Accurate and timely land monitoring is crucial for addressing global environmental, economic, and societal challenges, including climate change, sustainable development, and disaster mitigation. While single-source remote sensing data offers significant capabilities, inherent limitations such as cloud cover interference (optical), speckle noise (radar), or limited spectral information (LiDAR) often hinder comprehensive and robust characterization of land surfaces. Recent advancements in synergistic harmonization technology for land monitoring, along with enhanced signal processing techniques and the integration of machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize the comprehensive applications of synergistic harmonization technology for geosciences, with a particular focus on recent advancements. Most of the existing review papers focus on the application of a single technology in a specific area, highlighting the need for a comprehensive review that integrates synergistic harmonization technology. This review provides a comprehensive review of advancements in land monitoring achieved through the synergistic harmonization of optical, radar, and LiDAR satellite technologies. It details the unique strengths and weaknesses of each sensor type, highlighting how their integration overcomes individual limitations by leveraging complementary information. This review analyzes current data harmonization and preprocessing techniques, various data fusion levels, and the transformative role of machine learning and deep learning algorithms, including emerging foundation models. Key applications across diverse domains such as land cover/land use mapping, change detection, forest monitoring, urban monitoring, agricultural monitoring, and natural hazard assessment are discussed, demonstrating enhanced accuracy and scope. Finally, this review identifies persistent challenges such as technical complexities in data integration, issues with data availability and accessibility, validation hurdles, and the need for standardization. It proposes future research directions focusing on advanced AI, novel fusion techniques, improved data infrastructure, integrated "space-air-ground" systems, and interdisciplinary collaboration to realize the full potential of multi-sensor satellite data for robust and timely land surface monitoring. Supported by deep learning, this synergy will improve our ability to monitor land surface conditions more accurately and reliably.

This article designs a high-power broadband random noise source based on a Ku-band gyrotron traveling wave tube (gyro-TWT) with a bandpass Bragg filter. The linear lossy dielectric-loaded circuit of the gyro-TWT is long enough to achieve sufficient electron pre-bunching and modulation to excite extremely broadband noise floor signals without any external input. They will be further amplified to high power in the nonlinear beam–wave interaction circuit. A bandpass Bragg filter integrated with the tube is designed for frequency selection and improving noise amplification on the sideband for better signal flatness. The gyro-TWT is driven by a gyrating electron beam with a voltage of 60 kV, a current of 20 A, and a pitch factor of 1.2. Particle-in-cell simulation shows that the noise source can achieve an output power above 28.81 kW in the frequency range of 11.55 ∼ 18.1 GHz. The excess noise ratio (ENR) is up to 80 dB with an envelope average flatness of ± 20 dB. The probability density function approximately satisfies a Gaussian distribution with a mathematical expectation of 1.5 × 10−3 and a standard deviation σ of 166. The generated high-power broadband random noise will have potential applications in noise radar, electronic countermeasures, and secure communication.

In recent years, several advantages of noise radars have positioned this technology as a promising alternative to conventional radar technology. Immunity to jamming, low mutual interference, and low probability of interception are good examples of these advantages. However, the nature of random sequences introduces several issues, such as fluctuations in the range sidelobes of the autocorrelation function causing high sidelobe levels, hence not exploitable by radar systems. This study introduces the use of multi-objective evolutionary (MOE) algorithms to design noise radar waveforms with good autocorrelation properties as well as a low peak-to-average power ratio (PAPR). A set of Pareto-optimal waveforms are produced and, most importantly, entropy is introduced as a constraint in order to maintain the transmitted signal close to a full non-deterministic waveform. Moreover, a relation between PAPR and negentropy (negative entropy) is established theoretically and validated with other authors’ simulations.

Paddy rice mapping is critical for food security and environmental management, yet existing methods face challenges such as cloud obstruction in optical data and speckle noise in synthetic aperture radar (SAR). To address these limitations, this study introduces PRICOS, a novel paddy rice index that systematically combines time series Sentinel-2 optical features (NDVI for bare soil/peak growth, MNDWI for the submerged stages) and Sentinel-1 SAR backscatter (VH polarization for structural dynamics). PRICOS automates key phenological stage detection through harmonic fitting and dynamic thresholding, requiring only 10–20 samples per region to define rice growth cycles. Validated across six agroclimatic regions, PRICOS achieved overall accuracy (OA) and F1 scores of 0.90–0.98, outperforming existing indices like SPRI (OA: 0.79–0.95) and TWDTW (OA: 0.85–0.92). By integrating multi-sensor data with minimal sample dependency, PRICOS provides a robust, adaptable solution for large-scale paddy rice mapping, advancing precision agriculture and climate change mitigation efforts.

Abstract The Surface Water and Ocean Topography (SWOT) mission offers new insights into submesoscale ocean processes. Realizing this requires careful consideration of other geophysical signals such as the signal delay induced by water vapor in the troposphere. Over short spatial scales (<∼80 km), this signal is not well‐captured by radiometer observations. Here we investigate the wet troposphere in Australian coastal regions during SWOT's 3‐month calibration phase. Using a high‐resolution atmospheric model and a novel in situ array of GNSS observations, we find the SWOT error budget for wet troposphere is regularly exceeded, with signal magnitudes up to double the error budget at small scales. We also find centimeter level biases in radiometer derived delays within ∼50 km of the coast. We suggest that, given the low radar noise and high resolution of SWOT KaRIn observations, wet troposphere errors can bias geophysical interpretation and hence have increased significance for ocean topography.

Subject and Purpose. The work presents numerical modeling results on the characteristics and parameters of a Noise Radar Sensor (NRS) during remote sensing of terrestrial surfaces. The radiometric (passive) mode and the mode with "backlighting" (active) of the mapping scene are considered. Radiometric signals of surfaces at wavelengths of 3.37 and 1.34 mm are used along with echo signals from the same surfaces under their backlighting (or illuminating) with ultra-weak-power quasicontinuous noise-like signals at a wavelength of 1.53 mm. The focus is on developing a numerical modeling technique to calculate the potential input characteristics of the NRS and compare them with the output parameters of the imagery. Methods and Methodology. The obtained output parameters and characteristics of terrestrial surface imagery are analyzed and synthesized for potential NRS embodiments. The airborne NRS carrier is an AN-14 "Bdzhilka" aircraft. Attention is given to atmospheric conditions and limited time of accumulating useful low-contrast radiometric "grass–concrete" signals. Approximate effective specific grass and concrete scattering surfaces are sought under backlighting conditions. Results. The numerical modeling results regarding the characteristics and parameters of the NRS embodiment have been optimized for two operating modes. The range, coverage sector of surfaces, imagery bands, resolution capability, number of Doppler filters at the NRS outputs, and accuracy features have been established in radiometric mode and during the backlighting of mapping surfaces. Conclusions. Numerical modeling has been conducted based on technologically feasible characteristics of the NRS. The key parameters and features of the NRS in radiometric mode and under conditions of mapping scene backlighting have been optimized. We have analyzed the NRS input characteristics in connection with the output parameters of the imagery. The obtained results will allow us to predict the quality of imagery during remote sensing.

ABSTRACTDoppler‐tolerant waveforms are some of the most common radar waveforms used in practice. However, their deterministic and repetitive nature impedes control of mutual interference when multiple radars operate in close proximity. Noise radar technology may address this problem but is not Doppler tolerant. In this study, we design a machine learning radar detector capable of Doppler‐tolerant performance with noise waveforms. The detector is implemented as a feedforward multilayer neural network. We show that the detector may be trained to operate with one‐bit data. Further, to evaluate the proposed detector's performance, we derive a closed‐form expression of the receiver operating characteristic (ROC) for one‐bit detection of a Swerling 1 target using the square‐law detector under the assumption of low signal‐to‐noise ratio (SNR). Numerical results demonstrate that the proposed machine learning detector, when suitably trained, is capable of operating with Doppler tolerance over a wide range of Doppler shifts.

The purpose of this article is a brief overview of the results of research on the use of noise-like signals in broadband radio systems conducted under the leadership of Yuri Vasilyevich Gulyaev. Methods. The conducted research was based on the previous experience of the V.A. Kotelnikov IRE RAS research team related to the development of analog noise-like devices (shumotrons) based on the concept of dynamic chaos. The continuation of these studies was associated with the development of digital chaos based on integer generating algorithms that could be easily reproduced on any digital technology. Results. Promising directions of using information technologies using dynamic chaos for the transmission, processing, storage and protection of information are considered. Broadband information transmission systems using complex signals with a large base, built on the basis of systems with chaotic dynamics, are presented. Finite-dimensional mathematical algorithms for calculating chaotic signals by reconstructing nonlinear dynamics in dissipative systems with a delay are proposed. Conclusion. It is shown that a digital information transmission system with spectrum expansion and dynamic change of chaotic codes has high noise immunity, secrecy, electromagnetic compatibility and ensures reliable and confidential transmission of messages in a complex electromagnetic environment. Schemes for masking, protecting, processing, and transmitting information are implemented based on original chaotic algorithms. An experimental study of the noise radar layout in laboratory conditions demonstrated a sufficiently high accuracy of radar range measurements over the entire measurement range with dual spectral signal processing, as well as a high range resolution of 15 cm (with an effective bandwidth of 800- 900 MHz).

Millimeter-wave automotive radar has become an essential tool for autonomous driving, providing reliable sensing capabilities under various environmental conditions. To reduce hardware size and cost, sparse arrays are widely employed in automotive radar systems. Additionally, because the targets detected by automotive radar typically exhibit sparsity, compressed sensing-based algorithms have been utilized for sparse array reconstruction, achieving superior performance. However, traditional compressed sensing algorithms generally assume that targets are located on a finite set of grid points and perform sparse reconstruction based on predefined grids. When targets are off-grid, significant off-grid errors can occur. To address this issue, we propose an automotive radar sparse reconstruction algorithm based on accelerated Atomic Norm Minimization (ANM). By using the Iterative Vandermonde Decomposition and Shrinkage Threshold (IVDST) algorithm, we can achieve fast ANM, which effectively mitigates off-grid errors while reducing reconstruction complexity. Furthermore, we adopt a Generalized Likelihood Ratio Test (GLRT) detector to eliminate noise and clutter in the automotive radar operating environment. Simulation results show that our proposed algorithm significantly improves reconstruction accuracy compared to the iterative soft threshold (IST) algorithm while maintaining the same computational complexity. The effectiveness of the proposed algorithm in practical applications is further validated through real-world data experiments, demonstrating its superior capability in clutter elimination.

ABSTRACT A quadrilateral filtering algorithm is proposed in this letter to reduce noise in video synthetic aperture radar (video SAR). The novel filtering approach draws inspiration from the traditional bilateral filter and effectively exploits the similarities in both greyscale and geometric levels. Moreover, the proposed quadrilateral filter incorporates the similarity of time domain and rank-ordered absolute difference (ROAD) statistic for detecting the abundant speckle and impulse noise in video SAR frames. An impulse detector, which is related to the distribution of ROAD, is added in the filtering process to remove the outliers in the intensity domain after time redundancy processing with the target frame image. The proposed quadrilateral filter can obviously smooth various sources of noise, including strong speckle noise and impulse noise, while considering the details of each image frame of the video SAR. The new algorithm has been verified to achieve superior filtering performance with equivalent number of looks (ENL) values increased by 25% on average comparing with other widely used algorithms.

An inland waterway traffic complexity evaluation method using radar sequential images

Removing atmospheric noise from InSAR interferograms in mountainous regions with a convolutional neural network

Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing efficiency. This paper constructed a composite radar waveform based on noise frequency modulation (NFM) and linear frequency modulation (LFM) signals, enhancing the signal's complexity and anti-jamming capability. Furthermore, a method for optimizing the processing of echo signals based on de-chirp and delay matching is proposed. The locally generated LFM signal is used to de-chirp the received echoes, resulting in a narrowband difference frequency noise signal. Subsequently, delay matching is performed in the fast time domain using the locally generated NFM signal according to the number of sampling points in the traversal processing period, allowing for the acquisition of target delay information. While reducing the analog-to-digital (A/D) sampling rate, the detection performance for wideband echo signals under high sampling rates is still maintained, with sidelobe levels and range resolution preserved. Accumulating this information in the slow time domain enables accurate target detection. The effectiveness of the proposed method is validated through simulation experiments.

This work, written for engineers or managers with no special knowledge of quantum mechanics, nor deep experience in radar, aims to help the scientific, industrial, and governmental community to better understand the basic limitations of proposed microwave quantum radar (QR) technologies and systems. Detection and ranging capabilities for QR are critically discussed and a comparison with its closest classical radar (CR), i.e., the noise radar (NR), is presented. In particular, it is investigated whether a future fielded and operating QR system might really outperform an “equivalent” classical radar, or not. The main result of this work, coherently with the recent literature, is that the maximum range of a QR for typical aircraft targets is intrinsically limited to less than one km, and in most cases to some tens of meters. Detailed computations show that the detection performance of all the proposed QR types are orders of magnitude below the ones of any much simpler and cheaper equivalent “classical” radar set, in particular of the noise radar type. These limitations do not apply to very-short-range microwave applications, such as microwave tomography and radar monitoring of heart and breathing activity of people (where other figures, such as cost, size, weight, and power, shall be taken into account). Moreover, quantum sensing at much higher frequencies (optical and beyond) is not considered here.

Guest Editorial: Advancements and future trends in noise radar technology

Abstract Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)‐based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI‐based algorithms for NRT and provide results for its use.

Noise radar waveform, as an important waveform in the field of low-probability-of-intercept radars, has been widely studied. However, the general noise waveform has a high peak average power ratio (PAPR), which leads to a reduction of signal-to-noise ratio. Thus, it is necessary to control the noise waveform PAPR to a suitable range. In this paper, the PAPR value reduction problem of low sidelobe noise waveform is studied. First, the proposed new algorithm is given a general overview. Then, the Combined Spectral Shaping and Peak-to-Average Power Ratio Reduction (COSPAR) algorithm is adjusted according to the alternate projection method and phase retrieval algorithm, and an improved COSPAR (ICOSPAR) algorithm with a faster execution speed and higher waveform generation efficiency is derived. The specific method performs waveform projection between the spectral constraint domain and the PAPR constraint domain and then evaluates the computational load of the ICOSPAR algorithm. Simulation results show that the ICOSPAR algorithm is reliable and efficient in generating low PAPRs and low sidelobe noise waveforms.

Abstract Robustness against Electronic Warfare/Electronic Defence attacks represents an important advantage of Noise Radar Technology (NRT). An evaluation of the related Low Probability of Detection (LPD) and of Intercept (LPI) is presented for Continuous Emission Noise Radar (CE‐NR) waveforms with different operational parameters, that is, “tailored”, and with various “degrees of randomness”. In this frame, three different noise radar waveforms, a phase Noise (APCN) and two “tailored” noise waveforms (FMeth and COSPAR), are compared by time–frequency analysis. Using a correlator (i.e. a two antennas) receiver, assuming a complete knowledge of the band (B) and duration (T) of the coherent emission of these waveforms, it will be shown that the LPD features of a CE‐NR do not significantly differ from those of any CE radar transmitting deterministic waveforms. However, in real operations, B and T are unknown; hence, assuming an instantaneous bandwidth estimation will show that the duration T can be estimated only for some specific “tailored” waveforms (of course, not to be operationally used). The effect of “tailoring” is analysed with prospects for future work. Finally, some limitations in the classification of these radar signals are analysed.

The prevalence of Low Probability of Interception (LPI) and Low Probability of Exploitation (LPE) radars in contemporary Electronic Warfare (EW) presents an ongoing challenge to defense mechanisms, compelling constant advances in protective strategies. Noise radars are examples of LPI and LPE systems that gained substantial prominence in the past decade despite exhibiting a common drawback of limited Doppler tolerance. The Advanced Pulse Compression Noise (APCN) waveform is a stochastic radar signal proposed to amalgamate the LPI and LPE attributes of a random waveform with the Doppler tolerance feature inherent to a linear frequency modulation. In the present work, we derive closed-form expressions describing the APCN signal's ambiguity function and spectral containment that allow for a proper analysis of its detection performance and ability to remove range ambiguities as a function of its stochastic parameters. This paper also presents a more detailed address of the LPI/LPE characteristic of APCN signals claimed in previous works. We show that sophisticated Electronic Intelligence (ELINT) systems that employ Time Frequency Analysis (TFA) and image processing methods may intercept APCN and estimate important parameters of APCN waveforms, such as bandwidth, operating frequency, time duration, and pulse repetition interval. We also present a method designed to intercept and exploit the unique characteristics of the APCN waveform. Its performance is evaluated based on the probability of such an ELINT system detecting an APCN radar signal as a function of the Signal-to-Noise Ratio (SNR) in the ELINT system. We evaluated the accuracy and precision of the random variables characterizing the proposed estimators as a function of the SNR. Results indicate a probability of detection close to 1 and show good performance, even for scenarios with a SNR slightly less than -10 dB. The contributions in this work offer enhancements to noise radar capabilities while facilitating improvements in ESM systems.

RCDformer: Transformer-based dense depth estimation by sparse radar and camera