and shadows, which lead to the loss of performance in existing algorithms. In this paper, we propose a novel network architecture to achieve more precise segmentation of oil spill areas by reintroducing rich semantic contextual information before obtaining the final segmentation mask. Specifically, the proposed architecture can re-fuse feature maps from different levels at the decoder end. We design a multi-convolutional layer (MCL) module to extract basic feature information from SAR images, and a feature extraction module (FEM) module further extracts and fuses feature maps generated by the U-Net decoder at different levels. Through these operations, the network can learn rich global and local contextual information, enable sufficient interaction of feature information at different stages, enhance the model’s contextual awareness, and improve its ability to recognize complex textures and blurry boundaries, thereby enhancing the segmentation accuracy of SAR images. Compared to many U-Net based segmentation networks, our method shows promising results and achieves state-of-the-art performance on multiple evaluation metrics. Full article

(This article belongs to the Special Issue Quantitative Inversion and Validation of Satellite Remote Sensing Products)

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19 pages, 59071 KiB

Open AccessArticle

Road Extraction from Remote Sensing Imagery with Spatial Attention Based on Swin Transformer

by Xianhong Zhu, Xiaohui Huang, Weijia Cao, Xiaofei Yang, Yunfei Zhou and Shaokai Wang

Remote Sens. 2024, 16(7), 1183; https://doi.org/10.3390/rs16071183 - 28 Mar 2024

Viewed by 736

Abstract

Road extraction is a crucial aspect of remote sensing imagery processing that plays a significant role in various remote sensing applications, including automatic driving, urban planning, and path navigation. However, accurate road extraction is a challenging task due to factors such as high road density, building occlusion, and complex traffic environments. In this study, a Spatial Attention Swin Transformer (SASwin Transformer) architecture is proposed to create a robust encoder capable of extracting roads from remote sensing imagery. In this architecture, we have developed a spatial self-attention (SSA) module that captures efficient and rich spatial information through spatial self-attention to reconstruct the feature map. Following this, the module performs residual connections with the input, which helps reduce interference from unrelated regions. Additionally, we designed a Spatial MLP (SMLP) module to aggregate spatial feature information from multiple branches while simultaneously reducing computational complexity. Two public road datasets, the Massachusetts dataset and the DeepGlobe dataset, were used for extensive experiments. The results show that our proposed model has an improved overall performance compared to several state-of-the-art algorithms. In particular, on the two datasets, our model outperforms D-LinkNet with an increase in Intersection over Union (IoU) metrics of 1.88% and 1.84%, respectively. Full article

(This article belongs to the Special Issue Quantitative Inversion and Validation of Satellite Remote Sensing Products)

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22 pages, 11428 KiB

Open AccessArticle

Evaluation of GSMaP Version 8 Precipitation Products on an Hourly Timescale over Mainland China

by Xiaoyu Lv, Hao Guo, Yunfei Tian, Xiangchen Meng, Anming Bao and Philippe De Maeyer

Remote Sens. 2024, 16(1), 210; https://doi.org/10.3390/rs16010210 - 4 Jan 2024

Cited by 1 | Viewed by 1162

Abstract

A thorough evaluation of the recently released Global Satellite Mapping of Precipitation (GSMaP) is critical for both end-users and algorithm developers. In this study, six products from three versions of GSMaP version 8, including real time (NOW-R and NOW-C), near real time (NRT-R and NRT-C), and post-real time (MVK-R and MVK-C), are systematically and quantitatively evaluated based on time-by-time observations from 2167 stations in mainland China. Among each version, both products with and without gauge correction are adopted to detect the gauge correction effect. Error quantification is carried out on an hourly timescale. Three common statistical indices (i.e., correlation coefficient (CC), relative bias (RB), and root mean square error (RMSE)) and three event detection capability indices (i.e., probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI)) were adopted to analyze the inversion errors in precipitation amount and precipitation event frequency across the various products. Additionally, in this study, we examine the dependence of GSMaP errors on rainfall intensity and elevation. The following main results can be concluded: (1) MVK-C exhibits the best ability to retrieve rainfall on the hourly timescale, with higher CC values (0.31 in XJ to 0.47 in SC), smaller RMSE values (0.14 mm/h in XJ to 0.99 mm/h in SC), and lower RB values (−4.78% in XJ to 16.03% in NC). (2) Among these three versions, the gauge correction procedure plays a crucial role in reducing errors, especially in the post-real-time version. After being corrected, MVK-C demonstrates an obvious CC value improvement (>0.3 on the hourly timescale) in various sub-regions, increasing the percentage of sites with CC values above 0.5 from 0.03% (MVK-R) to 28.47% (MVK-C). (3) GSMaP products generally exhibit error dependencies on precipitation intensity and elevation, particularly in areas with drastic elevation changes (such as 1200–1500 m and 3000–3300 m), where the accuracy of satellite precipitation estimates is significantly affected. (4) CC values decreased with an increasing rainfall intensity; RB and RMSE values increased with an increasing rainfall intensity. The results of this study may be helpful for algorithm developers and end-users and provide a scientific reference for different hydrological applications and disaster risk reduction. Full article

(This article belongs to the Special Issue Quantitative Inversion and Validation of Satellite Remote Sensing Products)

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25 pages, 37764 KiB

Open AccessArticle

GF-1 WFV Surface Reflectance Quality Evaluation in Countries along “the Belt and Road”

by Yaozong Ding, Xingfa Gu, Yan Liu, Hu Zhang, Tianhai Cheng, Juan Li, Xiangqin Wei, Min Gao, Man Liang and Qian Zhang

Remote Sens. 2023, 15(22), 5382; https://doi.org/10.3390/rs15225382 - 16 Nov 2023

Viewed by 834

Abstract

The GaoFen-1 wide field of view (GF-1 WFV) has produced level 1 digital number data globally; however, most applications have focused on China, and data quality outside China has not been validated. This study presents a preliminary assessment of the 2020 GF-1 WFV surface reflectance data for Nepal, Azerbaijan, Kenya, and Sri Lanka along “the Belt and Road” route using Sentinel-2 Multi-Spectral Instrument (MSI), Landsat-8 Operational Land Image (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A method for obtaining the GF-1 WFV surface reflectance data was also proposed, with steps including atmospheric correction, cross-radiation calibration, and bidirectional reflectance distribution function correction. The results showed that WFV surface reflectance data was not significantly different from MSI, OLI, and MODIS surface reflectance data. In the visible and near-infrared bands, for most landcover types, the bias was less than 0.02, and the precision and root mean square error were less than 0.04. When the landcover types were forest and water, the MSI, OLI, and MODIS surface reflectance data were higher than that of WFV in the near-infrared band. The results of this study provide a basis for assessing the global application potential of GF-1 WFV. Full article

(This article belongs to the Special Issue Quantitative Inversion and Validation of Satellite Remote Sensing Products)

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18 pages, 6613 KiB

Open AccessArticle

The Influence of Validation Colocation on X_CO₂ Satellite–Terrestrial Joint Observations

by Ruoxi Li, Xiang Zhou, Tianhai Cheng, Zui Tao, Xingfa Gu, Ning Wang, Hongming Zhang and Tingting Lv

Remote Sens. 2023, 15(22), 5270; https://doi.org/10.3390/rs15225270 - 7 Nov 2023

Viewed by 737

Abstract

Comparing satellite retrieval with high-precision ground observations is an essential component for the validation of CO₂ satellite products. The initial stage of assessing the bias in retrieval products from satellite and ground sources involves establishing a geographical connection between observations that are temporally and spatially proximate. The primary aim of this paper is to evaluate the influence of variations in neighborhood definitions and colocation methods on the assessment of satellite products and provide quantitative references. To achieve this, a series of experiments were conducted involving the Global Total Column Carbon Observation Network (TCCON) and the OCO-2 satellite. Various spatial-temporal neighborhoods and colocation methods were considered in these experiments. The results indicate that spatial neighborhoods exert a more substantial influence on bias compared to temporal neighborhoods. In the mid-latitudes of the Northern Hemisphere, there is an observed linear increase trend between the difference of OCO-2 and TCCON observations and the spatial neighborhood, with an average increase of 0.32 ppm as the neighborhood size changes from 1

°

to 10

°

. Regarding colocation methods, the simple spatiotemporal geographic constraints tend to overlook changes in the atmospheric state to a certain extent. The target geographic constraint method reduces the bias by 2% to 5% by increasing the proportion of OCO-2 observations targeting TCCON while the method of introducing T₇₀₀ potential temperature reduces by 2% to 13% by screening the gradient of CO₂ concentration change. Moreover, an evident correlation exists between the bias and their corresponding latitudes, with a 0.20 ppm increase in bias observed for every 10

°

increment in latitudes in the Northern Hemisphere. The bias of TCCON and OCO-2 shows a pronounced seasonal regularity, with the highest in summer. The study also discusses the selection of spatiotemporal matching with low satellite coverage, the bias distribution, and the attribution of bias to the natural wind field. Full article

(This article belongs to the Special Issue Quantitative Inversion and Validation of Satellite Remote Sensing Products)

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21 pages, 6807 KiB

Open AccessArticle

Study on Surface Reflectance Sampling Method and Uncertainty Based on Airborne Hyperspectral Images

by Hailiang Gao, Qianqian Wang, Xingfa Gu, Jian Yang, Qiyue Liu, Zui Tao, Xingchen Qiu, Wei Zhang, Xinda Shi and Xiaofei Zhao

Remote Sens. 2023, 15(21), 5090; https://doi.org/10.3390/rs15215090 - 24 Oct 2023

Viewed by 1191

Abstract

The validation of satellite remote sensing surface reflectance products is aimed at comparing the reflectance pixel values of products with ground measurement values at the pixel scale. Due to the existence of surface heterogeneity, we cannot obtain the satellite pixel scale truth value through ground sampling, and only the satellite relative pixel scale truth value that closely approximates it can be acquired. The process of converting the point-scale spectrum of ground sampling into a pixel-scale spectrum will produce certain errors, known as point-to-pixel-scale conversion uncertainty, which is closely related to the type of sample area and the ground sampling method. In this study, we conducted research on the uncertainty of point-to-pixel-scale conversion generated via different ground sampling methods in the upscaling process. We utilized unmanned aerial vehicle (UAV) hyperspectral images to invert the surface reflectance spectral curves of wheat, corn, bare soil, and soybeans at the pixel scale, and simulate the ground measurement spectra and satellite pixel scale ground truth of different sampling methods, so as to realize the quantitative calculation of the uncertainty of the ground truth at the satellite pixel scale. On this basis, we analyzed in depth the effects of the sampling method, measurement height, and number of spectra on the scale conversion uncertainty. The research results show that airborne hyperspectral images can accurately simulate the spectra of ground measurements, and can be used as an effective means of ground spectral sampling and uncertainty analysis. When using the systematic sampling method, the more the sampling points, the smaller the uncertainty. However, the uncertainty of scale conversion tends to stabilize when the number of sampling points is increased to a certain quantity. As the height of ground measurement increases, the number of spectra within the elementary sampling unit (ESU) increases, leading to smaller scale conversion uncertainties. The research results of this study will provide support for the subsequent optimization of ground sampling methods and the improvement of measurement efficiency and measurement accuracy. Full article

(This article belongs to the Special Issue Quantitative Inversion and Validation of Satellite Remote Sensing Products)

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19 pages, 19768 KiB

Open AccessArticle

Preliminary Evaluation of Geometric Positioning Accuracy of C-SAR Images Based on Automatic Corner Reflectors

by Yanan Jiao, Fengli Zhang, Xiaochen Liu, Qi Wang, Qiqi Huang and Zhiwei Huang

Remote Sens. 2023, 15(19), 4744; https://doi.org/10.3390/rs15194744 - 28 Sep 2023

Viewed by 864

Abstract

C-SAR/01 and C-SAR/02 serve as successors to the GF-3 satellite. They are designed to operate in tandem with GF-3, collectively forming a C-band synthetic aperture radar (SAR) satellite constellation. This constellation aims to achieve 1 m resolution imaging with a revisit rate of one day. It can effectively cater to various applications such as marine disaster prevention, monitoring marine dynamic environments, and supporting marine scientific research, disaster mitigation, environmental protection, and agriculture. Geometric correction plays a pivotal role in acquiring highly precise geographic location data for ground targets. The geometric positioning accuracy without control points signifies the SAR satellite’s geometric performance. However, SAR images do not exhibit a straightforward image-point–object-point correspondence, unlike optical images. In this study, we introduce a novel approach employing high-precision automatic trihedral corner reflectors as ground control points (GCPs) to assess the geometric positioning accuracy of SAR images. A series of satellite-ground synchronization experiments was conducted at the Xilinhot SAR satellite calibration and validation site to evaluate the geometric positioning accuracy of different C-SAR image modes. Firstly, we calculated the azimuth and elevation angles of the corner reflectors based on satellite orbit parameters. During satellite transit, these corner reflectors were automatically adjusted to align with the radar-looking direction. We subsequently measured the exact longitude and latitude coordinates of the corner reflector vertex in situ using a high-precision real-time kinematics instrument. Next, we computed the theoretical image coordinates of the corner reflectors using the rational polynomial coefficients (RPC) model. After that, we determined the accurate position of the corner reflector in the Single Look Complex (SLC) SAR image using FFT interpolation and the sliding window method. Finally, we evaluated and validated the geometric positioning accuracy of C-SAR images by comparing the two coordinates. The preliminary results indicate that the positioning accuracy varies based on the satellite, imaging modes, and orbital directions. Nevertheless, for most sample points, the range positioning accuracy was better than 60 m, and the azimuth positioning accuracy was better than 80 m. These findings can serve as a valuable reference for subsequent applications of C-SAR satellites. Full article

(This article belongs to the Special Issue Quantitative Inversion and Validation of Satellite Remote Sensing Products)

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15 pages, 21793 KiB

Open AccessTechnical Note

High-Resolution PM_2.5 Concentrations Estimation Based on Stacked Ensemble Learning Model Using Multi-Source Satellite TOA Data

by Qiming Fu, Hong Guo, Xingfa Gu, Juan Li, Wenhao Zhang, Xiaofei Mi, Qichao Zhao and Debao Chen

Remote Sens. 2023, 15(23), 5489; https://doi.org/10.3390/rs15235489 - 24 Nov 2023

Viewed by 951

Abstract

Nepal has experienced severe fine particulate matter (PM_2.5) pollution in recent years. However, few studies have focused on the distribution of PM_2.5 and its variations in Nepal. Although many researchers have developed PM_2.5 estimation models, these models have mainly focused on the kilometer scale, which cannot provide accurate spatial distribution of PM_2.5 pollution. Based on Gaofen-1/6 and Landsat-8/9 satellite data, we developed a stacked ensemble learning model (named XGBLL) combined with meteorological data, ground PM_2.5 concentrations, ground elevation, and population data. The model includes two layers: a XGBoost and Light GBM model in the first layer, and a linear regression model in the second layer. The accuracy of XGBLL model is better than that of a single model, and the fusion of multi-source satellite remote sensing data effectively improves the spatial coverage of PM_2.5 concentrations. Besides, the spatial distribution of the daily mean PM_2.5 concentrations in the Kathmandu region under different air conditions was analyzed. The validation results showed that the monthly averaged dataset was accurate (R² = 0.80 and root mean square error = 7.07). In addition, compared to previous satellite PM_2.5 datasets in Nepal, the dataset produced in this study achieved superior accuracy and spatial resolution. Full article

(This article belongs to the Special Issue Quantitative Inversion and Validation of Satellite Remote Sensing Products)

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