Yellow rust is a worldwide disease that poses a serious threat to the safety of wheat production. Numerous studies on near-surface hyperspectral remote sensing at the leaf scale have achieved good results for disease monitoring. The next step is to monitor the disease at the field scale, which is of great significance for disease control. In our study, an unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor was used to obtain hyperspectral images at the field scale. Vegetation indices (VIs) and texture features (TFs) extracted from the UAV-based hyperspectral images and their combination were used to establish partial least-squares regression (PLSR)-based disease monitoring models in different infection periods. In addition, we resampled the original images with 1.2 cm spatial resolution to images with different spatial resolutions (3 cm, 5 cm, 7 cm, 10 cm, 15 cm, and 20 cm) to evaluate the effect of spatial resolution on disease monitoring accuracy. The findings showed that the VI-based model had the highest monitoring accuracy (R2 = 0.75) in the mid-infection period. The TF-based model could be used to monitor yellow rust at the field scale and obtained the highest R2 in the mid- and late-infection periods (0.65 and 0.82, respectively). The VI-TF-based models had the highest accuracy in each infection period and outperformed the VI-based or TF-based models. The spatial resolution had a negligible influence on the VI-based monitoring accuracy, but significantly influenced the TF-based monitoring accuracy. Furthermore, the optimal spatial resolution for monitoring yellow rust using the VI-TF-based model in each infection period was 10 cm. The findings provide a reference for accurate disease monitoring using UAV hyperspectral images.

In recent decades, rice disease co-epidemics have caused tremendous damage to crop production in both China and Southeast Asia. A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate-resolution imagery. However, the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data. The aims of this study were (1) to develop a set of normalized two-stage vegetation indices (VIs) for characterizing the progressive development of different diseases with rice; (2) to explore the performance of combined normalized two-stage VIs in partial least square discriminant analysis (PLS-DA); and (3) to map and evaluate the damage caused by rice diseases at fine spatial scales, for the first time using bi-temporal, high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution. Our findings suggest that the primary biophysical parameters caused by different disease (e.g., changes in leaf area, pigment contents, or canopy morphology) can be captured using combined normalized two-stage VIs. PLS-DA was able to classify rice diseases at a sub-field scale, with an overall accuracy of 75.62% and a Kappa value of 0.47. The approach was successfully applied during a typical co-epidemic outbreak of rice dwarf (Rice dwarf virus, RDV), rice blast (Magnaporthe oryzae), and glume blight (Phyllosticta glumarum) in Guangxi Province, China. Furthermore, our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents.

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.

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Infected areas and damage levels due to crop pest and disease have been growing seriously according to the climate change. We aim to develop an automatic system to provide national pest and disease dynamic monitoring and early forecasting products, by integrating multi-source information (Earth Observation, meteorological, ecological, entomological and plant pathological, etc.) and cutting edge research on pest and disease modelling to support decision making in the sustainable management of pest and disease. Firstly, we selected the sensitive indexes for pest and disease habitat monitoring and early forecasting, and then optimized the forecasting model's parameters to enhance its applicability in national level. Secondly, we developed an automatic system based on WebGIS platform to efficiently realize the national pest and disease dynamic habitat monitoring and early forecasting. At last, we released the pest and disease forecasting thematic maps. China's national disease wheat yellow rust (Puccinia striiformis) and national pest oriental migratory locust (Locusta migratoria manilensis (Meyen)) are taking as the experimental objects. Based on the developed system, we forecasted the infected areas of rust and locust in China in 2019, with these R-square values are higher than 0.87. This system would not only promote the efficacy of pest and disease management and prevention by improving accuracy of monitoring and forecasting, but also help to reduce the amount of chemical pesticides, which could thus guarantee food security and agriculture sustainable development in China.

The outbreak of Oriental Migratory Locust(Locusta migratoria manilensis) causes devastating disasters to agriculture. With the impact of climate changes and human activities, the distribution of locust habitat (locust habitat is the environment in which locusts live and survive) in China is constantly changing. Monitoring and extracting locust habitat are of great significance for guiding largescale agricultural production. The occurrence of the locust is closely related to their habitat. Therefore, a comprehensive analysis of habitat factors that affect locust survival is carried out to monitor locust habitat distribution. Besides, the landscape structure also affects distribution. This study explored a model for analyzing multi-temporal Landsat and MODIS images, which combined multiple habitat factors and landscape structure to analyze locust habitat. The locust habitat near North Dagang Reservoir in Tianjin is the research object. First, the habitat factors that affect locust oviposition and growth were analyzed, and vegetation coverage, land cover class, soil moisture, soil salinity, and land surface temperature were selected as five habitat factors. The weights of five habitat factors were evaluated according to the Analytic Hierarchy Process (AHP) model. Then, considering the impact of landscape structure on locust habitat, a moving-window was used to correlate locust habitat factors at pixel scale with locust habitat at patch scale. Finally, the distribution map of the locust habitat at patch scale was generated. The Analytic Hierarchy Process(AHP) was used to compare and test the results. Our research shows that the Patch based -Analytic Hierarchy Process (PB-AHP) can monitor locust habitat. The overall accuracy reached 88%, which is 10% higher than the result based on the Analytic Hierarchy Process(AHP). These results show that the Patch based -Analytic Hierarchy Process (PB-AHP) model has strong robustness and generalization ability in identifying locust habitat and can provide scientific guidance for locust monitoring and control. INDEX TERMSlocust habitat, landscape, Patch based -Analytic Hierarchy Process (PB-AHP), remote sensing I.INTRODUCTION The Oriental Migratory Locust (Locusta migratoria manilensis) is a destructive agricultural pest in China [1][2].Locust is a major threat to crops such as wheat, maize, rice, and has caused massive economic damage [3][4]. The outbreak of locust plague could have a significant and negative impact on food security, ecological security, and social stability [5]. In China, the total acreage impacted by locust changed little from 2003 to 2018, at around 667 thousand hectares. In recent years, China has made remarkable gains in controlling locust plague. However, with additional impacts from global warming, drought, environmental changes, and human activities, new locust habitat has been created that does not have adequate monitoring by plant protection departments, which means that sudden locust plagues in the unexpected location are a

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The application of chemical harvest aids to defoliate leaves and ripen bolls plays a significant role in the once-over machine harvest of cotton (Gossypium hirsutum L.) fields. The boll opening rate (BOR) is a key indicator for the determination of harvest aid spraying times. However, the most commonly used method to determine BOR is manual investigation, which is subjective and cannot have a holistic judgment of the entire area. Remote sensing can be employed to overcome these limitations, due to a wide field of vision, acceptably spatial and temporal resolution, and rich spectral information beyond the perception of the human eye. The reflectance of open cotton bolls is relatively high in the visible and near-infrared bands. High reflectance of open bolls has a great influence on the reflectance of the mixed pixels on remote sensing imagery. Therefore, it is an effective method to detect boll opening status by constructing vegetation indices with the sensitive spectral bands of imagery. In this study, we proposed two new vegetation indices based on Sentinel-2 remote sensing data, namely, the boll area ratio index (BARI) and the boll opening rate index (BORI), in order to estimate the boll opening status on a regional scale. The proposed indices were strongly correlated with the boll area ratio (BAR) and BOR. In particular, BARI exhibited the most accurate and robust performance with BAR in the prediction (R2 = 0.754, RMSE = 2.56%) and validation (R2 = 0.706, RMSE = 5.00%) among all the indices, including published indices we chose. Furthermore, when comparing to all other indices, BORI demonstrated the best and satisfactory estimation with BOR in the prediction (R2 = 0.675, RMSE = 7.96%) and validation (R2 = 0.616, RMSE = 2.79%). Meanwhile, an exponential growth relationship between BOR and BAR was identified, and the underlying mechanisms behind this phenomenon were discussed. Overall, through our study, we provided convenient and accurate vegetation indices for the investigation of boll opening status in a cotton-producing area by accessible and free Sentinel-2 imagery.