Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960

In this paper,we describe and analyze two datasets entitled“Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021”and“Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010”.These datasets provide the homogenised monthly and daily mean(TG),minimum(TN),and maximum(TX)temperature and precipitation(RR)records since 1960 at 366 stations in China and 56stations in Greece.The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720.For China,the regional mean annual TG,TX,TN,and RR series during 1960–2021 showed significant warming or increasing trends of 0.27℃(10 yr)^(-1),0.22℃(10 yr)^(-1),0.35℃(10 yr)^(-1),and 6.81 mm(10 yr)-1,respectively.Most of the seasonal series revealed trends significant at the 0.05level,except for the spring,summer,and autumn RR series.For Greece,there were increasing trends of 0.09℃(10 yr)-1,0.08℃(10 yr)^(-1),and 0.11℃(10 yr)^(-1)for the annual TG,TX,and TN series,respectively,while a decreasing trend of–23.35 mm(10 yr)^(-1)was present for RR.The seasonal trends showed a significant warming rate for summer,but no significant changes were noted for spring(except for TN),autumn,and winter.For RR,only the winter time series displayed a statistically significant and robust trend[–15.82 mm(10 yr)^(-1)].The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses.

Diversity on the Interannual Variations of Spring Monthly Precipitation in Southern China and the Associated Tropical Sea Surface Temperature Anomalies

There is a continuous and relatively stable rainy period every spring in southern China(SC).This spring precipitation process is a unique weather and climate phenomenon in East Asia.Previously,the variation characteristics and associated mechanisms of this precipitation process have been mostly discussed from the perspective of seasonal mean.Based on the observed and reanalysis datasets from 1982 to 2021,this study investigates the diversity of the interannual variations of monthly precipitation in spring in SC,and focuses on the potential influence of the tropical sea surface temperature(SST)anomalies.The results show that the interannual variations of monthly precipitation in spring in SC have significant differences,and the correlations between each two months are very weak.All the interannual variations of precipitation in three months are related to a similar western North Pacific anomalous anticyclone(WNPAC),and the southwesterlies at the western flank of WNPAC bring abundant water vapor for the precipitation in SC.However,the WNPAC is influenced by tropical SST anomalies in different regions each month.The interannual variation of precipitation in March in SC is mainly influenced by the signal of El Nino-Southern Oscillation,and the associated SST anomalies in the equatorial central-eastern Pacific regulate the WNPAC through the Pacific-East Asia(PEA)teleconnection.In contrast,the WNPAC associated with the interannual variation of precipitation in April can be affected by the SST anomalies in the northwestern equatorial Pacific through a thermally induced Rossby wave response.The interannual variation of precipitation in May is regulated by the SST anomalies around the western Maritime Continent,which stimulates the development of low-level anomalous anticyclones over the South China Sea and east of the Philippine Sea by driving anomalous meridional vertical circulation.

Quantum Atmospheric Biophysics: A Comparison of Four Weather Stations in India on Average Monthly Temperatures Since 1892 and Forecasts to 2150

The identification method revealed asymmetric wavelets of dynamics, as fractal quanta of the behavior of the surface air layer at a height of 2 m, according to the average monthly temperature at four weather stations in India (Srinagar, Jolhpur, New Delhi and Guvahati). For Srinagar station, the maximum for all years is observed in July, for Jolhpur and New Delhi stations it shifts to June, and for Guvahati it shifts to August. With a high correlation coefficient of 0.9659, 0.8640 and 0.8687, a three-factor model of the form was obtained. The altitude, longitude and latitude of the station are given sequentially. The hottest month for Srinagar over a period of 130 years is in July. At the same time, the temperature increased from 23.4 °C to 24.2 °C (by 3.31%). A noticeable decrease in the intensity of heat flows in June occurred at Jolhpur (over 125 years, a decrease from 36.2 °C to 33.3 °C, or by 8.71%) and New Delhi (over 90 years, a decrease from 35.1 °C to 32.4 °C, or by 7.69%). For almost 120 years, Guvahati has experienced complex climate changes: In 1902, the hottest month was July, but in 2021 it has shifted to August. The increase in temperature at various stations is considered. At Srinagar station in 2021, compared to 1892, temperatures increased in June, September and October. Guvahati has a 120-year increase in December, January, March and April. Temperatures have risen in February, March and April at Jolhpur in 125 years, but have risen in February and March at New Delhi Station in 90 years. Despite the presence of tropical evergreen forests, the area around Guvahati Station is expected to experience strong warming.

Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine

Rapid and accurate access to large-scale,high-resolution crop-type distribution maps is important for agricultural management and sustainable agricultural development.Due to the limitations of remote sensing image quality and data processing capabilities,large-scale crop classification is still challenging.This study aimed to map the distribution of crops in Heilongjiang Province using Google Earth Engine(GEE)and Sentinel-1 and Sentinel-2 images.We obtained Sentinel-1 and Sentinel-2 images from all the covered study areas in the critical period for crop growth in 2018(May to September),combined monthly composite images of reflectance bands,vegetation indices and polarization bands as input features,and then performed crop classification using a Random Forest(RF)classifier.The results show that the Sentinel-1 and Sentinel-2 monthly composite images combined with the RF classifier can accurately generate the crop distribution map of the study area,and the overall accuracy(OA)reached 89.75%.Through experiments,we also found that the classification performance using time-series images is significantly better than that using single-period images.Compared with the use of traditional bands only(i.e.,the visible and near-infrared bands),the addition of shortwave infrared bands can improve the accuracy of crop classification most significantly,followed by the addition of red-edge bands.Adding common vegetation indices and Sentinel-1 data to the crop classification improved the overall classification accuracy and the OA by 0.2 and 0.6%,respectively,compared to using only the Sentinel-2 reflectance bands.The analysis of timeliness revealed that when the July image is available,the increase in the accuracy of crop classification is the highest.When the Sentinel-1 and Sentinel-2 images for May,June,and July are available,an OA greater than 80%can be achieved.The results of this study are applicable to large-scale,high-resolution crop classification and provide key technologies for remote sensing-based crop classification in small-scale agricultural areas.

Evaluation on monthly sea surface wind speed of four reanalysis data sets over the China seas after 1988

This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.

Forecasting method of monthly wind power generation based on climate model and long short-term memory neural network

Predicting wind power gen eration over the medium and long term is helpful for dispatchi ng departme nts,as it aids in constructing generation plans and electricity market transactions.This study presents a monthly wind power gen eration forecast!ng method based on a climate model and long short-term memory(LSTM)n eural n etwork.A non linear mappi ng model is established between the meteorological elements and wind power monthly utilization hours.After considering the meteorological data(as predicted for the future)and new installed capacity planning,the monthly wind power gen eration forecast results are output.A case study shows the effectiveness of the prediction method.

Overview of global monthly surface temperature data in the past century and preliminary integration

This paper analyzes the status of existing resources through extensive research and international cooperation on the basis of four typical global monthly surface temperature datasets including the climate research dataset of the University of East Anglia(CRUTEM3), the dataset of the U.S. National Climatic Data Center(GHCN-V3), the dataset of the U.S. National Aeronautics and Space Administration(GISSTMP), and the Berkeley Earth surface temperature dataset(Berkeley). China's first global monthly temperature dataset over land was developed by integrating the four aforementioned global temperature datasets and several regional datasets from major countries or regions. This dataset contains information from 9,519 stations worldwide of at least 20 years for monthly mean temperature, 7,073 for maximum temperature, and 6,587 for minimum temperature. Compared with CRUTEM3 and GHCN-V3, the station density is much higher particularly for South America, Africa,and Asia. Moreover, data from significantly more stations were available after the year 1990 which dramatically reduced the uncertainty of the estimated global temperature trend during 1990e2011. The integrated dataset can serve as a reliable data source for global climate change research.

Monthly Variations of Atmospheric Circulations Associated with Haze Pollution in the Yangtze River Delta and North China

Haze pollution in early winter(December and January) in the Yangtze River Delta(YRD) and in North China(NC)are both severe;however, their monthly variations are significantly different. In this study, the dominant large-scale atmospheric circulations and local meteorological conditions were investigated and compared over the YRD and NC in each month. Results showed that the YRD(NC) is dominated by the so-called Scandinavia(East Atlantic/West Russia)pattern in December, and these circulations weaken in January. The East Asian December and January monsoons over the YRD and NC have negative correlations with the number of haze days. The local descending motion facilitates less removal of haze pollution over the YRD, while the local ascending motion facilitates less removal of haze pollution over NC in January, despite a weaker relationship in December. Additionally, the monthly variations of atmospheric circulations showed that adverse meteorological conditions restrict the vertical(horizontal) dispersion of haze pollution in December(January) over the YRD, while the associated local weather conditions are similar in these two months over NC.

Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type

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Monthly admissions for heart failure (HF)— Environmental links

In recent years the role of HF in the outcomes, cost of treatment in cardiology is raising. Concomitantly a number of studies were published demonstrating connections of many cardiac events with Space Weather Activity-Solar, Geomagnetic, Cosmic Ray (Neutron) activity levels. The aim of this study was to study links of timing of hospital admissions for HF with season and space weather components. Patients and Methods: monthly admissions of male and female patients for HF in two hospitals of Rabin Medical Center for years 2000-2012 were the subject of the study. 76,601 patient were included, 42,293 men, 34,308 woman. The cosmophysical data from USA, Russia and Finland were used. Results: Monthly average number of admissions for HF: 491.0 ± 82.4, 271.1 ± 46.75 for men and 219.9 ± 39.8 for woman. Gender admissions strongly correlated. Monthly admission for HF number differed by 2.2 - 2.5 times. Minimal admissions were in August, September;maximal—in January, February, December and March (according to numbers). It was a significant inverse correlation of monthly HF admissions with monthly solar activity and GMA indices and correlation with CRA (neutron) activity. Conclusion: Monthly admissions number for HF is fluctuated by season of the year, depending on gender and related to monthly solar and Cosmic Ray (Neutron) activity level. Gender differences in HF exacerbation may be a component explaining gender differences in longevity.

Regional and Monthly Assessment of Possible Sunshine Duration in Pakistan: A Geographical Approach

The monthly possible sunshine hours have been simulated separately for all the months of the year with the help of ArcGIS. The results are evident that the geography of the area plays a pivotal role in giving shape to spatial distribution of monthly possible sunshine duration (PSD). The landforms distributions and latitudinal extent are the major geographical factors behind the spatial-temporal distribution of monthly PSD. Maps of all the months depict different sunshine hours that vary from region to region in Pakistan. The maximum difference in PSD was noticed between lofty mountains in the north and Indus Plains. In addition, the variation is phenomenal from January to August and vice versa. This sort of study based on spatial modeling is significant in Pakistan where we lack appropriate ground observed data of PSD.

Regional and Monthly Assessment of Extraterrestrial Solar Radiations in Pakistan

The monthly extraterrestrial solar radiations (ESR) have been simulated separately for all the months of the year. The subtropical location and distribution of mountains and their height determine the spatial distribution and amount of ESR in Pakistan. The mountains, piedmonts, enclosed valleys and plains show distinct diversity of ESR values. The assessment acknowledged that countries like Pakistan with ever increasing demand of energy receive sufficient amount of ESR that could be linked with solar irradiance where development of solar energy has great potential. The simulation was done with the help of ArcGIS based on distributed modeling.

Trend Analysis in Annual and Monthly Pan Evaporation and Pan Coefficient in the Context of Climate Change in Togo

Trend analysis was performed for the long-term measured pan evaporation and estimated pan coefficient for 4 meteorological stations during 1976-2011 in Togo. Measured pan evaporation was recorded at four meteorological stations in Togo for the global period of 1976 to 2011 at Lome, Tabligbo, Atakpame, and Sokode. ETo was estimated using the Penman-Monteith model. The Mann-Kendall test was used for trend analysis. The results showed that annual Epan varied from 1803 to 2081 mm at Lome, from 1294 to 1496 mm at Tabligbo, from 1605 to 1974 mm at Atakpame and from 1839 to 1990 mm at Sokode. It had significant increasing trend at Lome, Tabligbo, and Sokode and a negative trend at Atakpame. Monthly Epan varied from 137 to 197 mm at Lome, 89 to 149 mm at Tabligbo, 137 to 214 mm at Atakpame and from 137 to 190 mm at Sokode. At Lome, Kpan varied from 0.61 to 1.17 and averaged 0.81. At Tabligbo, Kpan varied from 0.59 to 0.98 and averaged 0.75. At Atakpame, Kpan varied from 0.5 to 2.0 and averaged 1.12. At Sokode, Kpan varied from 0.43 to 1.92 and averaged 0.98. Monthly mean Kpan is recommended for use in hydrological studies, irrigation scheduling and water management in Togo.

Frequency of Monthly Ejaculation at Sexual Intercourse, DNA Fragmentation Index and Fertilization Rate among Sperm Donors and Fertility-Challenged Men in Sub-Saharan Africa: Paper 1

Introduction: In humans, the ideal ejaculation frequency depends on age, exercise and sexual potential. Natural ejaculation frequency balances the testosterone levels in the body. Materials and Methods: Semi-structured questionnaire was used to collect information from subjects. Semen samples were collected from sperm donors and sub-fertile men who presented for infertility challenges. Processing and analysis of semen samples were done according to World Health Organization guidelines. Sperm DNA fragmentation was evaluated using the Halosperm&#174;?kit. Results: A total of 114 subjects, including 19 sperm donors and 95 sub-fertile males were studied. There was a significant difference (t =?&#8722;5.96, P-value = 0.00001) in the mean [±sd] age of sperm donors (30.8 [8.1]) and that of sub-fertile men (42.3 [76]). There was a significant difference (t=?&#8722;4.10, P-value = 0.0005) in the mean monthly ejaculation during sexual intercourse (MESI) among sperm donors with DNA fragmentation index &#8722;2.20, P-value = 0.02) in MESI among sub-fertile men aged ≥40 years (8.9 [4.6]) than among those aged &#8722;0.67, SE = 0.28, t = &#8722;2.40, P-value = 0.02, 95% CI: &#8722;1.24,?&#8722;0.10). Conclusion: In men < 40 years, fewer MESI did not worsen the DFI. A higher number of professionals such as doctors, lawyers and engineers, reported lower monthly frequency of sexual ejaculations compared to entrepreneurs. Entrepreneurs and non-smokers had the highest frequencies of MESI.

Monthly Forecast of Indian Southwest Monsoon Rainfall Based on NCEP’s Coupled Forecast System

The monthly forecast of Indian monsoon rainfall during June to September is investigated by using the hindcast data sets of the National Centre for Environmental Prediction (NCEP)’s operational coupled model (known as the Climate Forecast System) for 25 years from 1981 to 2005 with 15 ensemble members each. The ensemble mean monthly rainfall over land region of India from CFS with one month lead forecast is underestimated during June to September. With respect to the inter-annual variability of monthly rainfall it is seen that the only significant correlation coefficients (CCs) are found to be for June forecast with May initial condition and September rainfall with August initial conditions. The CFS has got lowest skill for the month of August followed by that of July. Considering the lower skill of monthly forecast based on the ensemble mean, all 15 ensemble members are used separately for the preparation of probability forecast and different probability scores like Brier Score (BS), Brier Skill Score (BSS), Accuracy, Probability of Detection (POD), False Alarm Ratio (FAR), Threat Score (TS) and Heidke Skill Score (HSS) for all the three categories of forecasts (above normal, below normal and normal) have been calculated. In terms of the BS and BSS the skill of the monthly probability forecast in all the three categories are better than the climatology forecasts with positive BSS values except in case of normal forecast of June and July. The “TS”, “HSS” and other scores also provide useful probability forecast in case of CFS except the normal category of July forecast. Thus, it is seen that the monthly probability forecast based on NCEP CFS coupled model during the southwest monsoon season is very encouraging and is found to be very useful.

Study of monthly variations in primary production and their relationships with environmental factors in the Daya Bay based on a general additive model

In this study, the horizontal and vertical distribution of primary production(PP) and its monthly variations were described based on field data collected from the Daya Bay in January–December of 2016. The relationships between PP and environmental factors were analyzed using a general additive model(GAM). Significant seasonal differences were observed in the horizontal distribution of PP, while vertical distribution showed a relatively consistent unimodal pattern. The monthly average PP(calculated by carbon) ranged from 48.03 to 390.56 mg/(m^2·h),with an annual average of 182.77 mg/(m^2·h). The highest PP was observed in May and the lowest in November.Additionally, the overall trend in PP was spring>summer>winter>autumn, and spring PP was approximately three times that of autumn PP. GAM analysis revealed that temperature, bottom salinity, phytoplankton, and photosynthetically active radiation(PAR) had no significant relationships with PP, while longitude, depth, surface salinity, chlorophyll a(Chl a) and transparency were significantly correlated with PP. Overall, the results presented herein indicate that monsoonal changes and terrestrial and offshore water systems have crucial effects on environmental factors that are associated with PP changes.

Neuroid BP-type Model Applied to the Study of Monthly Rainfall Forecasting

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A Nonlinear Time－lag Differential Equation Model for Predicting Monthly Precipitation

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