Abstract:The soil freeze-thaw controls the hydrological and carbon cycling and thus affects water and energy exchanges at land surface. This article reported a newly developed algorithm for distinguishing the freeze/thaw status of surface soil. The algorithm was based on information from Advanced Microwave Scanning Radiometer Enhanced (AMSR-E) which records brightness temperature (Tb) in the afternoon and after midnight. The criteria and discriminant functions were obtained from both radiometer observations and model simulations. First of all, the microwave radiation from freeze-thaw soil was examined by carrying out experimental measurements at 18Ð7 and 36Ð5 GHz using a Truck-mounted Multi-frequency Microwave Radiometer (TMMR) in the Heihe River of China. The experimental results showed that the soil moisture is a key component that differentiates the microwave radiation behaviours during the freeze-thaw process, and the differences in soil temperature and emissivity between frozen and thawed soils were found to be the most important criteria. Secondly, a combined model was developed to consider the impacts of complex ground surface conditions on the discrimination. The model simulations quite followed the trend of in situ observations with an overall relation coefficient (R) of approximately 0Ð88. Finally, the ratio of Tb18Ð7H (horizontally polarized Tb at 18Ð7 GHz) to Tb36Ð5V was considered primarily as the quasi-emissivity, which is more reasonable and explicit in measuring the microwave radiation changes in soil freezing and thawing than the spectral gradient. By combining Tb36Ð5V to indicate the soil temperature variety, a Fisher linear discrimination analysis was used to establish the discriminant functions. After being corrected by TMMR measurements, the new discriminant algorithm had an overall accuracy of 86% when validated by 4-cm soil temperature. The multi-year discriminant results also provided a good agreement with the classification map of frozen ground in China.

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Time series of soil moisture (SM) data in the Qinghai–Tibet plateau (QTP) covering a period longer than one decade are important for understanding the dynamics of land surface–atmosphere feedbacks in the global climate system. However, most existing SM products have a relatively short time series or show low performance over the challenging terrain of the QTP. In order to improve the spaceborne monitoring in this area, this study presents a random forest (RF) method to rebuild a high-accuracy SM product over the QTP from 19 June 2002 to 31 March 2015 by adopting the advanced microwave scanning radiometer for earth observing system (AMSR-E), and the advanced microwave scanning radiometer 2 (AMSR2), and tracking brightness temperatures with latitude and longitude using the International Geosphere–Biospheres Programme (IGBP) classification data, the digital elevation model (DEM) and the day of the year (DOY) as spatial predictors. Brightness temperature products (from frequencies 10.7 GHz, 18.7 GHz and 36.5 GHz) of AMSR2 were used to train the random forest model on two years of Soil Moisture Active Passive (SMAP) SM data. The simulated SM values were compared with third year SMAP data and in situ stations. The results show that the RF model has high reliability as compared to SMAP, with a high correlation (R = 0.95) and low values of root mean square error (RMSE = 0.03 m3/m3) and mean absolute percent error (MAPE = 19%). Moreover, the random forest soil moisture (RFSM) results agree well with the data from five in situ networks, with mean values of R = 0.75, RMSE = 0.06 m3/m3, and bias = −0.03 m3/m3 over the whole year and R = 0.70, RMSE = 0.07 m3/m3, and bias = −0.05 m3/m3 during the unfrozen seasons. In order to test its performance throughout the whole region of QTP, the three-cornered hat (TCH) method based on removing common signals from observations and then calculating the uncertainties is applied. The results indicate that RFSM has the smallest relative error in 56% of the region, and it performs best relative to the Japan Aerospace Exploration Agency (JAXA), Global Land Data Assimilation System (GLDAS), and European Space Agency’s Climate Change Initiative (ESA CCI) project. The spatial distribution shows that RFSM has a similar spatial trend as GLDAS and ESA CCI, but RFSM exhibits a more distinct spatial distribution and responds to precipitation more effectively than GLDAS and ESA CCI. Moreover, a trend analysis shows that the temporal variation of RFSM agrees well with precipitation and LST (land surface temperature), with a dry trend in most regions of QTP and a wet trend in few north, southeast and southwest regions of QTP. In conclusion, a spatiotemporally continuous SM product with a high accuracy over the QTP was obtained.

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Non-coding RNA (ncRNA) is abundant in plant genomes, but is poorly described with unknown functionality in most species. Using whole genome RNA sequencing, we identified 1885, 1910 and 1299 lncRNAs and 186, 157 and 161 miRNAs at the whole genome level in the three Brassica species B. napus, B. oleracea and B. rapa, respectively. The lncRNA sequences were divergent between the three Brassica species. One quarter of lncRNAs were located in tandem repeat (TR) region. The expression of both lncRNAs and miRNAs was strongly biased towards the A rather than the C subgenome in B. napus, unlike mRNA expression. miRNAs in genic regions had higher average expression than miRNAs in non-genic regions in B. napus and B. oleracea. We provide a comprehensive reference for the distribution, functionality and interactions of lncRNAs and miRNAs in Brassica.

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Background Although rapeseed ( Brassica napus L.) mutant forming multiple siliques was morphologically described and considered to increase the silique number per plant, an important agronomic trait in this crop, the molecular mechanism underlying this beneficial trait remains unclear. Here, we combined bulked-segregant analysis (BSA) and whole genome re-sequencing (WGR) to map the genomic regions responsible for the multi-silique trait using two pools of DNA from the near-isogenic lines (NILs) zws-ms (multi-silique) and zws-217 (single-silique). We used the Euclidean Distance (ED) to identify genomic regions associated with this trait based on both SNPs and InDels. We also conducted transcriptome sequencing to identify differentially expressed genes (DEGs) between zws-ms and zws-217. Results Genetic analysis using the ED algorithm identified three SNP- and two InDel-associated regions for the multi-silique trait. Two highly overlapped parts of the SNP- and InDel-associated regions were identified as important intersecting regions, which are located on chromosomes A09 and C08, respectively, including 2044 genes in 10.20-MB length totally. Transcriptome sequencing revealed 129 DEGs between zws-ms and zws-217 in buds, including 39 DEGs located in the two abovementioned associated regions. We identified candidate genes involved in multi-silique formation in rapeseed based on the results of functional annotation. Conclusions This study identified the genomic regions and candidate genes related to the multi-silique trait in rapeseed. Electronic supplementary material The online version of this article (10.1186/s12864-019-5675-4) contains supplementary material, which is available to authorized users.

High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms.