Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that threaten rice crops. This paper aims to provide insights into current and future trends in remote sensing for rice crop monitoring. First, we expound the mechanism of remote sensing monitoring of rice diseases and pests and introduce the applications of different commonly data sources (hyperspectral data, multispectral data, thermal infrared data, fluorescence, and multi-source data fusion) in remote sensing monitoring of rice diseases and pests. Secondly, we summarize current methods for monitoring rice diseases and pests, including statistical discriminant type, machine learning, and deep learning algorithm. Finally, we provide a general framework to facilitate the monitoring of rice diseases or pests, which provides ideas and technical guidance for remote sensing monitoring of unknown diseases and pests, and we point out the challenges and future development directions of rice disease and pest remote sensing monitoring. This work provides new ideas and references for the subsequent monitoring of rice diseases and pests using remote sensing.

Currently, it is a great challenge for remote sensing technology to accurately map mangrove forests owing to periodic inundation. A submerged mangrove recognition index (SMRI) using two high- and low-tide images was recently proposed to remove the influence of tides and identify mangrove forests. However, when the tidal height of the selected low-tide image is not at the lowest tidal level, the corresponding SMRI does not function well, which results in mangrove forests below the low tidal height being undetected. Furthermore, Spartina alterniflora Loisel (S. alterniflora) was introduced to China in 1979 and rapidly spread to become the most serious invasive plant along the Chinese coastline. The current SMRI has failed to distinguish S. alterniflora from submerged mangrove forests because of their similar spectral signatures. In this study, an SMRI-based mangrove forest mapping method was developed using the time series of Sentinel-2 images to mitigate the two aforementioned issues. In the proposed method, quantile synthesis was applied to the time series of Sentinel-2 images to generate a lowest-tide synthetic image for creating SMRI to identify submerged mangrove forests. Unsubmerged mangrove forests were classified using a support vector machine, and a preliminary mangrove forest map was created by merging them. In addition, S. alterniflora was distinguished from the mangrove forests by analyzing their phenological differences. Finally, mangrove forest mapping was performed by masking S. alterniflora. The proposed method was applied to the entire coastline of the Guangxi Province, China. The results showed that it can reliably and accurately identify submerged mangrove forests derived from SMRI by synthesizing low- and high-tide images using quantile synthesis, and the differentiation of S. alterniflora using phenological differences results in more accurate mangrove mapping. This work helps to improve the accuracy of mangrove forest mapping using SMRI and its feasibility for coastal wetland monitoring. It also provides data for sustainable management, ecological protection, and restoration of vegetation in coastal zones.

Rice blast is a highly damaging disease that greatly impacts both the quality and yield of rice. Timely identification and monitoring of this disease are essential for effective agricultural management and for ensuring optimal crop performance. The spectral vegetation index has been widely used in the identification of crop diseases. However, a limitation of these indices is that they cannot identify diseases at different scales. This study aimed to address these issues by developing the rice blast-specific hyperspectral Geometry Ratio Vegetation Index (GRVI_RB) for monitoring rice blast disease at the leaf and canopy scales. The sensitive bands for identifying rice blast disease were 688 nm, 756 nm, and 1466 nm using the successive projection algorithm. Based on these three sensitive bands and the spectral response mechanism of rice blast, the GRVI_RB was designed. GRVI_RB demonstrated high classification accuracy using SVM (support vector machine) and LDA (Linear Discriminant Analysis) models in leaf-scale and canopy-scale datasets from 2020 and 2021, surpassing the current vegetation indices of rice blast detection. It is demonstrated that the GRVI_RB has excellent robustness and universality for rice blast detection from leaf to canopy scales in different years. Additionally, the research suggests that the new hyperspectral vegetation index can serve as a valuable reference for studies conducted at both unmanned aerial vehicle and satellite scales.

Accurate mapping of mangrove forests is crucial for understanding their ecosystem function and developing effective management policies. However, the absence of an operational multi-feature fusion approach and an ensemble classification system restricts the achievement of this goal. This study aims to develop an object-oriented multi-feature ensemble classification scheme (OMEC). First, an enhanced mangrove spectral index (EMSI) is established by analyzing the spectral reflectance differences between mangrove forests and other land cover types. Sentinel-2 images are segmented into objects using the multi-resolution segmentation method. Then, spectral, textural, and geometric features are extracted, and these features (including EMSI) are inputted into the nearest neighbor classifier to implement mangrove classification. The experiment was conducted in three typical mangrove areas in China using Sentinle-2 images. The results demonstrate that EMSI exhibits good spectral separability for mangroves and performs well in the ensemble classification scheme. The overall accuracy of mangrove classification exceeds 90%, with a Kappa coefficient greater than 0.88. The object-oriented multi-feature ensemble classification scheme significantly improves accuracy and exhibits excellent performance. The method enhances the accuracy of mangrove classification, enriches the approach to mangrove remote sensing interpretation, and offers data support and scientific references for the restoration, management, and protection of coastal wetlands.

Accurate assessment of the skid resistance of asphalt pavement is crucial for traffic safety. However, traditional detection methods suffer from inefficiency, high costs, and limited coverage, making them inadequate for large-scale road network monitoring. This paper proposes a method for assessing the skid resistance of asphalt pavements based on hyperspectral remote sensing. First, hyperspectral data of asphalt pavements with different aging degrees were acquired through ground-based spectral measurements, and feature bands correlated with the aging process were selected using the successive projections algorithm. Based on these results, the feature bands were applied to unmanned aerial vehicle (UAV)-based hyperspectral images to construct an aging spectral index capable of characterizing pavement aging conditions. Combined with the decision tree method, assessment of pavement aging conditions was achieved, with an overall accuracy of 96.52% and a Kappa coefficient of 0.948. Finally, a quantitative relationship model between the aging spectral index and skid resistance was established using regression analysis, with the coefficient of determination (R²) and root mean square error (RMSE) of the model being 0.869 and 3.26, respectively. The proposed method enables efficient, contactless and large-scale assessment of pavement skid resistance, expanding the application of UAV remote sensing technology in road maintenance.

Vegetation optical depth (VOD), a microwave-based vegetation index for assessing crop canopy water content and biomass, is becoming increasingly crucial for effective crop and irrigation management as well as yield optimization. Currently, VOD is primarily retrieved from passive microwave data with low resolution, which does not meet the needs for monitoring crop growth dynamics at finer scales. A high-resolution Sentinel-1 VOD time series, was developed based on the water cloud model (WCM) coupled with the TU-Wien (Vienna University of Technology, TU-Wien) change detection algorithm over Guangxi, China, during 2018–2020. The main challenge in retrieving a high-resolution VOD dataset from Sentinel-1 is the lack of high-resolution soil moisture observations, which impedes the precise correction of soil scattering component interference in the WCM. To address this issue, the corrected soil scattering components from the TU-Wien algorithm are used as input parameters for the WCM to construct the VOD time series. Consequently, the TU-Wien algorithm provides a 10-m resolution surface soil moisture (SSM) observation dataset, serving as a bridge to retrieve high-resolution VOD. The results show that the S1 SSM product shows strong agreement with the CLDAS-V data in southern Guangxi, indicating satisfactory performance in this region during 2019, with most pixels exhibiting R values greater than 0.50. Across most areas of the study region, the correlation between VOD and NDVI is positive, with R values exceeding 0.6. The high-resolution VOD we retrieved can be effectively used to monitor crop water stress, growth stages, and yield conditions with greater spatial detail, supporting precision agriculture and contributing to regional food security assessments.