Short Revisit Time Research Articles

Assessment of Atmospheric Correction Algorithms for Correcting Sunglint Effects in Sentinel-2 MSI Imagery: A Case Study in Clean Lakes

The Sentinel-2 Multi-Spectral Instrument (MSI) is characterized by short revisit times (5 days), red-edge spectral bands (665 nm and 705 nm), and a high spatial resolution (10 m), making it highly suitable for monitoring water quality in both inland and coastal waters. Unlike SeaWiFS, which can adjust its viewing angles to minimize sunglint, the Sentinel-2 MSI operates with fixed near-nadir angles, which makes it more susceptible to sunglint. Additionally, the complex optical properties of water pose challenges in accurately determining its water-leaving reflectance. Therefore, we compared the effectiveness of six atmospheric correction (AC) algorithms (POLYMER, MUMM, DSF, C2RCC, BP, and GRS) in correcting sunglint using two typical lakes in Xinjiang, China, as examples. The results indicated that POLYMER achieved the highest overall evaluation score (1.61), followed by MUMM (1.21), while BP exhibited the lowest performance (0.62). Specifically, POLYMER showed robust performance at the 665 nm band with RMSE = 0.0012 sr−1, R2 = 0.74, and MAPE = 30.68%, as well as at the 705 nm band with RMSE = 0.0014 sr−1, R2 = 0.42, and MAPE = 38.44%. At the 443, 490, and 560 nm bands, MUMM showed better performance (RMSE ≤ 0.0026 sr−1, R2 ≥ 0.86, MAPE ≤ 28.20%). In terms of band ratios, POLYMER exhibited the highest accuracy (RMSE ≤ 0.093 and MAPE ≤ 22.2%), particularly for the ratio Rrs(490)/Rrs(560) (R2 = 0.71). In general, POLYMER is the best choice for the sunglint correction of Xinjiang’s clean lakes. This study assessed the capability of different AC algorithms for sunglint correction and enhanced the monitoring capability of MSI data in clean waters.

Decorrelation rate and daily cycle in sub-daily time series of SAR coherence amplitude

A dataset of sub-daily C-band data, acquired with a ground-based synthetic aperture radar, has been used to study soil and vegetation dynamics during a complete growing season in a controlled agricultural test site. The data have been exploited to analyse the rate and sources of decorrelation in the scene, as well as the consequences of the observation conditions of a sub-daily satellite (with either low, medium or geosynchronous orbit): short revisit times, availability of multiple acquisitions during a single day, and shallow observations at some incidence angles. Repeat-pass coherence is found to be less affected by temporal decorrelation when the primary image is acquired during nighttime or the last hours predawn. Regarding the incidence angle, VV has increased sensitivity to certain phenological stages as the incidence angle increases. Additionally, a periodic oscillation on a sub-daily scale is observed when creating coherence time series with increasing temporal baseline. Factors which strongly contribute to these oscillations are the daily cycles of temperature, soil moisture and vegetation water dynamics.

The time-series GF-1 WFV data monitoring of sugarcane using a Random Forest Algorithm in South China

Abstract. There is large distribution of sugarcane growth in south China which is play an important role of sugar industry. Remote sensing technology is used in sugarcane monitoring for large areas. However, the optical satellite data coverage is influenced by the rainy weather especially in the grand growth period of sugarcane. GF-1 WFV has widely swath 800km and short revisit time which is ideal data for this study area. In this paper, the random forest model was chosen to get a precise classification result of sugarcane based on time-series band value and 5 spectral indexes image is 89.73% and the Kappa coefficient is 0.65 which is satisfied the overall extraction of sugarcane for large area is the southern China. Furthermore, the decision tree classification was chosen as a comparative experience research.

Mapping burned areas in Thailand using Sentinel-2 imagery and OBIA techniques

Monitoring burned areas in Thailand and other tropical countries during the post-harvest season is becoming increasingly important. High-resolution remote sensing data from Sentinel-2 satellites, which have a short revisit time, is ideal for accurately and efficiently mapping burned regions. However, automating the mapping of agriculture residual on a national scale is challenging due to the volume of information and level of detail involved. In this study, a Sentinel-2A Level-1C Multispectral Instrument image (MSI) from February 27, 2018 was combined with object-based image analysis (OBIA) algorithms to identify burned areas in Mae Chaem, Chom Thong, Hod, Mae Sariang, and Mae La Noi Districts in Chiang Mai, Thailand. OBIA techniques were used to classify forest, agricultural, water bodies, newly burned, and old burned regions. The segmentation scale parameter value of 50 was obtained using only the original Sentinel-2A band in red, green, blue, near infrared (NIR), and Normalized Difference Vegetation Index (NDVI). The accuracy of the produced maps was assessed using an existing burned area dataset, and the burned area identified through OBIA was found to be 85.2% accurate compared to 500 random burned points from the dataset. These results suggest that the combination of OBIA and Sentinel-2A with a 10 m spatial resolution is very effective and promising for the process of burned area mapping.

An On-Site InSAR Terrain Imaging Method with Unmanned Aerial Vehicles.

An on-site InSAR imaging method carried out with unmanned aerial vehicles (UAVs) is proposed to monitor terrain changes with high spatial resolution, short revisit time, and high flexibility. To survey and explore a specific area of interest in real time, a combination of a least-square phase unwrapping technique and a mean filter for removing speckles is effective in reconstructing the terrain profile. The proposed method is validated by simulations on three scenarios scaled down from the high-resolution digital elevation models of the US geological survey (USGS) 3D elevation program (3DEP) datasets. The efficacy of the proposed method and the efficiency in CPU time are validated by comparing with several state-of-the-art techniques.

A direct insertion technique to assimilate sea surface height into a storm surge model

Natural hazards like hurricanes, cyclones, and typhoons frequently cause major economic and livelihood loss in coastal areas around the world. However, accurately predicting sea surface height and flood inundation extent at the operational level remains a challenge. This work tests the hypothesis that assimilating satellite-like sea surface observations within a storm surge model can improve the estimation of storm surge dynamics for operational forecasting. A synthetic experiment is proposed based on Hurricane Irene, which moved along the United States East Coast in 2011. With a focus on the Chesapeake Bay area, a framework is developed to simulate coastal sea surface height using a storm surge model and merging synthetic satellite observations. To represent the “truth” of the system, a hindcast simulation was run for the Hurricane Irene event. Different satellite revisit times are considered to assess the improvement over the numerical model open-loop (i.e., no data assimilation) simulation. Results indicate that the assimilation of synthetic sea surface height observations has the potential to reduce errors and improve the accuracy in the hydrodynamic model simulations by up to 25% (in terms of correlation coefficient), especially when the satellite has short revisit times (i.e., daily, half-daily and flood tides). Such improvements are consistent regardless of the bias sign in the satellite observations and the location, which is fundamental in an operational setting where errors in satellite observations are often unknown.

Italian COSMO-SkyMed atlas: R-Index and the percentage of measurability of movement

ABSTRACT SAR (Synthetic Aperture Radar) satellite interferometry is a helpful remote sensing technique for large areas analyses and monitoring, especially where the study area is difficult to access for practical or for legal reasons. As a result, the use of these techniques has significantly increased over the past three decades. Among the available different satellite constellations displaying different spatial and temporal resolutions, COSMO-SkyMed of the Italian Space Agency (ASI) represents a cutting-edge reality. COSMO-SkyMed constellation, launched in 2007 by ASI, is a valuable Earth observation tool that provides all-weather, day-and-night imaging capabilities with high resolution and a short revisit time. In this study, we produced an atlas for the entire Italian peninsula using two parameters (R-Index and Percentage of measurability of movement), in order to evaluate the quality and a-priori applicability of satellite interferometry data collected by the COSMO-SkyMed constellation. The atlas was obtained by means of the implementation of different model builders in the GIS (Geographical Information Systems) environment, providing a semi-automatic way to generate the above-mentioned outputs. The R-Index describes the likelihood of detecting Permanent Scatterers in mountainous areas, while the Percentage of measurability of movement indicates the percentage of real motion that interferometry can detect at a certain point in the analyzed region. A high-detail Digital Terrain Model (DTM) has been used to identify the most suitable areas for satellite interferometry monitoring and studying. The results of our analysis showed that the R-Index and the Percentage of measurability of movement could be used to pre-evaluate the quality of satellite interferometry data collected by the COSMO-SkyMed constellation. This research has important implications for disaster response, environmental monitoring, and scientific research and is one of a few cases in the world in which a unified representation for an entire country is provided.

Water Area Extraction and Water Level Prediction of Dongting Lake Based on Sentinel-1 Dual-Polarization Data Decomposition

The Sentinel-1 imaging radar mission provides a short revisit-time, continuous all-weather, and day-and-night imagery at the C-band, which brings opportunities for the dynamic extraction of lake water areas. For wetland-type lakes, it is difficult to distinguish between the water, submerged plants, and mudflats at the edge of a lake, which leads to difficulty in recognizing the water edge of a lake and affects the accuracy of lake water area extraction. In this paper, a water area extraction and water level prediction algorithm based on Sentinel-1 dual-polarization data decomposition is proposed to solve the problem. We can accurately extract lake water through generalized Stokes polarization decomposition. At the same time, we can verify the accuracy of water area extraction by establishing the water area and in situ water level elevation (A–E) relationship, and predicting the water level according to the calculated water area. In this study, dual-polarization Sentinel-1 time series SAR data covering the Dongting Lake wetland from 2018 to 2022 are used to verify the proposed water area extraction algorithm and establish the A–E relationship of the East Dongting Lake basin. The results show that the generalized Stokes decomposition parameters are very sensitive to the water boundary, and the R2 of the water area and the water level can reach 0.98 by using the piecewise linear function. It confirms the accuracy of the water area inversion, which is of extremely important significance for the high-precision monitoring of the water area of Dongting Lake with long-term Sentinel-1 data. Meanwhile, the predicted lake water level acquired using the A–E relationship established in this paper is compared with the field water level, with an RMSE of 0.4857 m and R2 of 0.9930. This means that the water level derived using the method in this study is quite compatible with the field observations, which provides a good idea for the water level monitoring of lakes lacking hydrological monitoring stations.

UCTNet with Dual-Flow Architecture: Snow Coverage Mapping with Sentinel-2 Satellite Imagery

Satellite remote sensing (RS) has been drawing considerable research interest in land-cover classification due to its low price, short revisit time, and large coverage. However, clouds pose a significant challenge, occluding the objects on satellite RS images. In addition, snow coverage mapping plays a vital role in studying hydrology and climatology and investigating crop disease overwintering for smart agriculture. Distinguishing snow from clouds is challenging since they share similar color and reflection characteristics. Conventional approaches with manual thresholding and machine learning algorithms (e.g., SVM and Random Forest) could not fully extract useful information, while current deep-learning methods, e.g., CNNs or Transformer models, still have limitations in fully exploiting abundant spatial/spectral information of RS images. Therefore, this work aims to develop an efficient snow and cloud classification algorithm using satellite multispectral RS images. In particular, we propose an innovative algorithm entitled UCTNet by adopting a dual-flow structure to integrate information extracted via Transformer and CNN branches. Particularly, CNN and Transformer integration Module (CTIM) is designed to maximally integrate the information extracted via two branches. Meanwhile, Final Information Fusion Module and Auxiliary Information Fusion Head are designed for better performance. The four-band satellite multispectral RS dataset for snow coverage mapping is adopted for performance evaluation. Compared with previous methods (e.g., U-Net, Swin, and CSDNet), the experimental results show that the proposed UCTNet achieves the best performance in terms of accuracy (95.72%) and mean IoU score (91.21%) while with the smallest model size (3.93 M). The confirmed efficiency of UCTNet shows great potential for dual-flow architecture on snow and cloud classification.

Compensation of Background Ionospheric Effect on L-Band Geosynchronous SAR with Fully Polarimetric Data

The L-band geosynchronous synthetic aperture radar (GEO-SAR) has been widely praised for its advantages of short revisit time, wide coverage and stable backscattering information acquisition. However, due to the ultra-long integrated time, the echo will be affected by the time-variant background ionosphere, leading in particular to defocusing in the azimuth direction. Existing compensation methods suitable for low Earth orbit SAR (LEO-SAR) are based on the SAR image or the semi-focused image at the ionospheric phase screen, assuming that the ionosphere is time-frozen for a short integrated period; thus, accurate reconstruction of the time-variant characteristics for the ionosphere in GEO-SAR cannot be achieved. In this paper, a compensation method of background ionospheric effects on L-band GEO-SAR with fully polarimetric data is proposed. Considering the continuous variation of the ionosphere within the synthetic aperture, a decompression processing is proposed to reconstruct the echo by recovering the temporal sampling according to the imaging geometry. By virtue of the Faraday rotation angle, the time-variant total electron content (TEC) is accurately estimated with the reconstructed echo. Based on the established error model, the ionospheric effects are well compensated with the estimated TEC. Simulations with the real SAR data from ALOS-2 and the measured time-variant TEC from USTEC validate the effectiveness and performance of the proposed method. The impacts from thermal noise and polarimetric calibration error are also quantitatively analyzed. From this, the error thresholds are given to guarantee compensation accuracy, namely 18.96 dB for SNR, −15.63 dB for crosstalk and −1.02 dB to 0.31 dB for the amplitude of the channel imbalance, and the argument of the channel imbalance is suggested to be maintained as close to zero as possible.

Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning

Land Use and Land Cover (LULC) classification using remote sensing data is a challenging problem that has evolved with the update and launch of new satellites in orbit. As new satellites are launched with higher spatial and spectral resolution and shorter revisit times, LULC classification has evolved to take advantage of these improvements. However, these advancements also bring new challenges, such as the need for more sophisticated algorithms to process the increased volume and complexity of data. In recent years, deep learning techniques, such as convolutional neural networks (CNNs), have shown promising results in this area. Training deep learning models with complex architectures require cutting-edge hardware, which can be expensive and not accessible to everyone. In this study, a simple CNN based on the LeNet architecture is proposed to perform LULC classification over Sentinel-2 images. Simple CNNs such as LeNet require less computational resources compared to more-complex architectures. A total of 11 LULC classes were used for training and validating the model, which were then used for classifying the sub-basins. The analysis showed that the proposed CNN achieved an Overall Accuracy of 96.51% with a kappa coefficient of 0.962 in the validation data, outperforming traditional machine learning methods such as Random Forest, Support Vector Machine and Artificial Neural Networks, as well as state-of-the-art complex deep learning methods such as ResNet, DenseNet and EfficientNet. Moreover, despite being trained in over seven million images, it took five h to train, demonstrating that our simple CNN architecture is only effective but is also efficient.

Sentinel-1 Interferometry and UAV Aerial Survey for Mapping Coseismic Ruptures: Mts. Sibillini vs. Mt. Etna Volcano

The survey and structural analysis of surface coseismic ruptures are essential tools for characterizing seismogenic structures. In this work, a procedure to survey coseismic ruptures using satellite interferometric synthetic aperture radar (InSAR) data, directing the survey using Unmanned Aerial Vehicles (UAV), is proposed together with a field validation of the results. The Sentinel-1 A/B Interferometric Wide (IW) Swath TOPSAR mode offers the possibility of acquiring images with a short revisit time. This huge amount of open data is extremely useful for geohazards monitoring, such as for earthquakes. Interferograms show the deformation field associated with earthquakes. Phase discontinuities appearing on wrapped interferograms or loss-of-coherence areas could represent small ground displacements associated with the fault’s ruptures. Low-altitude flight platforms such as UAV permit the acquisition of high resolution images and generate 3D spatial geolocalized clouds of data with centimeter-level accuracy. The generated topography maps and orthomosaic images are the direct products of this technology, allowing the possibility of analyzing geological structures from many viewpoints. We present two case studies. The first one is relative to the 2016 central Italian earthquakes, astride which the InSAR outcomes highlighted quite accurately the field displacement of extensional faults in the Mt. Vettore–M. Bove area. Here, the geological effect of the earthquake is represented by more than 35 km of ground ruptures with a complex pattern composed by subparallel and overlapping synthetic and antithetic fault splays. The second case is relative to the Mt. Etna earthquake of 26 December 2018, following which several ground ruptures were detected. The analysis of the unwrapped phase and the application of edge detector filtering and other discontinuity enhancers allowed the identification of a complex pattern of ground ruptures. In the Pennisi and Fiandaca areas different generation of ruptures can be distinguished, while previously unknown ruptures pertaining to the Acireale and Ragalna faults can be identify and analyzed.

Outer Radiation Belt Flux and Phase Space Density Response to Sheath Regions: Van Allen Probes and GPS Observations

AbstractTurbulent and compressed sheath regions preceding interplanetary coronal mass ejections strongly impact electron dynamics in the outer radiation belt. Changes in electron flux can occur on timescales of tens of minutes, which are unlikely to be captured by a two‐satellite mission. The recently released Global Positioning System (GPS) data set generally has shorter revisit times (at L ∼ 4–8) owing to the large number of satellites in the constellation equipped with energetic particle detectors. Investigating electron fluxes at energies from 140 keV to 4 MeV and sheaths observed in 2012–2018, we show that the flux response to sheaths on a timescale of 6 hr, previously reported from Van Allen Probes (RBSP) data, is reproduced by GPS measurements. Furthermore, GPS data enables derivation of the response on a timescale of 30 min, which further confirms that the energy and L‐shell dependent changes in electron flux are associated with the impact of the sheath. Sheath‐driven loss is underestimated over longer timescales as the electrons recover during the ejecta. We additionally show the response of electron phase space density (PSD), which is a key quantity in identifying non‐adiabatic loss from the system and electron energization through wave‐particle interactions. The PSD response is calculated from both RBSP and GPS data for the 6 hr timescale, as well as from GPS data for the 30 min timescale. The response is divided based on the geoeffectiveness of the sheaths revealing that electrons are effectively accelerated only during geoeffective sheaths, while loss commonly occurs during all sheaths.

A Novel Imaging-Based Target Detection and Parameter Estimation Scheme for Airborne Multichannel Circular Stripmap SAR

Airborne multichannel circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) has drawn increasing attention in wide-area surveillance, reconnaissance and traffic monitoring due to its short revisit times. In this paper, a novel scheme for CSSAR-GMTI systems is proposed to offer high-resolution focusing of moving targets and enable efficient detection and accurate parameter estimation, which are ensured by integrating moving target imaging with a space-time adaptive processing clutter suppression step. The presented scheme develops a target imaging algorithm that forms SAR images focused on a particular 2-D motion parameter to maximize target signal energy and recover its high resolution. Moreover, benefiting from the suitable phase compensation designed in the 2-D frequency domain, moving targets can finally be properly focused in the SAR image without displacement and their parameters can be uniquely determined. Experiments on an emulated radar dataset are conducted to validate the effectiveness of the proposed scheme.

An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems

Medium-Earth-orbit (MEO) synthetic aperture radar (SAR) has the advantages of short revisit time and wide coverage, and thus is a potential tool for implementing ground moving target indication (GMTI) tasks. In the paper, aiming at MEO SAR’s problems of low signal-to-noise ratio and limited computation resource, an efficient imaging method is proposed for MEO multichannel SAR-GMTI systems with relatively low resolution. The proposed imaging method is designed with the consideration of both static scenes and ground moving targets, and it can simultaneously correct the range cell migrations of static scenes and multiple moving targets of no Doppler ambiguity. It needs only four Fourier transforms and twice phase multiplications, and thus is computationally efficient. Moreover, moving targets’ signal characteristics, including the azimuth and range displacements and along-track interferometric phase, in the SAR image obtained by the proposed imaging method are figured out. Experimental results validate the proposed imaging method and the theoretical analyses.

Multi-temporal InSAR tropospheric delay modelling using Tikhonov regularization for Sentinel-1 C-band data

The increased availability of satellite SAR data coupled with improved InSAR processing algorithms has led to higher accuracy of InSAR-derived displacements. However, obtaining millimeter-level accuracy still remains a challenge due to the inability to accurately model the tropospheric delay, which is sometimes larger than the actual deformation. We propose to estimate the relative daily SAR tropospheric delay by double differencing interferograms that share a common image by leveraging Sentinel-1’s short revisit time and the interchanging role of the common image. The resulting double-differenced interferograms consist of tropospheric delay and processing noise. This combination leads to an inverse problem as a result of the differential nature of the interferograms. The unknown SAR tropopsheric delay is solved by Tikhonov regularization and the processing errors are accounted for by covariance modelling. In cases of rapid localized displacement, the deforming region is masked and the tropospheric delay for the deforming region is determined through kriging. Validation is performed using simulated SAR data and GNSS tropospheric delays. We find a good correlation between estimated SAR tropospheric delays and GNSS tropospheric delays, with an average root mean square error (RMSE) of 1.93 radians across six GNSS locations. In addition, we examine the performance of the correction on interferograms through variogram modelling, which indicates improved correction both for short-wavelength and long-wavelength tropospheric noise. SAR tropospheric delay estimates are integrated into a multitemporal InSAR workflow through either interferometric subtraction or stochastic modelling. Validation using GNSS measurements indicates that SAR tropospheric delay estimates significantly improve the accuracy of the InSAR-derived time-series displacement.

Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube

Spaceborne Synthetic Aperture Radar (SAR) are well-established systems for flood mapping, thanks to their high sensitivity towards water surfaces and their independence from daylight and cloud cover. Particularly able is the 2014-launched Copernicus Sentinel-1 C-band SAR mission, with its systematic monitoring schedule featuring global land coverage in a short revisit time and a 20 m ground resolution. Yet, variable environment conditions, low-contrasting land cover, and complex terrain pose major challenges to fully automated flood monitoring. To overcome these issues, and aiming for a robust classification, we formulate a datacube-based flood mapping algorithm that exploits the Sentinel-1 orbit repetition and a priori generated probability parameters for flood and non-flood conditions. A globally applicable flood signature is obtained from manually collected wind- and frost-free images. Through harmonic analysis of each pixel’s full time series, we derive a local seasonal non-flood signal comprising the expected backscatter values for each day-of-year. From those predefined probability distributions, we classify incoming Sentinel-1 images by simple Bayes inference, which is computationally slim and hence suitable for near-real-time operations, and also yields uncertainty values. The datacube-based masking of no-sensitivity resulting from impeding land cover and ill-posed SAR configuration enhances the classification robustness. We employed the algorithm on a 6-year Sentinel-1 datacube over Greece, where a major flood hit the region of Thessaly in 2018. In-depth analysis of model parameters and sensitivity, and the evaluation against microwave and optical reference flood maps, suggest excellent flood mapping skill, and very satisfying classification metrics with about 96% overall accuracy and only few false positives. The presented algorithm is part of the ensemble flood mapping product of the Global Flood Monitoring (GFM) component of the Copernicus Emergency Management Service (CEMS).

Cover classifications in wetlands using Sentinel-1 data (Band C): a case study in the Parana river delta, Argentina

With the launch of the Sentinel-1 mission, for the first time, multitemporal and dual-polarization C-band SAR data with a short revisit time is freely available. How can we use this data to generate accurate vegetation cover maps on a local scale? Our main objective was to assess the use of multitemporal C-Band Sentinel-1 data to generate wetland vegetation maps. We considered a portion of the Lower Delta of the Paraná River wetland (Argentina). Seventy-four images were acquired and 90 datasets were created with them, each one addressing a combination of seasons (spring, autumn, winter, summer, complete set), polarization (VV, HV, both), and texture measures (included or not). For each dataset, a Random Forest classifier was trained. Then, the kappa index values (κ) obtained by the 90 classifications made were compared. Considering the datasets formed by the intensity values, for the winter dates the achieved kappa index values (κ) were higher than 0.8, while all summer datasets achieved κ up to 0.76. Including feature textures based on the GLCM showed improvements in the classifications: for the summer datasets, the κ improvements were between 9% and 22% and for winter datasets improvements were up to 15%. Our results suggest that for the analyzed context, winter is the most informative season. Moreover, for dates associated with high biomass, the textures provide complementary information.

Remote sensing evaluation of ecological restoration engineering effect: A case study of the Yongding River Watershed, China

Ecological restoration has been risen to a national strategic project for China and become an significant way to realize ecological civilization construction. As an important way to measure the effectiveness of ecological restoration engineering, the corresponding evaluation provides a reliable basis for ecological restoration decision-making and ecosystem management. Based on remote sensing technology with the advantages of short revisit time, large monitoring range, high spatial resolution, this paper proposes a remote sensing evaluation method of watershed ecological restoration effect, so as to monitor the implementation effect of ecological restoration engineering in the watershed and technically check whether the expected goals set before the restoration are achieved. The comprehensive ecological restoration engineering of Yongding River has been implemented since 2016, and it was the first focus of the coordinated development of Beijing-Tianjin-Hebei region to make advances in ecology. In this paper, year 2015 and 2020 were selected as the before and the after the ecological restoration engineering of Yongding River. The research object was the riparian zone from Luopoling Reservoir to Daning Reservoir in Yongding River Watershed. After obtaining and processing remote sensing images, ZY-3 images were used to obtain land cover data by means of human-computer interactive interpretation, and Landsat images were used to retrieve NDVI data. Based on these data, the changes of the eight indicators of water resources, water ecology and ecological function in the study area were calculated to evaluate the ecological restoration engineering effect. The results showed that the study area has basically realized the maintenance of ecological water quantity, formed the ecological corridor of Yongding River, and effectively enhanced the ecological function and improved the water environment. By verifying the feasibility of remote sensing evaluation method, this paper could provide relevant decision makers and planners with an effective evaluation method for the ecological restoration engineering effect based on remote sensing data and technology.

Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR

Due to the short revisit time and large coverage of Geosynchronous synthetic aperture radars (GEO SARs) and the increasing number of low earth orbit synthetic aperture radar (LEO SAR) constellations, radio frequency interference (RFI) between GEO SARs and LEO SARs may occur, deteriorating the quality of SAR images. Traditional methods only simplify RFI to noise-like interference without considering the signal characteristics. In this paper, to accurately evaluate the impacts of GEO-to-LEO RFI and LEO-to-GEO RFI on imaging quantitatively, an RFI-impact quantitative analysis model is established. Taking account of the chirp signal form of SAR systems, the RFI power and image Signal-to-Interference-plus-Noise Ratio (SINR) are theoretically deduced and validated by numerical experiments. Based on the proposed method, the SAR image quality under different system parameters and bistatic configurations is estimated, and the probability of different configurations is also given. The results show that specular bistatic scattering RFI between GEO SARs and LEO SARs has serious effects on imaging, and the probability can approach 2% for certain orbital parameters and will become higher as LEO SAR constellations increase in the future, implying the necessity to suppress the RFI between the GEO SAR and the LEO SAR system.