Aboveground biomass (AGB) is an important basis for wheat yield formation. It is useful to timely collect the AGB data to monitor wheat growth and to build high-yielding wheat groups. However, as traditional AGB data acquisition relies on destructive sampling, it is difficult to adapt to the modernization of agriculture, and the estimation accuracy of spectral data alone is low and cannot solve the problem of index saturation at later stages. In this study, an unmanned aerial vehicle (UAV) with an RGB camera and the real-time kinematic (RTK) was used to obtain imagery data and elevation data at the same time during the critical fertility period of wheat. The cumulative percentile and the mean value methods were then used to extract the wheat plant height (PH), and the color indices (CIS) and PH were combined to invert the AGB of wheat using parametric and non-parametric models. The results showed that the accuracy of the model improved with the addition of elevation data, and the model with the highest accuracy of multi-fertility period estimation was PLSR (PH + CIS), with R², RMSE and NRMSE of 0.81, 1248.48 kg/ha and 21.77%, respectively. Compared to the parametric models, the non-parametric models incorporating PH and CIS greatly improved the prediction of AGB during critical fertility periods in wheat. The inclusion of elevation data therefore greatly improves the accuracy of AGB prediction in wheat compared to traditional spectral prediction models. The fusion of UAV-based elevation data and image information provides a new technical tool for multi-season wheat AGB monitoring.

Surface water quality is critical to ecosystem health and sustainable development, yet conventional monitoring falls short of spatiotemporally continuous assessment. Remote sensing coupled with machine learning has become a powerful paradigm for large-scale quantitative retrieval of water quality parameters (WQPs). This review examines the progress and prospects of machine-learning-driven multi-source remote sensing for surface WQP retrieval. A systematic literature review following PRISMA 2020 guidelines, covering 437 Web of Science Core Collection publications (2000–2025), reveals exponential growth, with China and the United States contributing 70.3% of total output. A critical synthesis covers four dimensions: (1) characteristics and fusion strategies of satellite, airborne, and ground-based remote sensing data; (2) modeling features of traditional machine learning (SVR, RF, GBDT), deep learning (CNN, RNN, Transformer), and hybrid approaches; and (3) retrieval advances for optically active versus non-optically active parameters—the former approaches operational readiness while the latter remains constrained by weak indirect spectral correlations; and (4) uncertainty sources and mitigation strategies across the data–model–parameter chain. Five key challenges are identified: limited model generalizability, insufficient physical interpretability, optical heterogeneity and parameter coupling, scarce in situ data, and multi-source fusion bottlenecks. Five future directions are proposed—transfer learning, physically informed explainable machine learning, non-optically active parameter retrieval, benchmark dataset development, and intelligent multi-source fusion—offering a roadmap toward operational surface water quality monitoring.