Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications

ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation

The performance of a semantic segmentation model for remote sensing (RS) images pretrained on an annotated dataset would greatly decrease when testing on another unannotated dataset because of the domain gap. Adversarial generative methods, e.g., DualGAN, are utilized for unpaired image-to-image translation to minimize the pixel-level domain gap, which is one of the common approaches for unsupervised domain adaptation (UDA). However, the existing image translation methods are facing two problems when performing RS images translation: 1) ignoring the scale discrepancy between two RS datasets which greatly affects the accuracy performance of scale-invariant objects, 2) ignoring the characteristic of real-to-real translation of RS images which brings an unstable factor for the training of the models. In this paper, ResiDualGAN is proposed for RS images translation, where an in-network resizer module is used for addressing the scale discrepancy of RS datasets, and a residual connection is used for strengthening the stability of real-to-real images translation and improving the performance in cross-domain semantic segmentation tasks. Combined with an output space adaptation method, the proposed method greatly improves the accuracy performance on common benchmarks, which demonstrates the superiority and reliability of ResiDuanGAN. At the end of the paper, a thorough discussion is also conducted to give a reasonable explanation for the improvement of ResiDualGAN. Our source code is available at https://github.com/miemieyanga/ResiDualGAN-DRDG

Soil Moisture Retrieval Using BuFeng-1 A/B Based on Land Surface Clustering Algorithm

A new land surface clustering algorithm is developed to retrieve soil moisture (SM) using the Global Navigation Satellite System reflectometry (GNSS-R) technique. Data from the BuFeng-1 (BF-1) twin satellites A/B, a pilot mission for the Chinese GNSS-R constellation, is used for SM retrieval. The core concept of the algorithm is to cluster global land areas into different types according to the land properties and calculate the SM type by type, based on the linear relationship between equivalent specular reflectivity and SM. The global comparison between the results and SM product from the Soil Moisture Active Passive mission shows the correlation coefficient (R) is 0.82, and unbiased root mean square error (ubRMSE) is 0.070 cm3·cm-3. The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of 0.036 cm3·cm-3. This study proves that the global SM can be retrieved successfully from the BF-1 mission with the land surface clustering algorithm. By taking full advantage of the similarity of land surface physical properties in different regions, the algorithm provides a practical approach for global SM retrieval using spaceborne GNSS-R data.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 41971377). China Spacesat Company, Ltd. ESA-MOST China Dragon5 Programme (Grant Number: ID.58070) 10.13039/501100003392-Natural Science Foundation of Fujian Province (Grant Number: 2019J01853

Recent Activities on Cal/Val of the First Chinese GNSS-R Mission Bufeng-1 A/B

Ponencia expuesta online en el 2021 Dragon Symposium celebrado el 22 de julio de 2021Respect to the objectives and schedule of our project, the first-year report will include on-going activities and results of Bufeng-1 data processing, calibration workflow, and validation of the calibrated results on hurricane winds, soil moisture, and sea level measurements. The presentation has three parts. Firstly, a short introduction will be given about Bufeng-1 twin satellites that carry the Chinese first generation spaceborne GNSS-R instruments started using reflected GNSS signals to perform earth observation. Secondly, by utilizing the Bufeng-1 Normalized Bistatic Radar Cross Section (NBRCS), earth reflectivity, and range measurements, the preliminary results show that BuFeng-1 has a high agreement compared with other observations on severe sea surface winds, soil moisture, and sea level. In this presentation, the measurements of Bufeng-1 will be aligned with SFMR collected hurricanes, SMAP derived soil moisture, and DTU10 sea level models. Then, the validations of the accuracy and correlation coefficients will be analyzed to discuss the limitations and issues for the future research. For the last part, we will give the outlook about our future works of the objectives and the future plan of Bufeng missions

Early warning monitoring and management of disasters

Everyone would admit that disaster early warning is more important than later treatment and damage repair. If an effective tsunami early warning system had been in place in the Indian Ocean region on 26 December 2004, thousands of lives would have been saved. The same stark lesson can be drawn from other disasters that have killed tens of thousands of people in the past few years. Effective early warning systems not only save lives but also help protect livelihoods and assets created by national development. This paper addresses the issue of disaster early warning monitoring and management in a systemic manner and offers a general approach to a management solution. From the viewpoint of control theory, it depicts the disaster early warning monitoring and management as an information chain which has five links: disaster model bank link, disaster monitoring network link, disaster transmission channel, disaster analysis and management link and decision making and commanding link. The five links constitute an information loop, with disaster data being collected, processed through the chain and control information being fed back to the different links. With some vivid examples, this paper indicates the weakness of current links in the existing disaster early warning and management systems. On the basis of all the above analyses, the paper finally puts forward some suggestions in order to improve the performance of early warning monitoring and management of disasters.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000256657303086&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Engineering, Electrical & ElectronicGeosciences, MultidisciplinaryRemote SensingEICPCI-S(ISTP)

Estimation of Suspended Sediment Concentration in the Yangtze Main Stream Based on Sentinel-2 MSI Data

Suspended sediment concentration (SSC) is an important indicator of water quality that affects the biological processes of river ecosystems and the evolution of floodplains and river channels. The in situ SSC measurements are costly, laborious and spatially discontinuous, while the spaceborne SSC overcome these drawbacks and becomes an effective supplement for in situ observation. However, the spaceborne SSC observations of rivers are more challenging than those of lakes and reservoirs due to their narrow widths and the broad range of SSCs, among other factors. We developed a novel SSC retrieval method that is suitable for the rivers. Water was classified as clear or turbid based on the Forel–Ule index, and optimal SSC models were constructed based on the spectral responses to SSCs in cases of different turbidity. The estimated SSC had a strong correspondence with in situ measurements, with a root mean squared error (RMSE) of 24.87 mg/L and a mean relative error (MRE) of 51.91%. Satellite-derived SSC showed good consistency with SSCs obtained from gauging stations (r2 > 0.79). We studied the spatiotemporal variation in SSC in the Yangtze main stream from 2017 to 2021. It increased considerably from May to October each year, with the peak generally occurring in July or August (ca. 200–300 mg/L in a normal year and 800–1000 mg/L in a flood year), while it remained stable and decreased to around 50 mg/L from November to April of the following year. It was high in the east and low in the west, with local maxima in Chongqing (ca. 80–150 mg/L) and in the lower Dongting Lake reaches (ca. 80–100 mg/L) and a local minima in the downstream of the Three Gorges Dam (ca. 1–20 mg/L). Case studies in the Yibin reach and Three Gorges Reservoir determined that local variation in SSCs is due to special hydrodynamic conditions and anthropogenic activities. The procedure applied to process Sentinel-2 imagery and the novel SSC retrieval method we developed supplement the deficiencies in river SSC retrieval

Web3D Terrain Visualization using NURBS Method

In this paper, an approach is presented for Web3D terrain modeling, in which the NURBS method is used for surface fitting conforming to the X3D standard. On the basis of data sampling and searching control points, the model created in 3D MAX can be transformed to an X3D model. The approach can be viewed as the fundamental step of Web3D modeling and transportation.Geosciences, MultidisciplinaryRemote SensingImaging Science & Photographic TechnologyEICPCI-S(ISTP)

A combined sensor system of digital camera with LiDAR

In order to utilize the advantages of the high height accuracy of laser ranging and the good planimetric accuracy of processed digital camera imagery, the feasibility of a combined sensor system of LiDAR with digital camera using area or line array CCDs is first analyzed in this paper. The hardware composition of the combination system is given and the algorithm of integrally processing LiDAR points cloud and digital camera image is illustrated. Software development and availability for the processing at all stages of the work flow, is the key to the full utilization of such an integrated system.Engineering, Electrical & ElectronicGeosciences, MultidisciplinaryRemote SensingEICPCI-S(ISTP)

Design of network monitoring system of moving goods in the internet of things based on COMPASS

Using the monitoring system constructed with GPS to locate, track and schedule moving goods can effectively improve the utilization rate of moving goods and the level of information management. Traditional monitoring system is constructed based on the integration of GPS and information transmission system such as GSM, which can't fully control the state of moving goods for only a little information such as position, velocity can be obtained. The monitoring scope is also limited by the C/S (Client/Server) mode between the user and the monitoring center. In this paper, the related technology of the Internet of Things, the function of positioning and communication of COMPASS and the open Google Map service are analyzed. The key technology and implementation methods of hardware integration and software design of monitoring terminal, formatted information transmission and hardware configuration and software design of monitoring center are researched and described. A new B/S (Browser/Server) scheme compromising the COMPASS and related technology of the Internet of Things to monitor and schedule moving goods is put forward. It makes up insufficient aspects of traditional monitoring system and provides important reference and practical value for the application of COMPASS in related fields of the Internet of Things. ? 2011 IEEE.EI

Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha)