Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has proven to be a rapid, nondestructive, and effective technique for predicting soil properties in general and potassium, phosphorous, and organic matter in particular. However, the successful estimation of soil nutrient content by visible and near-infrared (Vis-NIR) reflectance spectroscopy depends on proper calibration methods (including preprocessing transformation methods and multivariate methods for regression analysis) and the selection of appropriate variable selection techniques. In this study, raw spectrum and 13 preprocessing transformations combined with 2 variable selection methods (competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA)) and 2 regression algorithms (support vector machine (SVM) and partial least squares regression (PLSR)), for a total of 56 calibration methods, were investigated for modeling and predicting the above three soil nutrients using hyperspectral Vis-NIR data (400–2450 nm). The results show that first-order derivatives based on logarithmic and inverse transformations (FD-LGRs) can provide better predictions of soil available potassium and phosphorous, and the best form of soil organic matter transformation is SG+MSC. CARS was superior to the SPA in selecting effective variables, and the PLSR model outperformed the SVM models. The best estimation accuracies (R², RMSE) for soil available potassium, phosphorous, and organic matter were 0.7532, 32.3090 mg/kg; 0.7440, 6.6910 mg/kg; and 0.9009, 3.2103 g/kg, respectively, and their corresponding calibration methods were (FD-LGR)/SPA/PLSR, (FD-LGR)/SPA/PLSR, and SG+MSC/CARS/SVM, respectively. Overall, for the prediction of the soil nutrient content, organic matter was superior to available phosphorous, followed by available potassium. It was concluded that the application of hyperspectral images (Vis-NIR data) was an efficient method for mapping and monitoring soil nutrients at the regional scale, thus contributing to the development of precision agriculture.

The first fall armyworm (FAW; Spodoptera frugiperda) attack in Yunnan, China, occurred in January 2019. Because FAW lacks diapause ability, its population outbreaks largely depend on environmental conditions experienced during the overwinter months. Thus, there is an urgent need to make short-term predictions regarding the potential overwintering distribution of FAW to prevent outbreaks. In this study, we selected the MaxEnt model with the optimal parameter combination to predict the potential overwintering distribution of FAW in Yunnan. Remote sensing data were used in the prediction to provide real-time surface conditions. The results predict variation in the severity and geographic distribution of suitability. The high potential distribution shows a concentration in southwestern Yunnan that suitability continues to increase from January to March, gradually extending to eastern Yunnan and a small part of the northern areas. The monthly independent contributions of meteorological, vegetation, and soil factors were 30.6%, 16.5%, and 3.4%, respectively, indicating that the suitability of conditions for FAW was not solely dominated by the weather and that ground surface conditions also played a decisive role. These results provide a basis for the precise prevention and control of fall armyworms by guiding management and decision-making and may facilitate meaningful reductions in pesticide application.

Grasshoppers can greatly interfere with agriculture and husbandry, and they will breed and grow rapidly in suitable habitats. Therefore, it is necessary to extract the distribution of the grasshopper potential habitat (GPH), analyze the spatial-temporal characteristics of the GPH, and detect the different effects of key environmental factors in the meadow and typical steppe. To achieve the goal, this study took the two steppe types of Xilingol (the Inner Mongolia Autonomous Region of China) as the research object and coupled them with the MaxEnt and multisource remote sensing data to establish a model. First, the environmental factors, including meteorological, vegetation, topographic, and soil factors, that affect the developmental stages of grasshoppers were obtained. Secondly, the GPH associated with meadow and typical steppes from 2018 to 2022 were extracted based on the MaxEnt model. Then, the spatial-temporal characteristics of the GPHs were analyzed. Finally, the effects of the habitat factors in two steppe types were explored. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. Additionally, most areas in the town of Gaorihan, Honggeergaole, Jirengaole, as well as the border of Wulanhalage and Haoretugaole became more suitable for grasshoppers from 2018 to 2022. This paper also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. The slope and precipitation in the egg stage played more important roles in the typical steppe, whereas the aspect had a greater contribution to the meadow steppe. These findings can provide a methodical guide for grasshopper control and management and for further ensuring the security of agriculture and husbandry.

The fall armyworm (Spodoptera frugiperda) (J. E. Smith) is a widespread, polyphagous, and highly destructive agricultural pest. Global climate change may facilitate its spread to new suitable areas, thereby increasing threats to host plants. Consequently, predicting the potential suitable distribution for the fall armyworm and its host plants under current and future climate scenarios is crucial for assessing its outbreak risks and formulating control strategies. This study, based on remote sensing assimilation data and plant protection survey data, utilized machine learning methods (RF, CatBoost, XGBoost, LightGBM) to construct potential distribution prediction models for the fall armyworm and its 120 host plants. Hyperparameter methods and stacking ensemble method (SEL) were introduced to optimize the models. The results showed that SEL demonstrated optimal performance in predicting the suitable distribution for the fall armyworm, with an AUC of 0.971 ± 0.012 and a TSS of 0.824 ± 0.047. Additionally, LightGBM and SEL showed optimal performance in predicting the suitable distribution for 47 and 30 host plants, respectively. Overlay analysis suggests that the overlap areas and interaction links between the suitable areas for the fall armyworm and its host plants will generally increase in the future, with the most significant rise under the RCP8.5 climate scenario, indicating that the threat to host plants will further intensify due to climate change. The findings of this study provide data support for planning and implementing global and intercontinental long-term pest management measures aimed at mitigating the impact of the fall armyworm on global food production.

The fall armyworm (FAW) (Spodoptera frugiperda) (J. E. Smith) is a migratory pest that lacks diapause and has raised widespread concern in recent years due to its global dispersal and infestation. Seasonal environmental changes lead to its large-scale seasonal activities, and quantitative simulations of its dispersal patterns and spatiotemporal distribution facilitate integrated pest management. Based on remote sensing data and meteorological assimilation products, we constructed a mechanistic model of the dynamic distribution of FAW (FAW-DDM) by integrating weather-driven flight of FAW with host plant phenology and environmental suitability. The potential distribution of FAW in China from February to August 2020 was simulated. The results showed a significant linear relationship between the dates of the first simulated invasion and the first observed invasion of FAW in 125 cities (R² = 0.623; p < 0.001). From February to April, FAW was distributed in the Southwestern and Southern Mountain maize regions mainly due to environmental influences. From May to June, FAW spread rapidly, and reached the Huanghuaihai and North China maize regions between June to August. Our results can help in developing pest prevention and control strategies with data on specific times and locations, reducing the impact of FAW on food security.

The Oriental migratory locust is a destructive agricultural pest in China. Large-scale locust area (the area possessing suitable breeding habitat for locusts and has locust infestation) extraction and its evolution analysis are essential for locust ecological control. Existing methods seldom consider the spatial differences in the locust development and habitat landscape structures in large areas. To analyze these effects, our study proposed a landscape-based habitat suitability model (LHS model) for large-scale locust area extraction based on remote sensing data, taking the middle and lower reaches of the Yellow River (MLYR) as an example. Firstly, the DD model was used to simulate locust development and obtain habitat factors of the corresponding dates; secondly, the patch distribution of different land cover classes and their adjacent landscape characteristics were analyzed to determine the landscape-based factors memberships; finally, the habitat suitability index was calculated by combining the factors memberships and weights to extract the locust area. Compared with the patch-based model using moving windows (patch based-analytic hierarchy process model, R² = 0.77), the LHS model accuracy improved significantly (R² = 0.83). Our results showed that the LHS model has a better application prospect in large-scale locust area extraction. By analyzing the locust areas evolution along the MLYR extracted using the LHS model, we found human activities were the main factors affecting the locust areas evolution from 2016 to 2020, including: (1) planting the plants that locusts do not like and urbanization caused the decrease of the locust area; (2) the wetland protection policies may cause the increase of the locust area. The model and research results help locust control and prevention to realize the sustainable development of agriculture.