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ABSTRACTMastering the changes in the planting area of crops are critical for formulating sustainable agricultural development strategies. The Landsat 9 satellite provides continuous observational data for crop mapping. It is necessary to evaluate the potential of Landsat 9 images for canola mapping. According to literatures, some canola indices have been proposed in relation to the spectral features of the yellow canola flower, such as the canola ratio index (CRI), normalised difference yellowness index (NDYI), canola index (CI), canola flower index (CFI), normalised difference rapeseed flower index (NRFI) and winter rapeseed index (WRI). However, it is unclear which index has more potential to extract canola based on the Landsat 9 images. Therefore, the potentials of these indices for canola mapping were quantitatively compared by using different classification methods including supervised and unsupervised classification methods. For supervised classification methods and the CFI, the overall accuracy of canola mapping was above 90% and the kappa coefficient was above 0.8. For unsupervised classification methods, CFI also performs best. It was demonstrated that the CFI outperformed the other five indices for canola mapping. It was also confirmed that Landsat 9 images and the CFI exhibit promising potential for canola mapping via quantitative comparison.

Called the “Water Tower”, the water resource in Qinghai Province plays a vital role in the ecological environment of northwestern China. However, significant uncertainty exists regarding its spatial-temporal variations. In this study, the data on the surface water of Qinghai Province from 1986 to 2018 was extracted and a systemic analysis of its spatial-temporal variations and responses to climate change was conducted using the Google Earth Engine (GEE) cloud processing platform. Our findings revealed that the surface water in Qinghai Province was primarily concentrated in the Qaidam Basin, the Hoh Xil Nature Reserve, and Qinghai Lake. The surface water area in Qinghai Province exhibited an overall increasing trend, with the temperature and precipitation being the primary drivers of this expansion. These results provide crucial insights into the variations of surface water variations in Qinghai Province, northwestern China, under the influence of climate change.

Landsat-9 is the latest satellite of the Landsat program and provides useful agricultural remote sensing data. The earlier the crop distribution data is obtained, the greater the value of the data. However, whether Landsat-9 image is suitable for the early-season mapping of winter garlic and winter wheat is a problem worthy of attention. Therefore, this study evaluates the potential to use two Landsat-9 images, acquired on 13 December 2021 and 30 January 2022, for the early-season identification of winter garlic and winter wheat in China. According to J-M (Jeffries-Matusita) distances, we evaluated the separability of winter garlic, winter wheat, and other ground objects based on the two Landsat-9 images. Then, winter garlic and winter wheat were extracted by using unsupervised classification method, i.e. the IsoData and K-means clustering algorithms, and supervised classification method, i.e. the Random Forest (RF), and Support Vector Machine (SVM) algorithms. The separability between garlic and wheat in January is stronger than that in December. The classification overall accuracy based on the Landsat-9 image on 30 January 2022 is 92.03% with a kappa coefficient of 0.87 using the SVM algorithm. This period is about 4 months earlier than the crop harvest period. Landsat-9 images have good potentiality for early-season mapping of winter garlic and winter wheat.

Abstract Background Monopterus albus is a hermaphroditic fish with sex reversal from ovaries to testes via the ovotestes in the process of gonadal development, but the molecular mechanism of the sex reversal was unknown. Methods We produced transcriptomes containing mRNAs and lncRNAs in the crucial stages of the gonad, including the ovary, ovotestis and testis. The expression of the crucial lncRNAs and their target genes was detected using qRT‒PCR and in situ hybridization. The methylation level and activity of the lncRNA promoter were analysed by applying bisulfite sequencing PCR and dual-luciferase reporter assays, respectively. Results This effort revealed that gonadal development was a dynamic expression change. Regulatory networks of lncRNAs and their target genes were constructed through integrated analysis of lncRNA and mRNA data. The expression and DNA methylation of the lncRNAs MSTRG.38036 and MSTRG.12998 and their target genes Psmβ8 and Ptk2β were detected in developing gonads and sex reversal gonads. The results showed that lncRNAs and their target genes exhibited consistent expression profiles and that the DNA methylation levels were negatively regulated lncRNA expression. Furthermore, we found that Ptk2β probably regulates cyp19a1 expression via the Ptk2β/EGFR/STAT3 pathway to reprogram sex differentiation. Conclusions This study provides novel insight from lncRNA to explore the potential molecular mechanism by which DNA methylation regulates lncRNA expression to facilitate target gene transcription to reprogram sex differentiation in M. albus, which will also enrich the sex differentiation mechanism of teleosts.

In this paper, by using the Riccati transformation and integral inequality technique, we establish several oscillation criteria for second-order Emden–Fowler neutral delay differential equations under the canonical case and non-canonical case, respectively. Compared with some recent results reported in the literature, we extend the range of the neutral coefficient. Therefore, our results generalize to some of the results presented in the literature. Furthermore, several examples are provided to illustrate our conclusions.

Highlights DNA methylation modification plays a more crucial role than chromatin accessibility in gonad development. DNA methylation in the transcription start site plays a key role in gonad development. DNA methylation of the plastin-2 promoter inhibited gene expression. Plastin-2 is positively regulated by dmrt1 while negatively regulated by foxl2.

Farmed chinese giant salamander (Andrias davidianus) was an important distinctive economically amphibian that exhibited male-biased sexual size dimorphism. Fgf9 and rspo1 genes antagonize each other in Wnt4 signal pathway to regulate mammalian gonadal differentiation has been demonstrated. However, their expression profile and function in A. davidianus are unclear. In this study, we firstly characterized fgf9 and rspo1 genes expression in developing gonad. Results showed that fgf9 expression level was higher in testes than in ovaries and increased from 1 to 6 years while rspo1 expression was higher in ovaries than in testes. In situ hybridization assay showed that both fgf9 and rspo1 genes expressed at 62 dpf in undifferentiated gonad, and fgf9 gene was mainly expressed in spermatogonia and sertoli cells in testis while strong positive signal of rspo1 was detected in granular cell in ovary. During sex-reversal, fgf9 expression was significantly higher in reversed testes and normal testes than in ovaries, and opposite expression pattern was detected for rspo1. When FH535 was used to inhibit Wnt/β-catenin pathway, expression of rspo1, wnt4 and β-catenin was down-regulated. Conversely, expression of fgf9, dmrt1, ftz-f1 and cyp17 were up-regulated. Furthermore, when rspo1 and fgf9 were knocked down using RNAi technology, respectively. We observed that female biased genes were down regulated in ovary primordial cells after rspo1 was knocked down, while the opposite expression profile was observed in testis primordial cells after fgf9 was knocked down. These results suggested that fgf9 and rspo1 played an antagonistic role to regulate sex differentiation in the process of the gonadal development and provided a foundation for further functional characterizations. The data also provided basic information for genome editing breeding to improve the Chinese giant salamander farming industry.

Abstract Background The Chinese giant salamander Andrias davidianus is an important amphibian species in China because of its increasing economic value, protection status and special evolutionary position from aquatic to terrestrial animal. Its large genome presents challenges to genetic research. Genetic linkage mapping is an important tool for genome assembly and determination of phenotype-related loci. Results In this study, we constructed a high-density genetic linkage map using ddRAD sequencing technology to obtain SNP genotyping data of members from an full-sib family which sex had been determined. A total of 10,896 markers were grouped and oriented into 30 linkage groups, representing 30 chromosomes of A. davidianus. The genetic length of LGs ranged from 17.61 cM (LG30) to 280.81 cM (LG1), with a mean inter-locus distance ranging from 0.11(LG3) to 0.48 cM (LG26). The total genetic map length was 2643.10 cM with an average inter-locus distance of 0.24 cM. Three sex-related loci and four sex-related markers were found on LG6 and LG23, respectively. Conclusion We constructed the first High-density genetic linkage map and identified three sex-related loci in the Chinese giant salamander. Current results are expected to be a useful tool for future genomic studies aiming at the marker-assisted breeding of the species.

Bacterial infection is a major issue after artificial bone transplantation due to the absence of antibacterial function of bone scaffold, which seriously causes the transplant failure and even amputation in severe cases. In this study, oxygen vacancy (OV) defects Fe-doped TiO _2 (OV-FeTiO _2 ) nanoparticles were synthesized by nano TiO _2 and Fe _3 O _4 via high-energy ball milling, which was then incorporated into polycaprolactone/polyglycolic acid (PCLGA) biodegradable polymer matrix to construct composite bone scaffold with good antibacterial activities by selective laser sintering. The results indicated that OV defects were introduced into the core/shell-structured OV-FeTiO _2 nanoparticles through multiple welding and breaking during the high-energy ball milling, which facilitated the adsorption of hydrogen peroxide (H _2 O _2 ) in the bacterial infection microenvironment at the bone transplant site. The accumulated H _2 O _2 could amplify the Fenton reaction efficiency to induce more hydroxyl radicals (·OH), thereby resulting in more bacterial deaths through ·OH-mediated oxidative damage. This antibacterial strategy had more effective broad-spectrum antibacterial properties against Gram-negative Escherichia coli ( E. coli ) and Gram-positive Staphylococcus aureus ( S. aureus ). In addition, the PCLGA/OV-FeTiO _2 scaffold possessed mechanical properties that match those of human cancellous bone and good biocompatibility including cell attachment, proliferation and osteogenic differentiation.

Abstract Background Chinese giant salamander Andrias davidianus is an endangered species. The success of artificial breeding provides a useful way to protect this species. However, the method to identify the sex and mechanism of sex determination were unclear which hinder the improvement of the artificial breeding. Detection of a sex specific marker provides an effective approach to identify genetic sex and investigate the sex determination mechanism. Results We used restriction-site-associated DNA (RAD) sequencing to isolate a sex-specific genetic marker in A. davidianus to expand knowledge of the sex determination mechanism. Four male and four female specimens were subjected to RAD sequencing, which generated 934,072,989 reads containing approximately 134.4 Gb of sequences. The first round of comparison of the assembled sequence against the opposite sex raw reads revealed 19,097 female and 17,994 male unmatched sequences. Subsequently, 19,097 female sequences were subjected to a BLAST search against male genomic data, which revealed 308 sequences unmapped to the male genome. One hundred of these were randomly selected and validated by PCR in five male and five female specimens, and four putative sex-specific sequences were produced. Further validation was performed by PCR in another 24 females and 24 males, and all female individuals exhibited the expected specific bands, while the males did not. To apply the sex-specific marker, three specimens reversed from genetic female to physiological male were found in a group exposed to elevated temperature, and 13 individuals reversed from genetic male to physiological female were obtained in a 17β-estradiol exposed group. Conclusion This is the first report of a sex-specific marker in A. davidianus and may have potential for elucidation of its sex determination mechanism and, hence, its conservation.

In this paper, we investigate some nonoscillatory and oscillatory solutions for a class of second-order nonlinear neutral delay differential equations with positive and negative coefficients. By means of the method of contraction mapping principle and some integral inequality techniques, we extend the recent results provided in the literature.

Accurate and reliable land cover information is vital for ecosystem management and regional sustainable development, especially for ecologically vulnerable areas. The South China Karst, one of the largest and most concentrated karst distribution areas globally, has been undergoing large-scale afforestation projects to combat accelerating land degradation since the turn of the new millennium. Here, we assess five recent and widely used global land cover datasets (i.e., CCI-LC, MCD12Q1, GlobeLand30, GlobCover, and CGLS-LC) for their comparative performances in land dynamics monitoring in the South China Karst during 2000–2020 based on the reference China Land Use/Cover Database. The assessment proceeded from three aspects: areal comparison, spatial agreement, and accuracy metrics. Moreover, divergent responses of overall accuracy with regard to varying terrain and geomorphic conditions have also been quantified. The results reveal that obvious discrepancies exist amongst land cover maps in both area and spatial patterns. The spatial agreement remains low in the Yunnan–Guizhou Plateau and heterogeneous mountainous karst areas. Furthermore, the overall accuracy of the five datasets ranges from 40.3% to 52.0%. The CGLS-LC dataset, with the highest accuracy, is the most accurate dataset for mountainous southern China, followed by GlobeLand30 (51.4%), CCI-LC (50.0%), MCD12Q1 (41.4%), and GlobCover (40.3%). Despite the low overall accuracy, MCD12Q1 has the best accuracy in areas with an elevation above 1200 m or a slope greater than 25°. With regard to geomorphic types, accuracy in non-karst areas is evidently higher than in karst areas. Additionally, dataset accuracy declines significantly (p < 0.05) with an increase in landscape heterogeneity in the region. These findings provide useful guidelines for future land cover mapping and dataset fusion.

Because canola is a major oilseed crop, accurately determining its planting areas is crucial for ensuring food security and achieving UN 2030 sustainable development goals. However, when canola is extracted using remote-sensing data, winter wheat causes serious interference because it has a similar growth cycle and spectral reflectance characteristics. This interference seriously limits the classification accuracy of canola, especially in mixed planting areas. Here, a novel canola flower index (CFI) is proposed based on the red, green, blue, and near-infrared bands of Sentinel-2 images to improve the accuracy of canola mapping, based on the finding that spectral reflectance of canola on the red and green bands is higher than that of winter wheat during the canola flowering period. To investigate the potential of the CFI for extracting canola, the IsoData, support vector machine (SVM), and random forest (RF) classification methods were used to extract canola based on Sentinel-2 raw images and CFI images. The results show that the average overall accuracy and kappa coefficient based on CFI images were 94.77% and 0.89, respectively, which were 1.05% and 0.02, respectively, higher than those of the Sentinel-2 raw images. Then we found that a threshold of 0.14 on the CFI image could accurately distinguish canola from non-canola vegetation, which provides a solution for automatic mapping of canola. The overall classification accuracy and kappa coefficient of this threshold method were 96.02% and 0.92, which were very similar to those of the SVM and RF methods. Moreover, the advantage of the threshold classification method is that it reduces the dependence on training samples and has good robustness and high classification efficiency. Overall, this study shows that CFI and Sentinel-2 images provide a solution for automatic and accurate canola extraction.

Abstract To search the evidence of molecular evolution mechanism for aquatic and cave habitat in Andrias davidianus, the evolution analysis was carried out among several species transcriptome data. The transcriptome data of Notophthalmus viridescens, Xenopus tropicalis, Cynops pyrrhogaster, Hynobius chinensis and A. davidianus were obtained from the Genbank and reassembled except Xenopus tropicalis. The BLAST search of transcriptome data obtained 1244 single-copy orthologous genes among five species. A phylogenetic tree showed A. davidianus to have the closest relationship to H. chinensis. Fourteen positively selected genes were detected in A. davidianus and N. vridescens group and fifteen in A. davidianus and H. chinensis group. Five genes were shared in the both groups which involved in the immune system, suggesting that A. davidianus adaptation to an aquatic and cave environment required rapid evolution of the immune system compared to N. viridescens and H. chinensis.

Sentinel-2 imagery is an unprecedented data source with high spatial, spectral and temporal resolution in addition to free access. The objective of this paper was to evaluate the potential of using Sentinel-2 data to map winter crops in the early growth stage. Analysis of three winter crop types—winter garlic, winter canola and winter wheat—was carried out in two agricultural regions of China. We analysed the spectral characteristics and vegetation index profiles of these crops in the early growth stage and other land cover types based on Sentinel-2 images. A decision tree classification model was built to distinguish the crops based on these data. The results demonstrate that winter garlic and winter wheat can be distinguished four months before harvest, while winter canola can be distinguished two months before harvest. The overall classification accuracy was 96.62% with a kappa coefficient of 0.95. Therefore, Sentinel-2 images can be used to accurately identify these winter crops in the early growth stage, making them an important data source in the field of agricultural remote sensing.

Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

Accurately quantifying spatiotemporal changes in surface water is essential for water resources management, nevertheless, the dynamics of Poyang Lake surface water areas with high spatiotemporal resolution, as well as its responses to climate change, still face considerable uncertainties. Using the time series of Sentinel-1 images with 6- or 12-day intervals, the Sentinel-1 water index (SWI), and SWI-based water extraction model (SWIM) from 2015 to 2020 were used to document and study the short-term characteristics of southwest Poyang Lake surface water. The results showed that the overall accuracy of surface water area was satisfactory with an average of 91.92%, and the surface water area ranged from 129.06 km2 on 2 March 2017 to 1042.57 km2 on 17 July 2016, with significant intra- and inter-month variability. Within the 6-day interval, the maximum change of lake area was 233.42 km2 (i.e., increasing from 474.70 km2 up to 708.12 km2). We found that the correlation coefficient between the water area and the 45-day accumulated precipitation reached to 0.75 (p < 0.001). Moreover, a prediction model was built to predict the water area based on climate records. These results highlight the significance of high spatiotemporal resolution mapping for surface water in the erratic southwest Poyang Lake under a changing climate. The automated water extraction algorithm proposed in this study has potential applications in delineating surface water dynamics at broad geographic scales.

Since the implementation of China’s afforestation and conservation projects during recent decades, an increasing number of studies have reported greening trends in the karst regions of southwest China using coarse-resolution satellite imagery, but small-scale changes in the heterogenous landscapes remain largely unknown. Focusing on two typical karst regions in the Nandong and Xiaojiang watersheds in Yunnan province, we processed 2,497 Landsat scenes from 1988 to 2016 using the Google Earth Engine cloud platform and analyzed vegetation trends and associated drivers. We found that both watersheds experienced significant increasing trends in annual fractional vegetation cover, at a rate of 0.0027 year−1 and 0.0020 year−1, respectively. Notably, the greening trends have been intensifying during the conservation period (2001−2016) even under unfavorable climate conditions. Human-induced ecological engineering was the primary factor for the increased greenness. Moreover, vegetation change responded differently to variations in topographic gradients and lithological types. Relatively more vegetation recovery was found in regions with moderate slopes and elevation, and pure limestone, limestone and dolomite interbedded layer as well as impure carbonate rocks than non-karst rocks. Partial correlation analysis of vegetation trends and temperature and precipitation trends suggested that climate change played a minor role in vegetation recovery. Our findings contribute to an improved understanding of the mechanisms behind vegetation changes in karst areas and may provide scientific supports for local afforestation and conservation policies.

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

Spatial variation of soil respiration (Rs) in cropland ecosystems must be assessed to evaluate the global terrestrial carbon budget. This study aims to explore the spatial characteristics and controlling factors of Rs in a cropland under winter wheat and summer maize rotation in the North China Plain. We collected Rs data from 23 sample plots in the cropland. At the late jointing stage, the daily mean Rs of summer maize (4.74 μmol CO2 m-2 s-1) was significantly higher than that of winter wheat (3.77μmol CO2 m-2 s-1). However, the spatial variation of Rs in summer maize (coefficient of variation, CV = 12.2%) was lower than that in winter wheat (CV = 18.5%). A similar trend in CV was also observed for environmental factors but not for biotic factors, such as leaf area index, aboveground biomass, and canopy chlorophyll content. Pearson's correlation analyses based on the sampling data revealed that the spatial variation of Rs was poorly explained by the spatial variations of biotic factors, environmental factors, or soil properties alone for winter wheat and summer maize. The similarly non-significant relationship was observed between Rs and the enhanced vegetation index (EVI), which was used as surrogate for plant photosynthesis. EVI was better correlated with field-measured leaf area index than the normalized difference vegetation index and red edge chlorophyll index. All the data from the 23 sample plots were categorized into three clusters based on the cluster analysis of soil carbon/nitrogen and soil organic carbon content. An apparent improvement was observed in the relationship between Rs and EVI in each cluster for both winter wheat and summer maize. The spatial variation of Rs in the cropland under winter wheat and summer maize rotation could be attributed to the differences in spatial variations of soil properties and biotic factors. The results indicate that applying cluster analysis to minimize differences in soil properties among different clusters can improve the role of remote sensing data as a proxy of plant photosynthesis in semi-empirical Rs models and benefit the acquisition of Rs in cropland ecosystems at large scales.

Areas and spatial distribution information of paddy rice are important for managing food security, water use, and climate change. However, there are many difficulties in mapping paddy rice, especially mapping multi-season paddy rice in rainy regions, including differences in phenology, the influence of weather, and farmland fragmentation. To resolve these problems, a novel multi-season paddy rice mapping approach based on Sentinel-1A and Landsat-8 data is proposed. First, Sentinel-1A data were enhanced based on the fact that the backscattering coefficient of paddy rice varies according to its growth stage. Second, cropland information was enhanced based on the fact that the NDVI of cropland in winter is lower than that in the growing season. Then, paddy rice and cropland areas were extracted using a K-Means unsupervised classifier with enhanced images. Third, to further improve the paddy rice classification accuracy, cropland information was utilized to optimize distribution of paddy rice by the fact that paddy rice must be planted in cropland. Classification accuracy was validated based on ground-data from 25 field survey quadrats measuring 600 m × 600 m. The results show that: multi-season paddy rice planting areas effectively was extracted by the method and adjusted early rice area of 1630.84 km2, adjusted middle rice area of 556.21 km2, and adjusted late rice area of 3138.37 km2. The overall accuracy was 98.10%, with a kappa coefficient of 0.94.