To determine the reasonable rate of straw return and nitrogen (N) fertilizer use which may maintain soil ecosystem health, we analyzed their soil microbial biomass and composition in a 10-year field experiment with different rates of straw return (50%, 100%) and N fertilizer (270, 360, 450, 540 kg N ha−1 yr−1) by phospholipid fatty acid (PLFA) analysis and high-throughput sequencing. A rate of 50% straw return combined with 450 or 540 kg N ha−1 yr−1 effectively increased the soil available nutrient contents mainly for total nitrogen, available potassium, and available phosphorus. Total PLFAs indicated that straw return combined with N fertilizer promoted soil microbial growth and increased biomass. A rate of 100% straw return with 450 kg N ha−1 yr−1 was not conducive to the stability of the soil ecosystem according to the ratio of fungi to bacteria (F:B). The similar rate of straw returning and the similar level of nitrogen fertilizer application will be divided into the same cluster using a heatmap analysis. Some saprophytic fungi or pathogens became the dominant fungi genera, such as Gibberella, Sarocladium, Pseudallescheria, and Mycosphaerella, in the treatments with 100% straw returning combining higher N fertilizer (>450 kg ha−1 yr−1 yr−1 added). The relative abundances of some heavy metal-tolerant bacteria, such as those in Proteobacteria and Chlorobi, increased in the soils in the 100% straw return treatments. Therefore, the combined application of 100% straw returning and higher N fertilizer (>450 kg ha−1 yr−1) added long-term was not appropriate for soil health, which will lead to the risk of disease and pollution in soil.
Passive microwave data have been extensively used for snow depth (SD) inversion, but their accuracy has large error in the Qinghai-Tibet Plateau (QTP). Thus, there is still room for improvement in regional SD inversion. Ground-measured SD data combined with elevation, longitude, and land cover data were used to develop a multifactor SD inversion model based on Advanced Microwave Scanning Radiometer 2 (AMSR2) over the QTP. The SD inversion model and the AMSR2 SD product were validated and compared using the meteorological stations and ground-measured SD over the QTP at the same time. The results show that the root-mean-square error (RMSE) of the novel model developed in this study is approximately 5 cm, which is much better than the accuracy of the AMSR2 SD product (11-13 cm) released by the Japan Aerospace Exploration Agency. When the ground-measured SD is less than 30 cm, the RMSE and the mean absolute error of the developed model are below 7 and 6 cm, respectively, and the BIAS is approximately 1 cm. The SD inversion results are poor in the western part of the plateau due to the complex terrain and thick snow cover, and its RMSE is greater than 15 cm. In conclusion, this novel SD inversion model is more applicable and accurate than the AMSR2 SD products.
Moderate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algorithms were developed using a linear regression model (LR), a linear spectral mixture analysis model (LSMA) and a back-propagation artificial neural network model (BP-ANN) based on MODIS data (version 006) and unmanned aerial vehicle (UAV) data. The accuracies of the three models were validated against Landsat 8 Operational Land Imager (OLI) snow-cover maps (Landsat 8 FSC) and compared with the MODIS global FSC product (MOD10A1 FSC, version 005) for the purpose of finding the optimal algorithm for FSC extraction for the Tibetan Plateau. The results showed that (1) the overall retrieval results of the LR and BP-ANN models based on MODIS and UAV data were relatively similar to the OLI snow-cover maps; the accuracy and stability were greatly improved, with even some reduction in errors; compared to the Landsat 8 FSC, the correlation coefficients (r) were 0.8222 and 0.8445 respectively and the root-mean-square errors (RMSEs) were 0.2304 and 0.2201, respectively. (2) The accuracy and stability of the fully constrained LSMA model using the pixel purity index (PPI) endmember extraction method based only on MODIS data suffered the worst performance of the three models; r was only 0.7921 and the RMSE was as large as 0.3485. There were some serious omission phenomena in the study area, specifically for the largest mean absolute error (MAE = 0.2755) and positive mean error (PME = 0.3411). (3) The accuracy of the MOD10A1 FSC product was much lower than that of the LR and BP-ANN models, although its accuracy slightly better that of the LSMA based on comprehensive evaluation of six accuracy indices. (4) The optimal model was the BP-ANN model with combined inputs of surface reflectivity data (R1–R7), elevation (DEM) and temperature (LST), which can easily incorporate auxiliary information (DEM and LST) on the basis of (R1–R7) during the relationship training period and can effectively improve the accuracy of snow area monitoring—it is the ideal algorithm for retrieving FSC for the Tibetan Plateau.
Multi-source remote sensing data were used to generate 500-m resolution cloud-free daily snow cover images for the Northern Hemisphere. Simultaneously, the spatial and temporal dynamic variations of snow in the Northern Hemisphere were evaluated from 2000 to 2015. The results indicated that (1) the maximum, minimum, and annual average snow-covered area (SCA) in the Northern Hemisphere exhibited a fluctuating downward trend; the variation of snow cover in the Northern Hemisphere had well-defined inter-annual and regional differences; (2) the average SCA in the Northern Hemisphere was the largest in January and the smallest in August; the SCA exhibited a downward trend for the monthly variations from February to April; and the seasonal variation in the SCA exhibited a downward trend in the spring, summer, and fall in the Northern Hemisphere (no pronounced variation trend in the winter was observed) during the 2000–2015 period; (3) the spatial distribution of the annual average snow-covered day (SCD) was related to the latitudinal zonality, and the areas exhibiting an upward trend were mainly at the mid to low latitudes with unstable SCA variations; and (4) the snow reduction was significant in the perennial SCA in the Northern Hemisphere, including high-latitude and high-elevation mountainous regions (between 35° and 50°N), such as the Tibetan Plateau, the Tianshan Mountains, the Pamir Plateau in Asia, the Alps in Europe, the Caucasus Mountains, and the Cordillera Mountains in North America.
Abstract. Through combining optical remote sensing snow cover products with passive microwave remote-sensing snow depth data, we produced a MODIS cloudless binary snow cover product and a 500-m spatial resolution snow depth product for December 2000 to November 2014. We used the synthesized products to analyze the temporal and spatial variation of the snow cover in China. The results indicated that in the past 14 years, the overall annual number of snow-covered days and average snow depth in China increased. The annual average snow-covered area did not change significantly, and the number of snow-covered days in summer in China decreased. The number of snow-covered days in the winter, spring, and fall seasons all increased. The average snow-covered area in the summer and winter seasons decreased, whereas the average snow-covered area in the spring and fall seasons increased. The average snow depth in the winter, summer, and fall seasons decreased. Only the average snow depth in spring increased. The spatial distribution of the increase and decrease in the annual average snow depth was highly consistent with that of the annual number of snow-covered days. The spatial distributions of the variation of the number of snow-covered days and the average snow depth of each season were also highly consistent. The regional differences in the snow cover variation in China were significant. The snow cover increased significantly in South and Northeast China, decreased significantly in Xinjiang, increased in the southwest edge and southeast of the Tibetan Plateau, and mainly decreased in the north and northwest regions of the plateau.
Abstract. Based on a snow depth (SD) dataset retrieved from meteorological stations, this experiment explored snow indices including SD, snow covered days (SCDs), and snow phenology variations in China from 1952 to 2012. The results indicated that the snow in China exhibits regional differences, and the snow cover is mainly concentrated in three snow cover areas in Northeast China, northern Xinjiang and the Tibetan Plateau. In China, the annual average SD showed an increasing trend, and the increases in the average snow depth (SDaverage), cumulative snow depth (SDoverall) and maximum snow depth (SDmax) reached 0.04 cm, 0.05 cm and 0.07 cm per decade, respectively. The significant increases were mainly concentrated in areas higher than 40° N latitude, especially in Northeast China. The SDaverage, SDoveralland SDmax jump points are mainly in 1956, 1957, 1978, and 1987. In the first main period, the SDoverall oscillation in China is relatively stable, and its average period is approximately 13 years. The SCDs showed an increasing trend, with an increase of 0.5 days per decade. The significant increases in SCDs were also concentrated in areas higher than 40° N latitude, especially in Northeast China.However, in the Tibetan Plateau, the decrease in the SCDs reached 0.1 days per decade. In snow phenology, the snow duration days (SDDs) of China decreased, and 17.4 % of the meteorological stations showed significant decreasing trends. This result is mainly caused by the postponement of the snow onset date (SOD) and the advancement of the snow end date (SED). Geographical factors, including latitude, longitude and altitude, affect snow cover distribution directly and indirectly. The squared multiple correlations of SDDs and SCDs are greater than 0.9. Among the effects of SDDs and SCDs, the largest standardized total effect is from altitude on the SDDs, and the effect reaches 0.8.
Abstract. By combining optical remote sensing snow cover products with passive microwave remote sensing snow depth (SD) data, we produced a MODIS (Moderate Resolution Imaging Spectroradiometer) cloudless binary snow cover product and a 500 m snow depth product. The temporal and spatial variations of snow cover from December 2000 to November 2014 in China were analyzed. The results indicate that, over the past 14 years, (1) the mean snow-covered area (SCA) in China was 11.3 % annually and 27 % in the winter season, with the mean SCA decreasing in summer and winter seasons, increasing in spring and fall seasons, and not much change annually; (2) the snow-covered days (SCDs) showed an increase in winter, spring, and fall, and annually, whereas they showed a decrease in summer; (3) the average SD decreased in winter, summer, and fall, while it increased in spring and annually; (4) the spatial distributions of SD and SCD were highly correlated seasonally and annually; and (5) the regional differences in the variation of snow cover in China were significant. Overall, the SCD and SD increased significantly in south and northeast China, and decreased significantly in the north of Xinjiang province. The SCD and SD increased on the southwest edge and in the southeast part of the Tibetan Plateau, whereas it decreased in the north and northwest regions.
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