In the study of the inversion of soil multi-species heavy metal element concentrations using hyperspectral techniques, the selection of feature bands is very important. However, interactions among soil elements can lead to redundancy and instability of spectral features. In this study, heavy metal elements (Pb, Zn, Mn, and As) in entisols around a mining area in Harbin, Heilongjiang Province, China, were studied. To optimise the combination of spectral indices and their weights, radar plots of characteristic-band Pearson coefficients (RCBP) were used to screen three-band spectral index combinations of Pb, Zn, Mn, and As elements, while the Catboost algorithm was used to invert the concentrations of each element. The correlations of Fe with the four heavy metals were analysed from both concentration and characteristic band perspectives, while the effect of spectral inversion was further evaluated via spatial analysis. It was found that the regression model for the inversion of the Zn elemental concentration based on the optimised spectral index combinations had the best fit, with R² = 0.8786 for the test set, followed by Mn (R² = 0.8576), As (R² = 0.7916), and Pb (R² = 0.6022). As far as the characteristic bands are concerned, the best correlations of Fe with the Pb, Zn, Mn and As elements were 0.837, 0.711, 0.542 and 0.303, respectively. The spatial distribution and correlation of the spectral inversion concentrations of the As and Mn elements with the measured concentrations were consistent, and there were some differences in the results for Zn and Pb. Therefore, hyperspectral techniques and analysis of Fe elements have potential applications in the inversion of entisols heavy metal concentrations and can improve the quality monitoring efficiency of these soils.

Effective features derived from an original hyperspectral image (HSI) are quite important to improve the classification performance. An improved feature set, namely HGFM, is constructed by integrating harmonic analysis (HA) optimized by a multiscale guided filter (GF) with morphological operation for HSI classification. To establish HGFM, HA is first adopted to convert the HSI from spectral space to the frequency domain represented by amplitude, phase, and residual. With the first component of minimum noise fraction obtained from the original HSI as the guidance image, the harmonic components are then processed by the multiscale GF. Finally, the obtained results are then operated via morphological opening by reconstruction and closing by reconstruction to generate an improved feature set for classification. The HGFM features are input to an ensemble learning (EL) based on classification framework, in which EL plays an auxiliary role to enhance the classification stability and reliability. Three commonly used HSIs are used for experiments, and different feature sets are evaluated by comparing EL and rotation forest, support vector machine optimized by particle swarm optimization, random forest, and others. Compared with benchmark feature sets, the proposed HGFM feature set can better depict the details of objects easily, and the experimental results confirm the effectiveness in terms of classification accuracy and generalization ability.

It is of great significance to determine the level of demand for thermal environment regulation and the availability of blue–green spaces for thermal environment regulation to alleviate the effects of urban heat islands. Taking Shandong Province, China, as the study area, combined multi–source remote sensing data are used in this study to construct the index system of cold island supply capacity (CIS) and the cold island demand level (CID). We use the methods of spatial regression, quadrant division, and coupling coordination degree to analyze the correlation, matching status, and the level of coordinated development between the supply capacity and demand for the cooling effect. We also explore the change law and spatial characteristics of the blue–green spaces’ cooling effects supply and demand matching. Results show that: (1) The CIS and the CID are significantly negatively correlated and spatially heterogeneous in distribution, with a significant spatial spillover effect. (2) The dominant type of supply and demand is one of low supply and high demand, which means that the supply and demand for cool islands’ cooling effect are unbalanced, with significant problems of spatial mismatch and quantitative imbalance. (3) The coupling between supply capacity and demand level is on the verge of becoming dysfunctional because the uneven distribution of urban buildings, population, and blue–green spaces reduce the coupling between supply and demand levels. This research can provide a new perspective and scientific basis for the study of the cooling effects of blue and green spaces and the mitigation of the heat island effect in densely populated urban centers.

To explore the potential of using the mineral alteration information extracted by remote sensing technology to indirectly estimate the heavy metal content of salinized soil, 23 sampling points were uniformly set up in the town of Gudao in the Yellow River Delta as the research area in 2022. The concentrations of seven heavy metals, Cr, Cu, Pb, Zn, As, Mn and Ni, at the sampling points were determined in laboratory tests. Spectral derivative indices, topographic factors, and mineral alteration information (iron staining, hydroxyl, and carbonate ions) were extracted and screened as modeling factors using Sentinel 2 imagery. An inverse model of heavy metal content was constructed using the random forest algorithm, and the model accuracy was evaluated using the cross-validation method. The results of the study show that: (1) Hydroxyl and carbonate ion alteration can be effectively used for the inversion of soil As and Ni content in this study area. Iron-stained alteration can be used as a modeling factor in the inversion of Cr, Cu, Pb, Zn, and Mn concentrations. (2) The inclusion of alteration information improves the accuracy of heavy metal content inversion. The Cu concentration was verified to be the best predictor, with an RMSE of 3.309, MAPE of 11.072%, and R² of 0.904, followed by As, Ni, and Zn; the predictive value of Mn, Cr and Pb was average. (3) Based on the results of concentration inversion, the high concentration areas of As, Ni, and Mn are primarily distributed on both sides of the river and around lakes and ponds. The high-concentration areas of Zn were mainly distributed in the farmland areas on both sides of the river. Areas with high concentrations of Cu were mainly distributed in the eastern oil extraction area, both sides of the rivers, and around lakes.

The invasive species Spartina alterniflora show a significant coexistence zonation pattern with local Phragmites australis in different mixture ratio, increasing the difficulty to monitor their distribution directly by remote sensing. Canopy chlorophyll content (CCC) is an important indicator to monitor the growth and physiological status. The objective of this study was to estimate CCC under different mixture ratio. Five spectral indices were selected and combined via back propagation (BP) neural network model for estimating CCC. Combining multi-indices yielded better results (R2 = 0.7729, RMSE = 53.01 ug.cm-2) on average than the best single spectral index (R2 = 0.7190, RMSE = 63.53 ug.cm-2) without distinguishing interspecies competition, with a total increase of 7.5% in the R2 and a decrease of 16.56% in the RMSE. Meanwhile, when considering interspecies competition, the estimating results obtained by the BP neural network model achieved a further improvement of the R2 value, ranging from 3.57% to 20.37%, while the prediction error reduced at varying degrees (maximum reduction of 23.78%). The results indicate that combining multi-indices by BP neural network model can alleviate the influence of interspecies competition and achieve higher estimating accuracy.