Zhitong Sun

The positive and negative terrains (P–N terrains) of the Loess Plateau of China are important geographical topography elements for measuring the degree of surface erosion and distinguishing the types of landforms. Loess shoulder-lines are an important terrain feature in the Loess Plateau and are often used as a criterion for distinguishing P–N terrains. The extraction of shoulder lines is important for predicting erosion and recognising a gully head. However, existing extraction algorithms for loess shoulder-lines in areas with insignificant slopes need to be improved. This study proposes a regional fusion (RF) method that integrates the slope variation-based method and region-growing algorithm to extract loess shoulder-lines based on a Digital Elevation Model (DEM) at a spatial resolution of 5 m. The RF method introduces different terrain factors into the growth standards of the region-growing algorithm to extract loess-shoulder lines. First, we employed a slope-variation-based method to build the initial set of loess shoulder-lines and used the difference between the smoothed and real DEMs to extract the initial set for the N terrain. Second, the region-growing algorithm with improved growth standards was used to generate a complete area of the candidate region of the loess shoulder-lines and the N terrain, which were fused to generate and integrate contours to eliminate the discontinuity. Finally, loess shoulder-lines were identified by detecting the edge of the integrated contour, with results exhibiting congregate points or spurs, eliminated via a hit-or-miss transform to optimise the final results. Validation of the experimental area of loess ridges and hills in Shaanxi Province showed that the accuracy of the RF method based on the Euclidean distance offset percentage within a 10-m deviation range reached 96.9% compared to the manual digitalisation method. Based on the mean absolute error and standard absolute deviation values, compared with Zhou’s improved snake model and the bidirectional DEM relief-shading methods, the proposed RF method extracted the loess shoulder-lines highly accurately.

Crop growth and yield monitoring are essential for food security and agricultural economic return prediction. Remote sensing is an efficient technique for measuring growing season crop canopies and providing information on the spatial variability of crop yields. In this study, ten vegetation indices (VIs) derived from time series PlanetScope and Sentinel-2 images were used to investigate the potential to estimate corn grain yield with different regression methods. A field-scale spatial crop yield prediction model was developed and used to produce yield maps depicting spatial variability in the field. Results from this study clearly showed that high-resolution PlanetScope satellite data could be used to detect the corn yield variability at field level, which could explain 15% more variability than Sentinel-2A data at the same spatial resolution of 10 m. Comparison of the model performance and variable importance measure between models illustrated satisfactory results for assessing corn productivity with VIs. The green chlorophyll vegetation index (GCVI) values consistently produced the highest correlations with corn yield, accounting for 72% of the observed spatial variation in corn yield. More reliable quantitative yield estimation could be made using a multi-linear stepwise regression (MSR) method with multiple VIs. Good agreement between observed and predicted yield was achieved with the coefficient of determination value being 0.81 at 86 days after seeding. The results would help farmers and decision-makers generate predicted yield maps, identify crop yield variability, and make further crop management practices timely.

Water quality estimation tools based on real-time monitoring are essential for the effective management of organic pollution in watersheds. This study aims to monitor changes in the levels of chemical oxygen demand (COD, CODMn) and dissolved organic matter (DOM) in Erhai Lake Basin, exploring their relationships and the ability of DOM to estimate COD and CODMn. Excitation emission matrix–parallel factor analysis (EEM–PARAFAC) of DOM identified protein-like component (C1) and humic-like components (C2, C3, C4). Combined with random forest (RF), maximum fluorescence intensity (Fmax) values of components were selected as estimation parameters to establish models. Results proved that the COD of rivers was more sensitive to the reduction in C1 and C2, while CODMn was more sensitive to C4. The DOM of Erhai Lake thrived by internal sources, and the relationship between COD, CODMn, and DOM of Erhai Lake was more complicated than rivers (inflow rivers of Erhai Lake). Models for rivers achieved good estimations, and by adding dissolved oxygen and water temperature, the estimation ability of COD models for Erhai Lake was significantly improved. This study demonstrates that DOM-based machine learning can be used as an alternative tool for real-time monitoring of organic pollution and deepening the understanding of the relationship between COD, CODMn, and DOM, and provide a scientific basis for water quality management.