Plant nitrogen concentration (PNC) is a key indicator reflecting the growth and development status of plants. The timely and accurate monitoring of plant PNC is of great significance for the refined management of crop nutrition in the field. The rapidly developing sensor technology provides a powerful means for monitoring crop PNC. Although RGB images have rich spatial information, they lack the spectral information of the red edge and near infrared bands, which are more sensitive to vegetation. Conversely, multispectral images offer superior spectral resolution but typically lag in spatial detail compared to RGB images. Therefore, the purpose of this study is to improve the accuracy and efficiency of crop PNC monitoring by combining the advantages of RGB images and multispectral images through image-fusion technology. This study was based on the booting, heading, and early-filling stages of winter wheat, synchronously acquiring UAV RGB and MS data, using Gram–Schmidt (GS) and principal component (PC) image-fusion methods to generate fused images and evaluate them with multiple image-quality indicators. Subsequently, models for predicting wheat PNC were constructed using machine-selection algorithms such as RF, GPR, and XGB. The results show that the RGB_B1 image contains richer image information and more image details compared to other bands. The GS image-fusion method is superior to the PC method, and the performance of fusing high-resolution RGB_B1 band images with MS images using the GS method is optimal. After image fusion, the correlation between vegetation indices (VIs) and wheat PNC has been enhanced to varying degrees in different growth periods, significantly enhancing the response ability of spectral information to wheat PNC. To comprehensively assess the potential of fused images in estimating wheat PNC, this study fully compared the performance of PNC models before and after fusion using machine learning algorithms such as Random Forest (RF), Gaussian Process Regression (GPR), and eXtreme Gradient Boosting (XGB). The results show that the model established by the fusion image has high stability and accuracy in a single growth period, multiple growth periods, different varieties, and different nitrogen treatments, making it significantly better than the MS image. The most significant enhancements were during the booting to early-filling stages, particularly with the RF algorithm, which achieved an 18.8% increase in R², a 26.5% increase in RPD, and a 19.7% decrease in RMSE. This study provides an effective technical means for the dynamic monitoring of crop nutritional status and provides strong technical support for the precise management of crop nutrition.

Chlorophyll serves as a crucial indicator for crop growth monitoring and reflects the health status of crops. Hyperspectral remote sensing technology, leveraging its advantages of repeated observations and high-throughput analysis, provides an effective approach for non-destructive chlorophyll monitoring. However, determining the optimal spectral scale remains the primary bottleneck constraining the widespread application of hyperspectral remote sensing in crop chlorophyll estimation: excessively fine spectral scale readily introduces redundant information, leading to dramatically increased data dimensions and reduced computational efficiency; conversely, overly coarse spectral scale risks losing critical spectral features such as absorption peaks and reflection troughs, thereby compromising model accuracy. Therefore, establishing an appropriate spectral scale that effectively preserves spectral feature information while maintaining computational efficiency is crucial for enhancing the accuracy and practicality of chlorophyll remote sensing estimation. To address this, this study proposes a three-dimensional analytical framework integrating “spectral scale—machine learning algorithm—crop growth stage” to systematically solve the scale optimization problem. Ground-truth measurements and hyperspectral data from five growth stages of winter wheat in Fengyang County, Anhui Province, were collected. Spectral bands sensitive to chlorophyll were analyzed, and four modeling methods—Ridge Regression (RR), K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Regression (SVR)—were employed to integrate data from different spectral scales with respective bandwidths of 2, 3, 5, 7, 10, 20, and 50 nanometers (nm). The results evaluated the response characteristics of raw band reflectance to chlorophyll values and its impact on machine learning-based chlorophyll estimation across different spectral scales. Results indicate: (1) Canopy spectra significantly correlated with winter wheat chlorophyll primarily reside in the red and red-edge bands; (2) For single-scale analysis, larger spectral scales (10, 20 nm) enhance monitoring accuracy compared to 1 nm high-resolution data, while medium and small scales (5, 7 nm) may degrade accuracy due to redundant noise introduction. (3) Integrating growth stages, spectral scales, and machine learning revealed optimal monitoring accuracy during the jointing and heading stages using 1–5 nm spectral scales combined with the KNN algorithm. For the booting, flowering, and grain filling stages, the highest accuracy was achieved using 20–50 nm spectral scales combined with either the KNN or RF algorithm. The results indicate that high-precision chlorophyll inversion for winter wheat does not rely on a single fixed model or scale, but rather on the dynamic adaptation of the “scale-model-growth stage” triad. The proposed systematic framework not only provides a theoretical basis for chlorophyll monitoring using multi-platform remote sensing data, but also offers methodological support for future crop-sensing sensor design and data processing strategy optimization.

Forestry enterprises play a pivotal role in economic development, ecological civilization construction, and sustainable development. This study employs GIS-based spatial analysis to examine the distribution patterns and interrelationships of forestry enterprises, investigating their key determinants and spatial heterogeneity. The findings provide valuable insights for policymakers aiming to optimize industrial structures and enhance national ecological security. This research develops a comprehensive evaluation index system to assess the factors influencing forestry industry development in China. Nine factors are considered: human resources, economic development, industrial structure, technological support, trade development, financial environment, natural conditions, urbanization, and transportation. Using panel data from 367 cities in 2020, the Multiscale Geographically Weighted Regression (MGWR) method quantifies the influence of these factors and their spatial variations. The results show the following. (1) Forestry enterprises in China exhibit persistent spatial clustering. The eastern regions have a notably higher concentration than the western regions, and new enterprises are increasingly concentrated in a few hotspot cities in the east. (2) The spatial center of forestry enterprises has steadily moved southeast. Initially, the distribution was balanced in the eastern regions, but it has become highly concentrated in the southeastern coastal areas. (3) Regarding spatial autocorrelation, regions within the northwest cold spot cluster have been disappearing entirely. The northeast and southwest hotspot clusters have shrunk significantly, while the southeast hotspot cluster has remained large. (4) Permanent population size and green land area are the most strongly positively correlated with forestry enterprise distribution. Patent authorizations, orchard area, and forest land area also show positive effects. In contrast, road density and total import/export volume are negatively correlated with the number of forestry enterprises. This aligns with the structure of China’s forestry industry, which relies more on natural resources and market demand than on economic development level or financial environment. (5) The factors influencing forestry enterprise distribution show significant spatial variation, driven by regional factors such as resources, economy, and population. These factors ultimately determine the spatiotemporal distribution of forestry enterprises. This study provides data-driven insights to optimize the distribution of forestry industries and formulate more effective ecological protection policies.

Accurate and efficient estimation of leaf chlorophyll content (LCC) is essential for crop physiological monitoring and precision agriculture. Current remote sensing approaches for LCC estimation mainly rely on machine learning and deep learning methods, which exhibit strong nonlinear modelling capabilities but require sufficient and representative samples. Radiative transfer models (RTMs) can simulate canopy spectral response based on physical mechanisms and generate large-scale datasets, thereby overcoming the limitations of purely data-driven approaches. However, their applications under diverse environmental conditions remain challenging due to uncertainties caused by model parameterization and discrepancies between simulated and observed data. This study proposed a hybrid framework, PROSAIL-DNN-TL, integrating the PROSAIL RTM with full-network fine-tuned transfer learning for winter wheat LCC estimation. First, 85,680 simulated samples generated by PROSAIL were used to pre-train a deep neural network (DNN), and field observations (n = 270) were subsequently used for full-network fine-tuning. A conventional DNN model trained only with field data and machine learning models based on different feature selection strategies were established for comparison. The results showed that: (1) UVE-selected bands were distributed across visible, red-edge, and near-infrared regions; CARS retained more bands in the visible region, whereas SPA mainly selected near-infrared bands; (2) RR and SVR achieved higher accuracy than KNN, with the full-spectrum and CARS feature sets showing superior performance and SPA showing the lowest accuracy; (3) PROSAIL-DNN-TL achieved reliable LCC estimation performance, with an average test R² of 0.79 across 500 repeated experiments, and showed improved performance compared with conventional machine learning models; (4) PROSAIL-DNN-TL achieved reliable predictive performance when evaluated using datasets collected during the 2024 and 2025 growing seasons, with R² values of 0.75 and 0.92, respectively. Overall, the proposed hybrid framework integrates physical knowledge from RTMs with data-driven modelling through transfer learning, providing a potential approach for crop chlorophyll monitoring. However, the SPAD-to-LCC conversion used in this study was not specifically calibrated for the instrument and crop, and therefore, the absolute LCC values should be interpreted with caution.