Shangrong Wu

The abrupt change of canopy structure at the flowering stage causes a long-standing monitoring blind zone for SAR-based inversion of winter rapeseed. This study proposed a feature-based inversion method for the Total Photosynthetic Area Index (TPAI) of winter rapeseed, in which a multi-dimensional spatiotemporal feature framework was constructed by integrating radar-derived features such as polarization interactions and spatial texture with acquisition-date-derived phenological priors including day-of-year harmonics and normalized z-score features, and a regularized linear Ridge model was selected from six machine-learning candidates. Evaluated under a spatial five-fold cross-validation protocol with geographically disjoint validation folds, the model achieved an overall R² of 0.615 and an RMSE of 1.033 m² m⁻², against an R² of 0.584 for a baseline encoding the growth calendar alone, indicating only a modest overall gain from the radar-derived features; the radar-derived features nevertheless delivered their largest within-stage improvement over this baseline at the flowering stage, suggesting that they can partly alleviate—though not resolve—the sensitivity loss of conventional single-polarization features during flowering. Error magnitudes were insensitive to systematic bias of up to ±40% in the SAI scaling assumption, and full-coverage TPAI mapping across five phenological stages, produced from seam-corrected Sentinel-1 mosaics, matched the observed growth trajectory. Given that the SAI was estimated indirectly and the analysis relied on a single growing season at a single study area, these results should be regarded as site- and season-specific. The framework therefore shows promise as a spatially validated, feature-based pathway toward TPAI monitoring of winter rapeseed in cloudy-rainy regions, with further validation across multiple seasons and regions required before operational application.

The canopy parameters play a critical role in determining crop productivity, resource use efficiency, and adaptation to environmental conditions. Traditional crop canopy parameters predominantly focus on measuring Leaf Area Index (LAI) and neglect other canopy components, leading to substantial uncertainty of crop yield estimation by canopy parameter. To fill this gap, a novel rape crop parameter—Canopy Area Index (CAI)—was proposed in this study. CAI accounts for both leaf area and the structure of nonfoliar green components, and was inverted using compact-polarization Synthetic Aperture Radar (SAR) data. First, by analyzing the succession characteristics of rape crop photosynthetic organs and canopy components at different growth stages, CAI was defined as the sum of LAI and Non-Foliar Area Index (NFAI), the latter derived from the structure of siliques. Then, using the compact-polarization SAR data simulated from fully polarized data as the data source, regional rape crop CAI inversion was performed using the RF model, and its accuracy was compared to that obtained using full-polarization SAR data. Results indicate that LAI underestimated canopy area by 29.1% during the silique stage and 42.1% during the maturity stage. Inversion model using full-polarization SAR achieved a normalized root mean squared error (nRMSE) of 6.7%, while compact-polarization SAR produced comparable accuracy (nRMSE: 7.7% to 5.5%), suggesting that compact-polarization SAR could provide sufficient information for CAI inversion with simpler design and lower data volume. These results offer a robust technical basis for monitoring crop growth and estimating yield, particularly in regions prone to cloudy and rainy weather.

Crop chlorophyll contents affect growth, and accurate assessment aids field management. SPAD (Soil Plant Analysis Development) values of leaves were mainly used to estimate chlorophyll content. Background interference affects the accuracy of SPAD value inversion. To address this issue, a rice leaf SPAD inversion method combining deep learning and feature selection is proposed. First, a leaf segmentation model based on U-Net was established. Then, the color features of leaf images were extracted. Seven color features highly correlated with SPAD were selected via the Pearson correlation coefficient and recursive feature elimination optimization. Finally, leaf SPAD inversion models based on random forest, support vector regression, BPNNs, and XGBoost were established. The results demonstrated that the U-Net could achieve accurate segmentation of leaves with a maximum mean intersection over union (MIoU) of 88.23. The coefficients of determination R² between the anticipated and observed SPAD values of the four models were 0.819, 0.829, 0.896, and 0.721, and the root mean square errors (RMSEs) were 2.223, 2.131, 1.564, and 2.906. Through comparison, the method can accurately predict SPAD in different low-definition and saturation images, showing a certain robustness. It can offer technical support for accurate, nondestructive, and expedited evaluation of crop leaves’ chlorophyll content via machine vision.

Presents corrections to the paper, The Joint Experiment for Crop Assessment and Monitoring (JECAM) in GEOGLAM unites 11 countries and regions.

Most existing studies on the optimal bandwidth selection for plant nitrogen are based on the sensitive band center, and determine the optimal bands by manually adjusting the bandwidth, step by step. However, this method has a high level of manual involvement and is time-consuming. This paper focused on rice as the research subject, based on determining the center of the rice plant nitrogen-sensitive bands and the maximum region Ω of the fitted R² between the narrow-band vegetation indices (N-VIs) and plant nitrogen, a method was proposed to automatically select the optimal bandwidth by constructing inscribed rectangles. UAV hyperspectral images were used to carry out the spatial inversion and precision verification of the rice plant nitrogen, based on the optimal width of sensitive bands. The results revealed that the optimal bandwidths, automatically selected on the basis of N-VIs via the inscribed rectangle method, achieved good results in the remote sensing inversion of plant nitrogen at the rice jointing and flowering stages, with the coefficient of determination (R²) greater than 0.49 to satisfy the requirement of significance (p < 0.05) and the normalized root mean square error (NRMSE) and mean relative error (MRE) of less than 13%. These findings indicate that the method of crop plant nitrogen inversion band center screening and automatic search for the optimal bandwidth in this study has certain feasibility, which provides a new idea for screening the optimal bandwidth on the basis of the sensitive band center and provides technical support for the design of satellite band parameters.

Accurately predicting arsenic (As) concentration in farmland soil on a large scale is essential for effectively preventing and managing soil pollution in agricultural areas, thereby safeguarding food security. Multispectral imaging presents a cost-effective and efficient method for monitoring As concentration across extensive farmland regions. Nevertheless, the underlying process and mechanisms determining the relationship between As concentration in farmland soil and spectral data remain uncertain. The primary aim of this study was to evaluate whether employing a hierarchical strategy (based on soil organic matter (SOM) and pH) results in more accurate prediction of As concentration in farmland soil than those employing nonhierarchical (global) models. Our results show that with respect to global models, the best prediction of As concentration was achieved using the convolutional neural network (CNN) model (validated ratio of the model performance to the interquartile distance (RPIQ) = 2.50), followed by the Cubist model (validated RPIQ = 2.19) and the extreme learning machine (ELM) model (validated RPIQ = 2.10). After SOM-based hierarchization, the Cubist model exhibited the highest prediction accuracy (validated coefficient of determination (R²) = 0.73), representing a 0.02 improvement in the R² compared with the that of global CNN model. The clay minerals ratio (CMR) was identified as the most important variable for predicting As concentration. Notably, the identification of high As concentration in the central old town areas underscores the importance of early soil contamination risk warnings on arable lands. These findings indicate that SOM-hierarchical machine learning models could serve as an effective approach to addressing the influence of soil environmental complications on spectral prediction of As concentration in farmland soil. By implementing this proposed method, soil environmental monitoring efforts can be significantly improved.

Timely and accurate monitoring of key agricultural information on crops is the premise and foundation for ensuring national food security and guiding agricultural production in a scientific and precise manner [1]. In recent years, domestic and foreign governments and research departments have included agricultural information monitoring within key research programs.

To address the underestimation of rape yield by traditional gramineous crop yield simulation methods based on crop models, this study used the WOFOST crop model to estimate rape yield in the main producing areas of southern Hunan based on 2 years of field-measured data, with consideration given to the photosynthesis of siliques, which are non-foliar green organs. First, the total photosynthetic area index (TPAI), which considers the photosynthesis of siliques, was proposed as a substitute for the leaf area index (LAI) as the calibration variable in the model. Two parameter calibration methods were subsequently proposed, both of which consider photosynthesis by siliques: the TPAI-SPA method, which is based on the TPAI coupled with a specific pod area, and the TPAI-Curve method, which is based on the TPAI and curve fitting. Finally, the 2 proposed parameter calibration methods were validated via 2 years of observed rape data. The results indicate that compared with traditional LAI-based crop model calibration methods, the TPAI-SPA and TPAI-Curve methods can improve the accuracy of rape yield estimation. The estimation accuracy (R²) for the total weight of storage organs (TWSO) and above-ground biomass (TAGP) increased by 9.68% and 49.86%, respectively, for the TPAI-SPA method and by 14.04% and 42.94%, respectively, for the TPAI-Curve method. Thus, the 2 calibration methods proposed in this study are of important practical importance for improving the accuracy of rape yield simulations. This study provides a novel technical approach for utilizing crop growth models in the yield estimation of oilseed crops.