Monitoring of CO₂ column concentrations is valuable for atmospheric research. A mobile open-path system was developed based on tunable diode laser absorption spectroscopy (TDLAS) to measure atmospheric CO₂ column concentrations. A laser beam was emitted downward from a distributed feedback diode laser at 2 μm and then reflected by the retroreflector array on the ground. We measured the CO₂ column concentrations over the 20 and 110 m long vertical path. Several single-point sensors were distributed at different heights to provide comparative measurements for the open-path TDLAS system. The results showed that the minimum detection limit of system was 0.52 ppm. Some similarities were observed in trends from the open-path TDLAS system and these sensors, but the average of these sensors was more consistent with the open-path TDLAS system values than the single-point measurement. These field measurements demonstrate the feasibility of open-path TDLAS for measuring the CO₂ column concentration and monitoring carbon emission over large areas.

When backpropagation neural network (BPNN) is often applied to supervised classification, problems arise, including a slow convergence rate, local extremum, and difficulty in determining the number of hidden layers and hidden nodes that affect the classification accuracy and efficiency. These problems can be overcome by using smarter network designs. Adaptive boosting (AdaBoost), which combines multiple weak classifiers to create a strong classifier, has a strong classification advantage. In this article, we propose an acoustic seabed classification method that combines AdaBoost with the particle swarm optimization (PSO). The PSO-BP-AdaBoost algorithm uses multibeam echosounder backscatter data to solve the multiclassification problem of diverse seafloor sediment types with small differences between types. We optimize a BPNN using the PSO algorithm to obtain the optimal initial weight and threshold and combine these to form an AdaBoost strong classifier. The input data is obtained from the sonar mosaic from multibeam echosounder backscatter data collected in Jiaozhou Bay using a series of fine processing techniques. These processing techniques result in 34-dimensional (34-D) features using ReliefF analysis. The most advantageous 8-D features are used as input into the AdaBoost algorithm based on one-level decision tree, PSO-BP algorithm, support vector machine (SVM), and PSO-BP-AdaBoost algorithm. The PSO-BP-AdaBoost classification model has better classification accuracy. The overall accuracy is improved by 12.68%, 6.78%, and 3.56%, respectively, which demonstrates that the PSO-BP-AdaBoost algorithm can be effectively applied to acoustic seabed classification and identification and achieves high precision.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon data is the emerging satellite-based LiDAR data, widely used in surveying and mapping due to its small photometric spot and high density. Since ICESat-2 data collect weak signals, it is difficult to denoise in shallow sea island areas, and the quality of the denoising method will directly affect the precision of bathymetry. This paper proposes a back propagation (BP) neural network-based denoising algorithm for the data characteristics of shallow island reef areas. First, a horizontal elliptical search area is constructed for the photons in the dataset. Suitable feature values are selected in the search area to train the BP neural network. Finally, data with a geographic location far apart, including daily and nightly data, are selected respectively for experiments to test the generality of the network. By comparing the results with the confidence labels provided in the official documents of the ATL03 dataset, the DBSCAN algorithm, and the manual visual interpretation, it is proved that the denoising algorithm proposed in this paper has a better processing effect in shallow island areas.

Shallow-water depth information is essential for ship navigation and fishery farming. However, the accurate acquisition of shallow-water depth has been a challenge for marine mapping. Combining Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data with multispectral data, satellite-derived bathymetry is a promising solution through which to obtain bathymetric information quickly and accurately. This study proposes a photon refraction correction method considering sea-surface undulations to address errors in the underwater photons obtained by the ICESat-2. First, the instantaneous sea surface and beam emission angle are integrated to determine the sea-surface incidence angle. Next, the distance of photon propagation in water is determined using sea-surface undulation and Snell’s law. Finally, position correction is performed through geometric relationships. The corrected photons were combined with the multispectral data for bathymetric inversion, and a bathymetric map of the Yongle Atoll area was obtained. A bathymetric chart was created using the corrected photons and the multispectral data in the Yongle Atoll. Comparing the results of different refraction correction methods with the data measured shows that the refraction correction method proposed in this paper can effectively correct bathymetry errors: the root mean square error is 1.48 m and the R2 is 0.86.