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[Display omitted] • A sequentially integrated algorithm for crop lodging identification with low data acquisition costs, high automation and high accuracy. • An automatic method to extract lodged and non-lodged samples based on the isolation forest algorithm. • The automatic identification of crop lodging achieved an overall accuracy of 78%. • Exploring the practicality of Sentinel-1 and Sentinel-2 data for crop lodging identification. • Providing support for agricultural disaster emergency management and agricultural insurance loss assessment. Rapid identification of crop lodging has implications for optimizing disaster prevention and mitigation measures. This study proposes a sequentially integrated algorithm for crop lodging identification, which first automatically extracts lodged and non-lodged samples based on the isolation forest algorithm from the screened features dataset and then uses a supervised classifier for lodging identification. Taking the crop lodging disaster caused by the super typhoon "Maysak" in Bohetai Township as an example, we calculated 162 feature indices from Sentinel-1 and Sentinel-2 satellite data pre- and post-disaster and screened the optimal features. Then, the abnormal scores of the cultivated-land pixels were calculated using the isolation forest algorithm. After that, we calculated the coefficient of variation (CV) of the abnormal scores of each group, extracted the lodged and non-lodged samples based on the cumulative CV contribution rate, and input samples into three supervised classifiers (random forest, maximum likelihood, and support vector machine) to identify lodged crop pixels. The results showed that feature screening increased the identification accuracy of crop lodging by more than 10% and difference features of short-wave infrared (SWIR) and red edge bands contributed significantly to the identification. The accuracy of crop lodging automatic identification was verified using samples obtained from visual interpretation and digitization of UAV images as reference data and the overall accuracy reached 78%. The proposed method can simultaneously recognize lodging of different crop types without the need to select lodged samples and extract lodged ranges for each crop. With low data acquisition costs and high automation, the proposed method has promising applications in agricultural disaster emergency management and agricultural insurance loss assessment. [ABSTRACT FROM AUTHOR]

• The radar time series of maize populations was constructed. • The internal relationship between Synthetic Aperture Radar (SAR) features and lodging grades was explored. • The lodging severities of maize were monitored on the regional scale by Time-weighted Dynamic Time Warping (TwDTW). Lodging is one of the most important stresses in maize growth, which affects the yield, quality and mechanical harvesting ability of maize. Monitoring the lodging range and severity of large-scale maize by satellite remote sensing technology can support agricultural insurance claims, planting structure adjustment and field management. The purpose of this study is to investigate the application of Synthetic Aperture Radar (SAR) remote sensing images in monitoring the severity of maize lodging at the county scale. A time series feature dataset was obtained from multi-temporal Sentinel-1 SAR images before and after lodging. The sensitive features of maize lodging severity were screened by Jeffreys-Matusita (J-M) distance. The study compared the performance of three different methods for monitoring maize lodging severity, including Time-weighted Dynamic Time Warping (TwDTW), random forest (RF) and minimum distance (MinD). The results showed that all time phases of SAR images selected in the study had an important contribution to the differentiation of maize lodging severities. Among all features, the VH (Vertical Send and Horizontal Receive) time series features composed of five time-phase had better discrimination for different maize lodging severities. The accuracy of extracting maize lodging severities based on TwDTW was 76% and the Kappa coefficient was 0.70, which was higher than RF and MinD. Thus, by utilizing the multi-time phase features of Sentinel-1, we can effectively monitor the grades of maize lodging at the county scale. [ABSTRACT FROM AUTHOR]

• A lodging area detection model performance evaluation method was proposed. • Three indicators were introduced to assess model comprehensive performance. • Challenges were addressed in the presence of spatial and temporal heterogeneity. Fast and accurate assessment of wheat lodging holds significant importance for disaster prevention and agricultural insurance claim settlement. However, lodging detection is often influenced by the spatial and temporal heterogeneity of fields, leading to inconsistent lodging features in images captured at different times and locations. This inconsistency poses challenges for evaluating model performance and obtaining consistent results. In this study, we proposed a comprehensive lodging area detection model performance evaluation method for multi-year and multi-phenological periods. Three evaluation indicators—accuracy, potential, and stability—were introduced to assess model performance from different perspectives. Additionally, a weighted method is employed to combine the results of multiple experiments. To evaluate the performance of lodging area segmentation in wheat fields, we selected three feature adaptive models (CBAM-unet, SE-unet, and Swin-transformer) along with the Unet model. The experimental results are as follows: (1) Swin-transformer achieved the highest weighted average accuracy (Acc cp) of 83.53% among the four models evaluated. (2) Swin-transformer exhibited the highest upper limit of segmentation accuracy, reaching 97.98%. However, its average accuracy in the prediction experiment was 85% of the upper limit, suggesting potential for optimization. (3) CBAM-unet demonstrated the lowest overall weighted segmentation accuracy variance (35.92) compared to the other three models, indicating higher stability. Based on the experimental results it can be seen that our proposed method and model can address the challenge of evaluating lodging area detection models under spatial and temporal heterogeneity. [ABSTRACT FROM AUTHOR]

• Lodging severity of maize was described by canopy height and plant lodging angle. • Three methods of reconstructing the canopy structure of lodging maize were compared. • The lodging angle inversion model of crop was established. • UAV-LiDAR performed best for monitoring maize lodging severity. Lodging is one of the common stresses in maize production, which affects the benefit of farmers and impacts food security. Rapid monitoring of crop lodging can support loss assessment and agricultural insurance claims. Unmanned aerial vehicle (UAV) technology has been widely used in maize lodging monitoring in recent years. Compared with remote sensing monitoring based on spectral and texture features, the method based on crop three-dimensional structure data can accurately and quantitatively monitor the severity of maize lodging. The purpose of this study was to compare the effectiveness of UAV-LiDAR, backpack LiDAR, and 3D model reconstruction of UAV digital image (UAV-DIM) data on monitoring maize lodging. The canopy height model (CHM) was constructed using three kinds of data in the same conditions, and the canopy height of different lodging types of maize was extracted. The angle inversion model was proposed and the lodging angle of maize in the experimental plots was calculated. The results showed that UAV-LiDAR had the best performance in monitoring maize lodging. The determination coefficient (R2) of the canopy height was 0.956, while the RMSE was 0.097 m and NRMSE was 0.067. The correlation coefficient (ρ) of the lodging angle was about 0.9. It is the best method to monitor the lodging of maize based on three-dimensional structural information. The R2 , RMSE , and NRMSE of canopy height by the UAV-DIM method were 0.790, 0.482 m, and 0.336, ρ ≥ 0.8, respectively. It shows that the accuracy of maize lodging monitoring by this method is weaker than those of the UAV-LiDAR and backpack LiDAR methods. However, considering the advantages of low cost and high efficiency of data acquisition, the UAV-DIM method still has a wide application prospect in crop lodging monitoring. [ABSTRACT FROM AUTHOR]

• A deep leaning network was designed for farmland segmentation in satellite images. • Two-part framework of pseudo label generation and segmentation model fusion. • New block-wise metrics were introduced for parcel-level evaluation. • Experiments show significant performance improvements at the parcel level. The participation of insured smallholders and involvement of related requirements have been emerging and increasing with the popularization and promotion of agricultural insurance. Compared with traditional field surveys, remote sensing and deep learning technologies, with their rapid development in recent years, have provided an automatic and effective means for underwriting control in the agricultural insurance industry. Moreover, "smallholder-wise underwriting" is intensively expected by the local government and insurance companies to ensure precise claim settlements. In this process, farmland segmentation is a crucial and fundamental step for smallholder farmland claim settlements. However, segmentation of smallholder farmland parcels from satellite images remains a challenge, as the ridges and roads between farmland parcels are usually narrow and display confusing characteristics with their neighboring farmlands. This challenge causes undersegmentation on the farmland boundaries, leading to inaccurate and incomplete farmland parcel recognition. In this study, we aim to solve this problem by proposing a novel Deep Edge Enhancement Semantic Segmentation Network to refine parcels' boundary segmentation and improve the closure level of the farmland segmentation. We designed a framework for farmland edge enhancement through pseudo road label generation and model fusion. The proposed network aggregates feature extraction for ridge and road segmentation to improve its performance on farmland parcel recognition. Furthermore, we found that the commonly used image segmentation evaluation metric, such as pixel-wise–based mean Intersection over Union, cannot objectively reflect the effectiveness of the smallholder farmland segmentation. Therefore, we proposed two block-wise farmland evaluation metrics that are consistent with the practical evaluation rules and requirements of farmland segmentation at the parcel level. We implemented experiments in the study area of Jiaxiang County in northern China using the GaoFen-1 4-band multispectral images (red, green, blue, and near-infrared) at a spatial resolution of 2 m. Experimental results demonstrated the effectiveness of our method, which outperformed the baseline DeepLabv3+ network. [ABSTRACT FROM AUTHOR]

• LodgeNet was used to obtain the information of different lodging degrees in rice. • LodgeNet achieved 97.30% accuracy in rice lodging semantic segmentation. • LodgeNet has a better effect than other networks on a small data set. Rice lodging not only causes difficulty in harvest operations, but also drastically reduces yield. Therefore, it is very important to identify rice lodging efficiently. For unmanned aerial vehicle (UAV) remote sensing images, this paper combines the advantages of dense block, DenseNet, attention mechanism, and jump connection on the basis of U-Net network to propose an end-to-end, pixel-to-pixel semantic segmentation method to identify rice lodging. And the method can process the input multi-band image. The accuracy of the model proposed in this paper was 97.30% on rice lodging images, which performed better than other comparison methods in the test. At the same time, it has good effect on small sample data set. The results show that it is feasible to use the improved U-Net network model to extract the lodging area of rice, which provide a useful reference for rice breeding and agricultural insurance claims. [ABSTRACT FROM AUTHOR]