Soil moisture (SM) is vital for understanding the Earth’s water cycle, soil health, and climate change. Missions like SMAP and AirMOSS estimate SM, and more recently, GNSS-reflectometry systems like CYGNSS are used for this purpose. CYGNSS offers high temporal resolution and lower cost compared to traditional microwave observatories. Current SM estimation methods using CYGNSS fall into two categories: 1) Physics-Based (PB) models, which are accurate but depend on detailed geophysical parameters often unavailable globally; and 2) Black-box Machine Learning (ML) models, which are generally not constrained by physics-based models and can produce inconsistent results. We propose a novel physics-guided ML model with two modules: a PB forward model generating Delay Doppler Maps (DDMs) from SM, and an ML inverse model predicting SM from DDMs. The PB model selected is the Improved Geometric Optics with Topography (IGOT), and the ML model is a multi-layer perceptron, trained in two stages: pre-training and fine-tuning, both influenced by the PB forward model. Inspired by CycleGAN, the pre-training stage optimizes a cycle consistency loss function to minimize the differences between measured DDM and synthetic DDM generated by the PB forward model, and between arbitrary SM and predicted SM. This method addresses the in situ data scarcity challenge and encourages the ML model to act as an inverse of the PB forward model, producing consistent results. The fine-tuning stage minimizes the difference between predicted and in situ measured SM and incorporates the pre-training loss function as a regularizer. The ML models will benefit from using other satellite and ancillary data sources, including SMAP. Our hybrid approach leverages the ML model’s ability to deduce complex data relationships while ensuring physically consistent results. Additionally, it reduces the time complexity of SM estimation due to offline training and moderate complexity inference. Validation will be conducted using CYGNSS observations over three SoilSCAPE sites: San Luis Valley, CO; Jornada Experimental Range, NM; and Walnut Gulch Experimental Watershed, AZ. These sites offer diverse conditions, from smooth terrain and dry soil to complex topographies, monsoon seasons, and vegetation, providing a robust testing ground for the proposed method.

The Age of Incorrect Information (AoII) is a recently proposed metric for real-time remote monitoring systems. In particular, AoII measures the time the information at the monitor is incorrect, weighted by the magnitude of this incorrectness, thereby combining the notions of freshness and distortion. This paper addresses the definition of an AoII-optimal transmission policy in a discrete-time communication scheme with a resource constraint and a hybrid automatic repeat request (HARQ) protocol. Considering an N-ary symmetric Markov source, the problem is formulated as an infinite-horizon average-cost constrained Markov decision process (CMDP). Interestingly, it is proved that, under some conditions, the optimal transmission policy is to never transmit. This reveals a region of the source dynamics where communication is inadequate in reducing the AoII. Elsewhere, there exists an optimal transmission policy, which is a randomized mixture of two discrete threshold-based policies that randomize on at most one state. The optimal threshold and the randomization component are derived analytically. Numerical results illustrate the impact of the source dynamics, channel conditions, and resource constraints on the average AoII.

The Age of Incorrect Information (AoII) is studied within the context of remote monitoring a Markov source using variable-length stop-feedback (VLSF) coding. Leveraging recent results on the non-asymptotic channel coding rate, we consider sources with small cardinality, where feedback is non-instantaneous as the transmitted information and feedback message have comparable lengths. We focus on the feedback sequence, i.e. the times of feedback transmissions, and derive AoII-optimal and delay-optimal feedback sequences. Our results showcase the impact of the feedback sequence on the AoII, revealing that a lower average delay does not necessarily correspond to a lower average AoII. We discuss the implications of our findings and suggest directions for coding scheme design.

This paper presents an enhanced methodology for Recurrence Quantification Analysis (RQA) designed specifically for video analysis. By utilizing image quality metrics, with a focus on the Peak Signal-to-Noise Ratio (PSNR), we determine meaningful values for the RQA threshold ε, a critical factor for successful image processing. Utilizing the False Nearest Neighbors (FNN) technique, we identify the optimal embedding dimension D for each patch within the video frames. Our approach produces a heatmap that visualizes temporal recurrence information for each video patch.