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Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic research, and accurate observation of SSW is valuable for a wide range of applications. However, most existing SSW data products are at a coarse spatial resolution, which is insufficient, especially for regional or local studies. Therefore, in this paper, to derive finer-resolution estimates of SSW, we present a novel statistical downscaling approach for satellite SSW based on generative adversarial networks and dual learning scheme, taking WindSat as a typical example. The dual learning scheme performs a primal task to reconstruct high resolution SSW, and a dual task to estimate the degradation kernels, which form a closed loop and are simultaneously learned, thus introducing an additional constraint to reduce the solution space. The integration of a dual learning scheme as the generator into the generative adversarial network structure further yield better downscaling performance by fine-tuning the generated SSW closer to high-resolution SSW. Besides, a model adaptation strategy was exploited to enhance the capacity for downscaling from low-resolution SSW without high-resolution ground truth. Comprehensive experiments were conducted on both the synthetic paired and unpaired SSW data. In the study areas of the East Coast of North America and the North Indian Ocean, in this work, the downscaling results to 0.25° (high resolution on the synthetic dataset), 0.03125° (8× downscaling), and 0.015625° (16× downscaling) of the proposed approach achieve the highest accuracy in terms of root mean square error and R-Square. The downscaling resolution can be enhanced by increasing the basic blocks in the generator. The highest downscaling reconstruction quality in terms of peak signal-to-noise ratio and structural similarity index was also achieved on the synthetic dataset with high-resolution ground truth. The experimental results demonstrate the effectiveness of the proposed downscaling network and the superior performance compared with the other typical advanced downscaling methods, including bicubic interpolation, DeepSD, dual regression networks, and adversarial DeepSD. [ABSTRACT FROM AUTHOR]

We sought to explore the role of postanthesis carbon and nitrogen (C-N) metabolism of hybrid rice in increasing yield and nitrogen utilization efficiency (NUE). We used the 13C and 15N dual-isotope tracer method and physiological/biochemical analysis and established different irrigation and nitrogen fertilization (W-N) regimes to investigate the relationship of C-N metabolism characteristics, yield, and NUE. The results showed that W-N regimes had significant effects on postanthesis absorption and translocation of N and photosynthate, yield and NUE. Aerobic irrigation combined with the N fertilization regime 30% base, 30% tillering, 40% booting was the best W-N coupling regime for rice yield and NUE increase. The regime enhanced flag leaf photosynthesis rate and the activities of ribulose 1,5-diphosphate carboxylase/oxygenase (RuBPCase), glutamine synthetase (GS), and other key enzymes of C-N metabolism, and improved the total accumulations of photoassimilates (0.97–21.57 mg 13C plant−1) and N (1.55–23.36 mg 15N plant−1), respectively. Correlation analysis showed that, under the W-N interaction, C-N metabolism enzymes promoted the positive synergistic effect between 13C and 15N accumulation in panicles (r = 0.825). In addition, the change in C/N ratio can be used as an indicator of the simultaneous improvement in yield and NUE in hybrid rice. [ABSTRACT FROM AUTHOR]

Rice lacks sufficient amounts of zinc despite its vitality for human health. Leaf senescence enables redistribution of nutrients to other organs, yet Zn retransfer during deficiency is often overlooked. In this hydroponic experiment, we studied the effect of Zn deficiency on rice seedlings, focusing on the fourth leaf under control and deficient conditions. Growth phenotype analysis showed that the growth of rice nodal roots was inhibited in Zn deficiency, and the fourth leaf exhibited accelerated senescence and increased Zn ion transfer. Analyzing differentially expressed genes showed that Zn deficiency regulates more ZIP family genes involved in Zn ion retransfer. OsZIP3 upregulation under Zn-deficient conditions may not be induced by Zn deficiency, whereas OsZIP4 is only induced during Zn deficiency. Gene ontology enrichment analysis showed that Zn-deficient leaves mobilized more biological pathways (BPs) during aging, and the enrichment function differed from that of normal aging leaves. The most apparent "zinc ion transport" BP was stronger than that of normal senescence, possibly due to Zn-deficient leaves mobilizing large amounts of BP related to lipid metabolism during senescence. These results provide a basis for further functional analyses of genes and the study of trace element transfer during rice leaf senescence. [ABSTRACT FROM AUTHOR]

As the human population grows rapidly, food shortages will become an even greater problem; therefore, increasing crop yield has become a focus of rice breeding programs. The maize gene, ZmDUF1645, encoding a putative member of the DUF1645 protein family with an unknown function, was transformed into rice. Phenotypic analysis showed that enhanced ZmDUF1645 expression significantly altered various traits in transgenic rice plants, including increased grain length, width, weight, and number per panicle, resulting in a significant increase in yield, but a decrease in rice tolerance to drought stress. qRT-PCR results showed that the expression of the related genes regulating meristem activity, such as MPKA, CDKA, a novel crop grain filling gene (GIF1), and GS3, was significantly changed in the ZmDUF1645-overexpression lines. Subcellular colocalization showed that ZmDUF1645 was primarily localized on cell membrane systems. Based on these findings, we speculate that ZmDUF1645, like the OsSGL gene in the same protein family, may regulate grain size and affect yield through the cytokinin signaling pathway. This research provides further knowledge and understanding of the unknown functions of the DUF1645 protein family and may serve as a reference for biological breeding engineering to increase maize crop yield. [ABSTRACT FROM AUTHOR]

As a special micro-motion feature of rotor target, rotational angular velocity can provide a discriminant basis for target classification and recognition. In this paper, the authors focus on an efficient rotational angular velocity estimation method of the rotor target is based on the combination of the time–frequency analysis algorithm and Hough transform. In order to avoid the problems of low time–frequency resolution and cross-term interference in short-time Fourier transform and Wigner–Ville distribution algorithm, a modified short-time fractional Fourier transform (M-STFRFT) is proposed to obtain the time-FRFT domain (FRFD)-frequency spectrum with the highest time–FRFD–frequency resolution. In particular, an orthogonal matching pursuit (OMP)-based algorithm is proposed to reduce the computational complexity when estimating the matched transform order in the proposed M-STFRFT algorithm. Firstly, partial transform order candidates are selected randomly from the complete candidates. Then, a partial entropy vector corresponding to partial transform order candidates is calculated from the FRFT results and utilized to reconstruct the complete entropy vector via the OMP algorithm, and the matched transform order can be estimated by searching minimum entropy. Based on the estimated matched transform order, STFRFT is performed to obtain the time–FRFD–frequency spectrum. Moreover, Hough transform is employed to obtain the energy accumulation spectrum, and the micro-Doppler parameter of rotational angular velocity can be estimated by searching the peak value from the energy accumulation spectrum. Both simulated data and measured data collected by frequency modulated continuous wave radar validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To evaluate the efficient use of nitrogen (N) for rice in a rapeseed–wheat–rice rotation system, a pot experiment was conducted. The results indicated that in the conventional 15N-labeled (Nc) and reduced 15N-labeled (Nr) urea applications, absorbed N and soil residual N was higher in rapeseed than in wheat. In the rice season, the higher accumulation of 15N was achieved with an Nr application rate during the rapeseed season and an N fertilizer management model (40% as basal fertilizer, 40% as tillering fertilizer, and 20% as panicle fertilizer) during the rice season (PrNrM3). A high 15N accumulation was also achieved under the Nc application rate during the wheat season and the N fertilizer management model during the rice season (PwNcM3). The accumulation of 15N in PrNrM3 and PwNcM3 accounted for 21.35% and 36.72% of the residual N under the Nr application rate in the rapeseed season and the Nc application rate in the wheat season, respectively. Compared with the Nc application rate in the rapeseed season and M3 N management in the rice season (PrNcM3), the N agronomy efficiency (NAE) and the N partial factor efficiency (NPFP) of rice were increased by 23.85% and 1.59%, respectively, in PrNrM3. The annual crop yield was 3.95% lower in PrNrM3, which was not significant. PrNrM3 was a stable yield, N-saving application rate for rapeseed-rice rotation systems in southern China. [ABSTRACT FROM AUTHOR]

This investigation aims to provide theoretical and practical evidence for the efficient utilization of nitrogen (N) in paddy-upland rapeseed-rice rotation systems because a lack of previous research on such rotation systems leads to inefficient management practices. The effects of the N application rates and the N fertilizer management strategies for rapeseed and rice were examined, respectively, in relation to the photosynthetic productivity and yields of hybrid rice. The results indicated that the leaf area, Pn, with 40% as basal fertilizer, 40% as tillering fertilize, and 20% as panicle fertilizer and a reduced N rate (30 kg/ha) during the rape season, were higher than other nitrogen management strategies trialed, with conventional N rates in the rape season. The average rice grain yield (9545.15 kg/ha) over the two years with 40% as basal fertilizer, 40% as tillering fertilizer, and 20% as panicle fertilizer was higher than other N treatments with the reduced N rates during the rape season. The reduced N rate during the rapeseed season and 40% as basal fertilizer, 40% as tillering fertilizer, and 20% as panicle fertilizer management during the rice season for the rape-rice rotation system exhibited the highest rice yields. Our findings indicated that the N fertilizer management model was a high-yielding, N-saving, and environmentally friendly measure for rape–rice rotation systems in southern China. [ABSTRACT FROM AUTHOR]

For a conventional narrow-band radar system, the detectable information of the target is limited, and it is difficult for the radar to accurately identify the target type. In particular, the classification probability will further decrease when part of the echo data is missed. By extracting the target features in time and frequency domains from multi-wave gates sparse echo data, this paper presents a classification algorithm in conventional narrow-band radar to identify three different types of aircraft target, i.e., helicopter, propeller and jet. Firstly, the classical sparse reconstruction algorithm is utilized to reconstruct the target frequency spectrum with single-wave gate sparse echo data. Then, the micro-Doppler effect caused by rotating parts of different targets is analyzed, and the micro-Doppler based features, such as amplitude deviation coefficient, time domain waveform entropy and frequency domain waveform entropy, are extracted from reconstructed echo data to identify targets. Thirdly, the target features extracted from multi-wave gates reconstructed echo data are weighted and fused to improve the accuracy of classification. Finally, the fused feature vectors are fed into a support vector machine (SVM) model for classification. By contrast with the conventional algorithm of aircraft target classification, the proposed algorithm can effectively process sparse echo data and achieve higher classification probability via weighted features fusion of multi-wave gates echo data. The experiments on synthetic data are carried out to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]