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• The comprehensive lodging evaluation index (CLEI) of maize was constructed. • The CLEI was calculated by using the fuzzy comprehensive evaluation (FCE) method. • The changes of the PH, LA, SPAD and CLEI in the field was explored. • The maize lodging severity in plot were evaluated according to the CLEI model. Lodging significantly reduces crop yield and grain quality. Timely and accurately monitoring crop lodging severity to help farmers to adjust the field management and settle reasonable insurance claim. The purpose of this paper is to effectively monitor maize lodging severity using unmanned aerial vehicle (UAV) with a multi-spectral camera. Considering the plant height (PH), lodging angle (LA) and SPAD, each agronomic trait was transformed into a membership matrix by giving each factor a weight. The comprehensive superiority of each evaluation result based on agronomic trait (PH, LA, SPAD) was calculated by using the fuzzy comprehensive evaluation (FCE) method, and the comprehensive lodging evaluation index (CLEI) was constructed. The relationships between the spectral reflectance (SR), texture feature (TF), vegetation index (VI) and PH, LA, SPAD, CLEI were established. The CLEI was calculated and mapped by using the sensitive features derived from UAV-based multi-spectral imagery. According to the distribution of CLEI, the research area was divided into five grades, including non-lodging (NL), light lodging (LL), moderate lodging (ML), severe lodging (SL) and very severe lodging (VSL). The results showed that among the CLEI models constructed by single feature, VI performed best and the R2 of cross-validation was 0.66 which was higher than agronomic trait models (the R2 of PH, LA, and SPAD models were 0.56, 0.58, 0.41, respectively). The R2 and nRMSE of cross-validation of the CLEI model constructed by the top three sensitive features (CIre, NDRE, Re_Mea) were 0.80 and 23.78%, while the R2 of the PH, LA, and SPAD models were 0.74, 0.71, 0.46, respectively. The accuracy of the new comprehensive index (CLEI) model was better than that of single agronomic trait (PH, LA and SPAD) model. The CLEI proposed in this paper using UAV-based multi-spectral imagery can effectively monitor the lodging severity of maize. [ABSTRACT FROM AUTHOR]

• The canopy chlorophyll density (CCD) of maize populations under lodging stress was constructed. • The internal relationship between CCD and canopy spectral feature under different lodging grades stress was explored. • The monitoring of maize lodging grade on the plot scale was realized. Lodging causes severe decreases in crop yield, reduces grain quality, and increases the difficulty of mechanical harvesting. Obtaining the spatial distribution information of maize lodging grades in a timely and accurate manner is essential for yield loss assessment, post-stress management, and insurance claims settlements. The purpose of this study is to explore the ability of unmanned aerial vehicle (UAV) imaging technology to monitor maize lodging stress. With the support of maize lodging control experiments, the canopy chlorophyll density (CCD) of maize populations under stress from different lodging grades was used as the characterization index. The responses between hyperspectral characteristic parameters and CCD with different lodging grade stresses were analyzed. The monitoring model of the maize CCD under lodging stress was constructed using the sensitive characteristic parameters of original canopy spectra (OCS), first-order differential (FOD), wavelet coefficient (WC), and vegetation index (VI). The results showed that the reflectance of the stalk was significantly higher than that of the leaf in hyperspectral imagery, which was the main reason for the change in the original canopy spectra under lodging stress. The original canopy spectral reflectance increased with the severity of lodging stress. The accuracy of the CCD model was VI > WC > FOD > OCS (R2 = 0.63, 0.61, 0.59, 0.57, respectively), in which the accuracy of VI was the highest (R2 = 0.63, R M S E = 0.36 g/m3). This is because CCD considers not only the change in canopy spatial structure after maize lodging, but also the change in physiological activity of maize plants under lodging stress. The maize lodging grades were evaluated according to the CCD model based on the UAV hyperspectral imagery. [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]

Unharvested upright maize straw is highly susceptible to burning in winter or in spring of the following year, contributing significantly to air pollution. The rapid and accurate acquisition of the spatial distribution of upright maize straw holds great importance for agricultural and environmental protection departments. This study introduces a new approach that focuses on extracting upright maize straw using Sentinel-2 satellite images. Initially, the Sentinel-2 images from the Botou and Hejian regions underwent preprocessing. To minimize interference during straw extraction, the normalized difference vegetation index (NDVI) was utilized to extract and eliminate built-up land and woodland areas. Subsequently, utilizing the spectral characteristics of maize straw across various bands including short-wave infrared, red edge, and near-infrared, three new indices were proposed and computed: the accumulation infrared straw index (AIRSI), the product near-infrared straw index (PNISI), and the normalized difference short-wave infrared straw index (NDSSI). Finally, the decision tree classification method was implemented to establish classification rules and extract the spatial distribution of upright maize straw. The study results revealed that the proposed new straw indices achieved an extraction accuracy of over 90% for upright maize straw. This new approach effectively enhances the identification accuracy of upright maize straw, providing valuable support for scientific supervision, comprehensive utilization, and prevention and control measures against maize straw burning. [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]