A in situ hyperspectral dataset containing multiple growth stages over multiple growing seasons was used to build paddy rice leaf area index (LAI) estimation models with a special focus on the effects of paddy rice growth stage development. The univariate regression method applied to the vegetation index (VI), the traditional multivariate calibration method of partial least squares regression (PLSR), and modern machine learning methods such as support vector regression (SVR), random forests (RF), and artificial neural networks (ANN) based on the original and first-derivative hyperspectral data were evaluated in this study for paddy rice LAI estimation. All the models were built on the whole growing season and on each separate vegetative, reproductive and ripening growth stage of paddy rice separately. To ensure a fair comparison, the models of the whole growing season were also validated on data for each separate growth stage of the standalone validation dataset. Moreover, the optimal band pairs for calculating narrowband difference vegetative index (DVI), normalized difference vegetation index (NDVI) and simple ratio vegetation index (SR) were determined for the whole growing season and for each separate growth stage separately. The results showed that for both the whole growing season and for each single growth stage, the red-edge and near-infrared band pairs are optimal for formulating the narrowband DVI, NDVI and SR. Among the four multivariate calibration methods, SVR and RF yielded more accurate results than the other two methods. The SVR and RF models built on first-derivative spectra provided more accurate results than the corresponding models on the original spectra for both whole growing season models and separate growth stage models. Comparing the prediction accuracy based on the whole growing season revealed that the RF and SVR models showed an advantage over the VI models. However, comparing the prediction accuracy based on each growth stage separately showed that the VI models provided more accurate results for the vegetative growth stages. The SVR and RF models provided more accurate results for the ripening growth stage. However, the whole growing season RF model on first-derivative spectra could provide reasonable accuracy for each single growth stage.

Prompt monitoring of maize dwarf mosaic virus (MDMV) is critical for the prevention and control of disease and to ensure high crop yield and quality. Here, we first analyzed the spectral differences between MDMV-infected red leaves and healthy leaves and constructed a sensitive index (SI) for measurements. Next, based on the characteristic bands (R_λ) associated with leaf anthocyanins (Anth), we determined vegetation indices (VI_s) commonly used in plant physiological and biochemical parameter inversion and established a vegetation index (VI_c) by utilizing the combination of two arbitrary bands following the construction principles of NDVI, DVI, RVI, and SAVI. Furthermore, we developed classification models based on linear discriminant analysis (LDA) and support vector machine (SVM) in order to distinguish the red leaves from healthy leaves. Finally, we performed UR, MLR, PLSR, PCR, and SVM simulations on Anth based on R_λ, VI_s, VI_c, and R_λ + VI_s + VI_c and indirectly estimated the severity of MDMV infection based on the relationship between the reflection spectra and Anth. Distinct from those of the normal leaves, the spectra of red leaves showed strong reflectance characteristics at 640 nm, and SI increased with increasing Anth. Moreover, the accuracy of the two VI_c-based classification models was 100%, which is significantly higher than that of the VI_s and R_λ-based models. Among the Anth regression models, the accuracy of the MLR model based on R_λ + VI_s + VI_c was the highest (R²_c = 0.85; R²_v = 0.74). The developed models could accurately identify MDMV and estimate the severity of its infection, laying the theoretical foundation for large-scale remote sensing-based monitoring of this virus in the future.

To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR~1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS systems to obtain hyperspectral (HS) reflectance and images of canopy leaves. First, we performed principal component analysis (PCA), first-order differential (FD), and continuum removal (CR) transformations on the original ground-based spectra; commonly used spectral parameters were implemented to estimate Anth content using multiple stepwise regression (MSR), partial least squares (PLS), back-propagation neural network (BPNN), and random forest (RF) models. The spectral transformation highlighted the characteristics of spectral curves and improved the relationship between spectral reflectance and Anth, and the RF model based on the FD spectrum portrayed the best estimation accuracy (R²_c = 0.91; R²_v = 0.51). Then, the RGB (red-green-blue) gray vegetation index (VI) and the texture parameters were constructed using UAV images, and an Anth estimation model was constructed using UAV parameters. Finally, the UAV image was fused with the ground spectral data, and a multisource RS model of Anth estimation was constructed, based on PCA + UAV, FD + UAV, and CR + UAV, using MSR, PLS, BPNN, and RF methods. The RF model based on FD+UAV portrayed the best modeling and verification effect (R²_c = 0.93; R²_v = 0.76); compared with the FD-RF model, R²_c increased only slightly, but R²_v increased greatly from 0.51 to 0.76, indicating improved modeling and testing accuracy. The optimal spectral transformation for the Anth estimation of tree peony leaves was obtained, and a high-precision Anth multisource RS model was constructed. Our results can be used for the selection of ground-based HS transformation in future plant Anth estimation, and as a theoretical basis for plant growth monitoring based on ground and UAV multisource RS.

Recently, a significant number of freshwater fishing gardens have sprouted up across mainland China. These recreational facilities are an important component in promoting the upgrading of the fishing industry and rural revitalization, and they are a key component in the high-quality development of rural tourism. This paper uses fishing gardens points of interest (POI) in China as data sources and employs kernel density estimation and geographical detectors to systematically uncover the multiscale spatial distribution pattern of these gardens, as well as the factors influencing their distribution. The results show that: (1) There are 15,090 fishing gardens in inland China. The spatial distribution of Chinese fishing gardens corresponds well with the “Hu-Line”, with a greater number of gardens clustered in the southeast and few in the northwest. The density distribution exhibits a polarized pattern with multiple high-density centers. (2) The number of fishing gardens varies significantly across regions, with the eastern > central > western > northeastern; Guangdong has the most fishing gardens. The top five provinces have 43.4% of the total number of fishing gardens in the country. Large-scale fishing gardens are common in developed cities such as the Pearl River Delta, Beijing-Tianjin-Hebei, and the Yangtze River economic belt. (3) In natural environmental factors, land altitude and contour are negatively correlated with the distribution of fishing gardens, whereas winter temperature is positively correlated with the distribution. More than 50% of fishing gardens are located within 6 km of urban built-up areas. (4) GDP, population, and tourism revenue are the most important social development factors influencing the distribution of fishing gardens. The moderate factors are per capita income and the rate of urbanization; the weak factors are fishery output value and freshwater products production. In the discussion, suggestions on how to guide the rational layout and healthy development of the fishing garden industry in the region are put forward. We believe that these suggestions could be part of the pursuit to improve the fishing garden industrial policy in China.