This paper presents generalized Arlery-Tan-Rabaste-Levenshtein lower bounds on the maximum aperiodic ambiguity function (AF) magnitude of unimodular sequences under certain delay-Doppler low ambiguity zones (LAZ). Our core idea is to explore the upper and lower bounds on the Frobenius norm of the weighted auto- and cross-AF matrices by introducing two weight vectors associated with the delay and Doppler shifts, respectively. As a second major contribution, we demonstrate that our derived lower bounds are asymptotically achievable with selected Chu sequence sets by analyzing their maximum auto- and cross-AF magnitudes within certain LAZ.

Remote sensing study of the carbon cycle in coastal marine systems using machine learning methods has received significant attention recently. The partial pressure of carbon dioxide (CO2) in seawater (pCO2w) is a crucial parameter for quantifying the air-sea carbon dioxide exchange. However, previous studies did not consider the effect of sea surface roughness (SSR) onpCO2wcaused by wind, waves, and other ocean dynamics. In this study, for the first time, we used SSR data derived from Synthetic Aperture Radar (SAR), with sea surface temperature (SST), chlorophyll-a concentration (Chl-a), sea surface salinity (SSS) conventional remote sensing data to predict thepCO2wdata along the North American East Coast from 2015 to 2021 using the Cubist algorithm. Results show that the semi-analytic algorithm, Cubist, performs best among 20 statistical and machine learning models. Moreover, compared with the control experiment without the SSR data, after adding SSR as an independent variable, the final Cubist model’s coefficient of determination (R2) increased from 0.88 to 0.95, and the root mean square error reduced from 21.75 to 14.79 μatm. Our results showed significant improvement over the previous study (R2= 0.8), proving the applicability of applying SSR data in retrieving high spatial resolution carbonate system parameters in the future, especially for coastal regions where wind and wave dynamics are more variable.

Accurately retrieving ocean interior parameters from remote sensing observations is essential for ocean and climate studies because direct observations are sparse and costly. Furthermore, high-resolution structure of seawater properties is critical for understanding the oceanic processes and changes on multiple scales. Here, we designed a new method based on a deep neural network to retrieve subsurface temperature anomaly (STA) and subsurface salinity anomaly (SSA) in the Pacific Ocean at high (1/4°) and super (1/12°) horizontal resolution. We utilized multisource satellite-observed sea surface data (e.g., sea level, temperature, salinity, and wind vector) as inputs. The results revealed that our model retrieved the high- and super-resolution STA/SSA with high accuracy, and the model was reliable in a wide range of depths (near surface to 4000 m) and times (all months in 2014). Regarding the high-resolution STA (SSA) estimation, the average coefficient of determination ($R^{2}$) was 0.984 (0.966), and the average root-mean-squared error (RMSE) was 0.068 °C (0.016 psu). For the super-resolution STA, the average$R^{2}$was 0.988 and RMSE was 0.093 °C. Here, we established an effective technique that improved the resolution and accuracy of estimating the ocean interior parameters from satellite observation. The new technique provides some new insights into oceanic observation and dynamics.