Analysis of Combining SAR and Optical Optimal Parameters to Classify Typhoon-Invasion Lodged Rice: A Case Study Using the Random Forest Method

Identification of a Major Locus for Lodging Resistance to Typhoons Using QTL Analysis in Rice

We detected a new target quantitative trait locus (QTL) for lodging resistance in rice by analyzing lodging resistance to typhoons (Maysak and Haishen) using a scale from 0 (no prostrating) to 1 (little prostrating or prostrating) to record the resistance score in a Cheongcheong/Nagdong double haploid rice population. Five quantitative trait loci for lodging resistance to typhoons were detected. Among them, qTyM6 and qTyH6 exhibited crucial effects of locus RM3343-RM20318 on chromosome 6, which overlaps with our previous rice lodging studies for the loci qPSLSA6-2, qPSLSB6-5, and qLTI6-2. Within the target locus RM3343-RM20318, 12 related genes belonging to the cytochrome P450 protein family were screened through annotation. Os06g0599200 (OsTyM/Hq6) was selected for further analysis. We observed that the culm and panicle lengths were positively correlated with lodging resistance to typhoons. However, the yield was negatively correlated with lodging resistance to typhoons. The findings of this study improve an understanding of rice breeding, particularly the culm length, early maturing, and heavy panicle varieties, and the mechanisms by which the plant's architecture can resist natural disasters such as typhoons to ensure food safety. These results also provide the insight that lodging resistance in rice may be associated with major traits such as panicle length, culm length, tiller number, and heading date, and thereby improvements in these traits can increase lodging resistance to typhoons. Moreover, rice breeding should focus on maintaining suitable varieties that can withstand the adverse effects of climate change in the future and provide better food security.

A Simple and Robust Spectral Index for Identifying Lodged Maize Using Gaofen1 Satellite Data

Crop lodging is a major destructive factor for agricultural production. Developing a cost-efficient and accurate method to assess crop lodging is crucial for informing crop management decisions and reducing lodging losses. Satellite remote sensing can provide continuous data on a large scale; however, its utility in detecting lodging crops is limited due to the complexity of lodging events and the unavailability of high spatial and temporal resolution data. Gaofen1 satellite was launched in 2013. The short revisit cycle and wide orbit coverage of the Gaofen1 satellite make it suitable for lodging identification. However, few studies have explored lodging detection using Gaofen1 data, and the operational application of existing approaches over large spatial extents seems to be unrealistic. In this paper, we discuss the identification method of lodged maize and explore the potential of using Gaofen1 data. An analysis of the spectral features after maize lodging revealed that reflectance increased significantly in all bands, compared to non-lodged maize. A spectral sum index was proposed to distinguish lodged and non-lodged maize. Two study areas were considered: Zhaodong City in Heilongjiang Province and Ningjiang District in Jilin Province. The results of the identified lodged maize from the Gaofen1 data were validated based on three methods: first, ground sample points exhibited the overall accuracies of 92.86% and 88.24% for Zhaodong City and Ningjiang District, respectively; second, the cross-comparison differences of 1.01% for Zhaodong City and 1.13% for Ningjiang District were obtained, compared to the results acquired from the finer-resolution Planet data; and third, the identified results from Gaofen1 data and those from farmer survey questionnaires were found to be consistent. The validation results indicate that the proposed index is promising, and the Gaofen1 data have the potential for rapid lodging monitoring.

Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat.

Bounds on the Lifetime Expectations of Series Systems with IFR Component Lifetimes

We consider series systems built of components which have independent identically distributed (iid) lifetimes with an increasing failure rate (IFR). We determine sharp upper bounds for the expectations of the system lifetimes expressed in terms of the mean, and various scale units based on absolute central moments of component lifetimes. We further establish analogous bounds under a more stringent assumption that the component lifetimes have an increasing density (ID) function. We also indicate the relationship between the IFR property of the components and the generalized cumulative residual entropy of the series system lifetime.