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[ Objective ] As an important regulator involved in protein synthesis and gene expression , riboso- mal proteins are crucial for plant growth and development and stress response. In this study , a ribosomal protein family gene OsRPL36A was cloned from rice , and a preliminary study was performed for its bio- logical function , providing a basis for the study of OsRPL36A function. [ Methods ] Bioinformatics tech- niques were used to analysis gene structure , cis -regulatory elements , and evolution process of OsRPL36A . Meanwhile , the expression of OsRPL36A in different tissues and under diurnal rhythm , different hor- mones , and abiotic stresses were analyzed by quantitative real-time PCR ( qRT-PCR ) . [ Results ] ( 1 ) OsR- PL36A encoded 98 amino acids with a 297 bp coding region , which belonged to the ribosomal protein L36 supergene family , and was named as OsRPL36A . ( 2 ) The promoter region of OsRPL36A contained 3 rhythmic expression-related elements , 10 light-responsive elements , 14 hormone-responsive elements , and 27 environmental stress response elements. ( 3 ) The expression of OsRPL36A in leaves was relatively higher than that in other tissues and presented a typical rhythmic expression pattern. The expression of OsRPL36A was in- duced by IAA , high temperature , low temperature , osmotic stress , and so on. [ Conclusion ] OsRPL36A was highly expressed in leaves and presented a typical rhythmic expression pattern. OsRPL36A responded to IAA and might be involved in response to heat shock , low temperature , salt , and osmotic stresses.

Ratoon rice (RR) has emerged as an active adaptation to climate uncertainty, stabilizing total paddy rice yield and effectively reducing agriculture-related ecological environmental issues. However, identifying key remote sensing parameters for RR under cloudy and foggy conditions is challenging, and existing RR monitoring methods in these regions face significant uncertainties. Here, given the sensitivity of synthetic aperture radar (SAR) backscattering signals to the crop phenological period, this paper introduces a threshold model utilizing Sentinel-1A SAR data and phenological information for mapping RR. The Yongchuan District of Chongqing, which is often cloudy and foggy, was selected as a specific study region where VH-polarized backscatter coefficients of Sentinel-1 images were obtained at 10 m spatial resolution in 2020. Based on the proposed threshold model, the RR extraction overall accuracy was up to 90.24%, F1 score was 0.92, and Kappa coefficient was 0.80. Further analysis showed that the extracted RR boundaries exhibited high consistency with true Sentinel-2 remote sensing images and the RR extracted area was in good agreement with the actual planted area situation. This threshold model demonstrated good applicability in the studied cloudy and foggy region, and successfully distinguished RR from other paddy rice types. The methodological framework established in this study provides a basis for extensive application in China and other significant RR-producing regions globally. [ABSTRACT FROM AUTHOR]

Ratoon rice, an effective rice cultivation system, allows paddy rice to be harvested twice from the same stubble, playing an important role in ensuring food security and adapting to climate change with its unique growth characteristics. However, there is an absence of research related to remote-sensing monitoring of ratoon rice, and the presence of other rice cropping systems (e.g., double-season rice) with similar characteristics poses a hindrance to the accurate identification of ratoon rice. Furthermore, cloudy and rainy regions have limited available remote-sensing images, meaning that remote-sensing monitoring is limited. To address this issue, taking Yongchuan District, a typical cloud-prone region in Chongqing, China, as an example, this study proposed the construction of a time-series optical dataset using the Modified Neighborhood Similar Pixel Interpolator (MNSPI) method for cloud-removal interpolation and the Flexible Spatiotemporal DAta Fusion (FSDAF) model for fusing multi-source optical remote-sensing data, in combination with vegetation index features and phenological information to build a threshold model to map ratoon rice at high-resolution (10 m). The mapping performance of ratoon rice was evaluated using independent field samples to obtain the overall accuracy and kappa coefficient. The findings indicate that the combination of the MNSPI method and FSDAF model had a stable and effective performance, characterized by high correlation coefficient (r) values and low root mean square error (RMSE) values between the restored/predicted images and the true images. Notably, it was possible to effectively capture the distinct characteristics of ratoon rice in cloudy and rainy regions using the proposed threshold model. Specifically, the identified area of ratoon rice in the study region was 194.17 km2, which was close to the official data (158–180 km2), and the overall accuracy and kappa coefficient of ratoon rice identification result were 90.73% and 0.81, respectively. These results demonstrate that our proposed threshold model can effectively distinguish ratoon rice during vital phenological stages from other crop types, enrich the technical system of rice remote-sensing monitoring, and provide a reference for agricultural remote-sensing applications in cloudy and rainy regions. [ABSTRACT FROM AUTHOR]