Although visible-near-infrared spectroscopy can rapidly and accurately determine soil nutrients without sample destruction, some problems remain unresolved, such as the mismatch of the established spectral model with different types of samples, limiting the wide application of this technology. Here, we took riverside and mountain soils as examples to explore the calibration transfer between two different types of soils by the WMPDS-S/B algorithm (wavelet multiscale piecewise direct standardization combined with Slope/Bias correction method) and by adding new samples. The predicted TN and TC concentrations improved significantly after being transformed. Compared with adding new samples, the WMPDS-S/B algorithm obtained more accurate results. The average relative errors dropped from 440.2% (without transformation) to approximately 6% for TN and from 342.0% to approximately 7% for TC. The maximum relative errors were reduced from 538.1% to less than 20% for TN and from 403.7% to less than 20% for TC. The RMSEP decreased from 2.42 to approximately 0.04 for TN and from 15.74 to approximately 0.4 for TC. The WMPDS-S/B algorithm had advantages in selecting fewer known samples and obtaining better prediction results. In contrast to past studies, which resolved the calibration transfer between different spectrometers and the measurement environment for the same samples, our study resolved the calibration transfer between different types of samples under the same spectrometer and the measurement environment. The former could only be used for correction among instruments, while the latter fundamentally solved the problem of model sharing across different samples.

The calibration transfer between instruments is mainly aimed at the calibration transfer between normal spectrometers. There are few studies on the calibration transfer of soil nutrient concentration from a hyperspectral camera to a normal spectrometer. In this paper, 164 soil samples from three regions in Qingdao, China, were collected. The spectral data of normal spectrometer and hyperspectral camera and the concentration of total carbon and nitrogen were obtained. And then, the models of soil total carbon and nitrogen content were established by using the spectral data of a normal spectrometer. The hyperspectral data were transferred by a variety of methods, such as single conventional calibration transfer algorithm, combination of multiple calibration transfer algorithms, and calibration transfer algorithm after spectral pretreatment. The transferred hyperspectral data were predicted by the total carbon and total nitrogen concentration model established by using a normal spectrometer. The absolute coefficients Rt2 and root mean square error of prediction (RMSEP) were used to evaluate the prediction performance after calibration transfer. After trying many calibration transfer methods, the prediction performance of calibration transfer by the Repfile-PDS and Repfile-SNV methods was the best. In the calibration transfer of the Repfile-PDS method, when the number of PDS windows was 27 and the number of standard data was 40, the Rt2 and the RMSEP of TC concentration were 0.627 and 2.351. When the number of PDS windows was 25 and the number of standard data was 100, the Rt2 and the RMSEP of TN concentration were 0.666 and 0.297. In the calibration transfer of the Repfile-SNV method, when the number of TC and TN standard data was 120, the Rt2 was the largest, 0.701 and 0.722, respectively, and the RMSEP was 2.880 and 0.399, respectively. After the hyperspectral data were calibration transferred by the above algorithms, they could be predicted by the soil TC and TN concentration model established by using a normal spectrometer, and better prediction results can be obtained. The solution of the calibration transfer of soil nutrient concentration from the hyperspectral camera to the normal spectrometer provides a powerful basis for rapid prediction of a large number of image information data collected by using a hyperspectral camera. It greatly reduces the workload and promotes the application of hyperspectral camera in quantitative analysis and rapid measurement technology.

Marine sediments record much information of the ecological process, which highly correlated with global and local environmental change. Particle size and its distributions of sediments indicate different ecological functions, thus are the key questions in marine ecology. The analysis method is tedious and laborious, which is not conducive for in-situ monitoring. Here, a spectral analysis was explored using surface sediments sampled in the intertidal zone of Dongdayang village, Qingdao, China. These samples were dried and sieved to pass through the mesh size of 0.3 mm, 0.2 mm, 0.1 mm, and 0.075 mm, respectively. Then, four types of subsamples were collected with the particle size of 0.3-0.2 mm, 0.2-0.1 mm, 0.1-0.075 mm, and < 0.075 mm, respectively. The visible and near infrared reflectance spectra (226-975nm) of these subsamples with different particle size were measured. Results showed that there was a negative correlation between the spectral reflectance and the particle size. And the characteristic spectra for particle size classification were 926-975nm. These particle size were classified by the support vector machine algorithm. The classification accuracy for the calibration set and validation set was 100% and 89.06%, respectively. Furthermore, the fusion classifier was compared with the single classifier. Three spectral bands were selected as the single particle size classifier, that is 226-325nm, 826-925nm and 226-975nm. These three single classifiers were fused by voting method, forming multiple classifier fusion. A fusion classifier was recommended whose validation set had the classification accuracy of 93.75%, better than any single classifier. Multiple classifier fusion is a good tool for searching the characteristic spectra of the chemical and physical parameter in sediments. This method provides a solution for the division of particle size in sediment.

Stereo matching plays a vital role in underwater environments, where accurate depth estimation is crucial for applications such as robotics and marine exploration. However, underwater imaging presents significant challenges, including noise, blurriness, and optical distortions that hinder effective stereo matching. This study develops two specialized stereo matching networks: UWNet and its lightweight counterpart, Fast-UWNet. UWNet utilizes self- and cross-attention mechanisms alongside an adaptive 1D-2D cross-search to enhance cost volume representation and refine disparity estimation through a cascaded update module, effectively addressing underwater imaging challenges. Due to the need for timely responses in underwater operations by robots and other devices, real-time processing speed is critical for task completion. Fast-UWNet addresses this challenge by prioritizing efficiency, eliminating the reliance on the time-consuming recurrent updates commonly used in traditional methods. Instead, it directly converts the cost volume into a set of disparity candidates and their associated confidence scores. Adaptive interpolation, guided by content and confidence information, refines the cost volume to produce the final accurate disparity. This streamlined approach achieves an impressive inference speed of 0.02 s per image. Comprehensive tests conducted in diverse underwater settings demonstrate the effectiveness of both networks, showcasing their ability to achieve reliable depth perception.