Inland waters, characterized by small scale but a large number, play an important role in the carbon budget and global carbon cycle. Colored dissolved organic matter (CDOM) is a significant indicator used for tracing dissolved organic carbon (DOC) in inland waters. Accurate remote-sensing estimation of CDOM concentration is still a challenge due to complex optical properties of inland waters. Many efforts have been made to estimate high concentration CDOM, leading to a knowledge gap in using remote sensing to estimate low concentration CDOM, which results in difficulty and uncertainty for estimating total carbon storage in global inland waters. Currently, few studies are devoted to estimating low concentration CDOM, while Landsat-8’s applicability for estimating low concentration CDOM is still unknown. In this study, two datasets, NRL_SFE and Lake_Erie, were collected to represent extremely low CDOM conditions that a_CDOM (440) (the absorptions of CDOM at 440 nm) ranges 0.215–1.165 m^–1, and 0.066–1.242 m^–1, respectively. The best CDOM retrieval model (validation results: R² = 0.78; RMSE = 0.161 m^–1; MRE = 26.02%), a_CDOM(440) = 0.483x^–1.776, x = R_rs(B2)/R_rs(B4), was developed for monitoring CDOM in the two regions. Results show that Landsat-8 and the best model work well for estimating low concentration CDOM in inland waters. In addition, we have proved that Landsat-8 surface reflectance products, which are freely provided by USGS, are convenient and useful for developing remote sensing algorithm of CDOM estimation after correcting water surface reflectance. The image-derived CDOM’s spatial patterns in Lake Erie demonstrate the Landsat-8’s applicability to observe spatiotemporal variations of low concentration CDOM.

Global food security and nutrition is suffering from unprecedented challenges. To reach a world without hunger and malnutrition by implementing precision agriculture, satellite remote sensing plays an increasingly important role in field crop monitoring and management. Alfalfa, a global widely distributed forage crop, requires more attention to predict its yield and quality traits from satellite data since it supports the livestock industry. Meanwhile, there are some key issues that remain unknown regarding alfalfa remote sensing from optical and synthetic aperture radar (SAR) data. Using Sentinel-1 and Sentinel-2 satellite data, this study developed, compared, and further integrated new optical- and SAR-based satellite models for improving alfalfa yield and quality traits prediction, i.e., crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and neutral detergent fiber digestibility (NDFD). Meanwhile, to better understand the physical mechanism of alfalfa optical remote sensing, a unified hybrid leaf area index (LAI) retrieval scheme was developed by coupling the PROSAIL radiative transfer model, spectral response function of the desired optical satellite, and a random forest (RF) model, denoted as a scalable optical satellite-based LAI retrieval framework. Compared to optical vegetation indices (VIs) that only capture canopy information, the results indicate that LAI had the highest correlation (r = 0.701) with alfalfa yield due to its capacity in delivering the vegetation structure characteristics. For alfalfa quality traits, optical chlorophyll VIs presented higher correlations than LAI. On the other hand, LAI did not provide a significant additional contribution for predicting alfalfa parameters in the RF developed optical prediction model using VIs as inputs. In addition, the optical-based model outperformed the SAR-based model for predicting alfalfa yield, CP, and NDFD, while the SAR-based model showed better performance for predicting ADF and NDF. The integration of optical and SAR data contributed to higher accuracy than either optical or SAR data separately. Compared to a traditional embedded integration approach, the combination of multisource heterogeneous optical and SAR satellites was optimized by multiple linear regression (yield: R² = 0.846 and RMSE = 0.0354 kg/m²; CP: R² = 0.636 and RMSE = 1.57%; ADF: R² = 0.559 and RMSE = 1.926%; NDF: R² = 0.58 and RMSE = 2.097%; NDFD: R² = 0.679 and RMSE = 2.426%). Overall, this study provides new insights into forage crop yield prediction for large-scale fields using multisource heterogeneous satellites.

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image recognition in the medical field, which requires large-scale and high-quality training datasets consisting of raw images and annotated images. However, suitable experimental datasets for cervical spine X-ray are scarce. We fill the gap by providing an open-access Cervical Spine X-ray Atlas (CSXA), which includes 4963 raw PNG images and 4963 annotated images with JSON format (JavaScript Object Notation). Every image in the CSXA is enriched with gender, age, pixel equivalent, asymptomatic and symptomatic classifications, cervical curvature categorization and 118 quantitative parameters. Subsequently, an efficient algorithm has developed to transform 23 keypoints in images into 77 quantitative parameters for cervical spine disease diagnosis and treatment. The algorithm’s development is intended to assist future researchers in repurposing annotated images for the advancement of machine learning techniques across various image recognition tasks. The CSXA and algorithm are open-access with the intention of aiding the research communities in experiment replication and advancing the field of medical imaging in cervical spine.