scholarly journals Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation

2021 ◽  
Vol 13 (17) ◽  
pp. 3502
Author(s):  
Jingshan Lu ◽  
Jan Eitel ◽  
Jyoti Jennewein ◽  
Jie Zhu ◽  
Hengbiao Zheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Least Squares ◽  
Spectral Index ◽  
Rice Plant ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Potassium Content ◽  
Estimation Accuracy ◽  
Average Temperature ◽  
Daily Average

Potassium (K) plays a significant role in the formation of crop quality and yield. Accurate estimation of plant potassium content using remote sensing (RS) techniques is therefore of great interest to better manage crop K nutrition. To improve RS of crop K, meteorological information might prove useful, as it is well established that weather conditions affect crop K uptake. We aimed to determine whether including meteorological data into RS-based models can improve K estimation accuracy in rice (Oryza sativa L.). We conducted field experiments throughout three growing seasons (2017–2019). During each year, different treatments (i.e., nitrogen, potassium levels and plant varieties) were applied and spectra were taken at different growth stages throughout the growing season. Firstly, we conducted a correlation analysis between rice plant potassium content and transformed spectra (reflectance spectra (R), first derivative spectra (FD) and reciprocal logarithm-transformed spectra (log [1/R])) to select correlation bands. Then, we performed the genetic algorithms partial least-squares and linear mixed effects model to select important bands (IBs) and important meteorological factors (IFs) from correlation bands and meteorological data (daily average temperature, humidity, etc.), respectively. Finally, we used the spectral index and machine learning methods (partial least-squares regression (PLSR) and random forest (RF)) to construct rice plant potassium content estimation models based on transformed spectra, transformed spectra + IFs and IBs, and IBs + IFs, respectively. Results showed that normalized difference spectral index (NDSI (R1210, R1105)) had a moderate estimation accuracy for rice plant potassium content (R2 = 0.51; RMSE = 0.49%) and PLSR (FD-IBs) (R2 = 0.69; RMSE = 0.37%) and RF (FD-IBs) (R2 = 0.71; RMSE = 0.40%) models based on FD could improve the prediction accuracy. Among the meteorological factors, daily average temperature contributed the most to estimating rice plant potassium content, followed by daily average humidity. The estimation accuracy of the optimal rice plant potassium content models was improved by adding meteorological factors into the three RS models, with model R2 increasing to 0.65, 0.74, and 0.76, and RMSEs decreasing to 0.42%, 0.35%, and 0.37%, respectively, suggesting that including meteorological data can improve our ability to remotely sense plant potassium content in rice.

scholarly journals Association Among Sentinel Surveillance, Meteorological Factors, and Infectious Disease in Gwangju, Korea

2021 ◽  
Author(s):  
You Hyun Joung ◽  
Taesu Jang ◽  
Jae Kyung Kim
Keyword(s):  
Infectious Diseases ◽  
Meteorological Factors ◽  
Climatic Factors ◽  
Meteorological Data ◽  
Sentinel Surveillance ◽  
E Coli ◽  
Average Temperature ◽  
Using Data ◽  
Strong Seasonality ◽  
Sentinel Surveillance System

Abstract Introduction: The outbreak of new infectious diseases is threatening human survival. Transmission of such diseases is determined by several factors, with climate being a very important factor. This study was conducted to assess the correlation between the occurrence of infectious diseases and climatic factors using data from the Sentinel Surveillance System and meteorological data from Gwangju, Jeollanam-do, Republic of Korea. Result The climate of Gwangju from June to September is humid, with this city having the highest average temperature, whereas that from December to February is cold and dry. Infection rates of Salmonella (Temperature: r = 0.710**; Relative humidity: r = 0.669**), E. coli (r = 0.617**; r = 0.626**), Rotavirus (r=-0.408**; r=-0.618**), Norovirus (r=-0.463**; r=-0.316**), Influenza virus (r=-0.726**; r=-0.672**), Coronavirus (r=-0.684**; r=-0.408**), and Coxsackievirus (r = 0.654**; r = 0.548**) have been shown to have a high correlation with seasonal changes, specifically in these meteorological factors. Discussion & Conclusions: Pathogens showing distinct seasonality in the occurrence of infection were observed, and there was a high correlation with the climate characteristics of Gwangju. In particular, viral diseases show strong seasonality, and further research on this matter is needed. Due to the current COVID-19 pandemic, quarantine and prevention have become important to block the spread of infectious diseases. For this purpose, studies that predicts infectivity through various types of data related to infection are important.

scholarly journals The Influence of Climate Conditions and Meteorological Factors on the Nutritional Value of Wheat (Triticum Aestivum L.) Used for Human and Animals Nutrition, in Romania

2021 ◽  
Vol 906 (1) ◽  
pp. 012019
Author(s):  
Ionela Hotea ◽  
Monica Dragomirescu ◽  
Olimpia Colibar ◽  
Emil Tirziu ◽  
Viorel Herman ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Meteorological Data ◽  
Triticum Aestivum L ◽  
Metabolizable Energy ◽  
Animal Nutrition ◽  
Annual Average ◽  
Average Temperature ◽  
Period 2 ◽  
Annual Average Temperature

Abstract Wheat (Triticum aestivum L.) is the basic cereal in human and animal nutrition. Every month, wheat is harvested somewhere in the world. In Romania, a country with a temperate-continental climate, the wheat is harvested between June and July, while the sowing is carried out between September and October. Climatic and meteorological factors during these periods can influence the nutritional quality of wheat. The aim of this study was to analyse the influence of annual average temperature and the amount of precipitate on the chemical composition and on the value of metabolizable energy of the wheat, respectively. The climatic and meteorological data used in this study come from NMA database. Were analysed the periods September 2017 - July 2018 (period 1, noted with 2018 - the year of harvesting) and September 2018 - July 2019 (period 2, noted with 2019 - the year of harvesting), respectively. For the chemical analysis, the NIR (Near InfraRed spectroscopy) method was used. The calculation of metabolizable energy was performed based on the ATWATER system, a system applicable to both human and animal nutrition. The statistical analysis of the climatic and meteorological data showed that the annual average temperature for period 1 was lower compared to the temperatures of period 2. Also, the precipitations were more abundant in period 1 compared to period 2. There were no significant statistical differences for any of the climatic and meteorological factors assayed during the analyzed periods. Following the statistical correlations between the nutrients studied by chemical analysis, for those 2 periods, significant differences were observed (p <0.001). The humidity of wheat grains harvested in 2018 was higher (average = 13.03%) compared to that of grains harvested in 2019 (average = 10.72%). The protein content was lower in 2018 (average = 10.02%) than in 2019 (average = 11.04%); and similar results were obtained for the fibre content (average 2018 = 2.17%; average 2019 = 2.96%). Also, the value of metabolizable energy was lower for wheat harvested in 2018 (average = 3517.90 kcal/kg) compared to 2019 (average = 3611.04 kcal/kg). In conclusion, the results of this study highlight the influence of temperature and precipitation on the chemical composition of wheat, thus having a direct impact on the nutritional quality of this grain for human and animal nutrition.

The role of temperature in reported chickenpox cases from 2000 to 2011 in Japan

2015 ◽  
Vol 143 (12) ◽  
pp. 2666-2678 ◽  
Author(s):  
K. HARIGANE ◽  
A. SUMI ◽  
K. MISE ◽  
N. KOBAYASHI
Keyword(s):  
Least Squares ◽  
Maximum Entropy Method ◽  
Least Squares Method ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Entropy Method ◽  
Spectral Lines ◽  
Power Spectral ◽  
Power Spectral Densities

SUMMARYAnnual periodicities of reported chickenpox cases have been observed in several countries. Of these, Japan has reported a two-peaked, bimodal annual cycle of reported chickenpox cases. This study investigated the possible underlying association of the bimodal cycle observed in the surveillance data of reported chickenpox cases with the meteorological factors of temperature, relative humidity and rainfall. A time-series analysis consisting of the maximum entropy method spectral analysis and the least squares method was applied to the chickenpox data and meteorological data of 47 prefectures in Japan. In all of the power spectral densities for the 47 prefectures, the spectral lines were observed at the frequency positions corresponding to the 1-year and 6-month cycles. The optimum least squares fitting (LSF) curves calculated with the 1-year and 6-month cycles explained the underlying variation of the chickenpox data. The LSF curves reproduced the bimodal and unimodal cycles that were clearly observed in northern and southern Japan, respectively. The data suggest that the second peaks in the bimodal cycles in the reported chickenpox cases in Japan occurred at a temperature of approximately 8·5 °C.

scholarly journals A retrospective study: meteorological factors and COVID-19

2020 ◽  
Author(s):  
Cai Chen ◽  
Xiyuan Li ◽  
Xiangwei Meng ◽  
Zhixiang Ma ◽  
Wei Li ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Correlation Coefficient ◽  
Meteorological Factors ◽  
Rank Correlation ◽  
Effect Of Temperature ◽  
Maximal Effect ◽  
Global Epidemic ◽  
Average Temperature ◽  
Daily Average ◽  
Daily Average Temperature

Abstract Background: With the outbreak of novel coronavirus, the global epidemic prevention form is severe. Purpose: This paper aimed to investigate the association between meteorological factors (temperature, precipitation and relative humidity) and the daily new cases in Wuhan. Methods: generalized linear model was built to evaluate the link between daily average temperature and the new cases COVID-19. Spearman rank correlation coefficient was used to investigate the association between temperature, relative humidity, precipitation and the daily new cases COVID-19. Result: The correlation coefficient for daily average temperature, relative humidity, precipitation and NCP were 0.11, -0.083 and 0.17, respectively. The maximal effect of temperature on the new cases NCP appeared on Lag0. Conclusion: The variation of temperature had an effect on the daily new cases.

scholarly journals Identifying high risk areas of Zika virus infection by meteorological factors in Colombia

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Lung-Chang Chien ◽  
Francisco Sy ◽  
Adriana Pérez
Keyword(s):  
At Risk ◽  
High Risk ◽  
Zika Virus ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Warning System ◽  
Zikv Infection ◽  
Total Rainfall ◽  
Average Temperature ◽  
Risk Areas

Abstract Background Several Zika virus (ZIKV) outbreaks have occurred since October 2015. Because there is no effective treatment for ZIKV infection, developing an effective surveillance and warning system is currently a high priority to prevent ZIKV infection. Despite Aedes mosquitos having been known to spread ZIKV, the calculation approach is diverse, and only applied to local areas. This study used meteorological measurements to monitor ZIKV infection due to the high correlation between climate change and Aedes mosquitos and the convenience to obtain meteorological data from weather monitoring stations. Methods This study applied the Bayesian structured additive regression modeling approach to include spatial interactive terms with meteorological factors and a geospatial function in a zero-inflated Poisson model. The study area contained 32 administrative departments in Colombia from October 2015 to December 2017. Weekly ZIKV infection cases and daily meteorological measurements were collected. Mapping techniques were adopted to visualize spatial findings. A series of model selections determined the best combinations of meteorological factors in the same model. Results When multiple meteorological factors are considered in the same model, both total rainfall and average temperature can best assess the geographic disparities of ZIKV infection. Meanwhile, a 1-in. increase in rainfall is associated with an increase in the logarithm of relative risk (logRR) of ZIKV infection of at most 1.66 (95% credible interval [CI] = 1.09, 2.15) as well as a 1 °F increase in average temperature is significantly associated with at most 0.79 (95% CI = 0.12, 1.22) increase in the logRR of ZIKV. Moreover, after controlling rainfall and average temperature, an independent geospatial function in the model results in two departments with an excessive ZIKV risk which may be explained by unobserved factors other than total rainfall and average temperature. Conclusion Our study found that meteorological factors are significantly associated with ZIKV infection across departments. The study determined both total rainfall and average temperature as the best meteorological factors to identify high risk departments of ZIKV infection. These findings can help governmental agencies monitor at risk areas according to meteorological measurements, and develop preventions in those at risk areas in priority.

scholarly journals STUDY ON DESERTIFICATION MONITORING FROM 2000 TO 2014 AND ITS DRIVING FACTORS THROUGH REMOTE SENSING IN NINGXIA，CHINA

2018 ◽  
Vol XLII-3 ◽  
pp. 2439-2447
Author(s):  
Z. Zhao ◽  
Q. Wu
Keyword(s):  
Remote Sensing ◽  
Relative Humidity ◽  
Grey Relational Analysis ◽  
Meteorological Data ◽  
Great Influence ◽  
Driving Factors ◽  
Average Temperature ◽  
Relational Analysis ◽  
Sunshine Hours ◽  
Grey Relational

Due to the implementation of national policy, the desertification in Ningxia has been gradually reduced, but the overall situation of desertification is still serious. Rainfall Use Efficiency(RUE) can make some improvement to the problem that the precipitation has a great influence on vegetation in arid area and fully reflect the dynamic characteristics of desertification. Using the MOD13Q1 data, land use classification map, as well as non-remote sensing data such as meteorological data and social statistics data, the paper carries out the evaluation of the status of desertification based on RUE through spatial trend analysis, gravity center migration model. The driving factors of desertification are quantitatively analyzed by using grey relational analysis. Our study demonstrated that RUE in most parts of Ningxia showed a trend of improvement, mainly located in central and southern Ningxia. The area where desertification occurred from 2000 to 2014 accounted for 7.79&amp;thinsp;%, mainly distributed in Helan Mountain, Liupan Mountain, Yinchuan Central. The proportion of desertification decreased gradually from 2005 to 2014, and the center of gravity of desertification had a tendency to migrate to northern Ningxia. By analyzing the driving factors, RUE had negative correlations with precipitation and relative humidity and there was no significant correlation between RUE and average temperature and sunshine hours. RUE was positively correlated with GDP, grain yield and number of sheep. On the basis of the results of grey relational analysis, it was found that sunshine hours, average temperature, relative humidity, population were the main influencing factors of desertification.

scholarly journals Associations between Meteorological Factors and Reported Mumps Cases from 1999 to 2020 in Japan

Epidemiologia ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 162-178
Author(s):  
Keiji Mise ◽  
Ayako Sumi ◽  
Shintaro Takatsuka ◽  
Shin-ichi Toyoda
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Time Series Data ◽  
Least Squares Method ◽  
Meteorological Factors ◽  
Vaccine Coverage ◽  
Meteorological Data ◽  
Spectral Lines ◽  
Series Data ◽  
Mean Temperature

The present study investigated associations between epidemiological mumps patterns and meteorological factors in Japan. We used mumps surveillance data and meteorological data from all 47 prefectures of Japan from 1999 to 2020. A time-series analysis incorporating spectral analysis and the least-squares method was adopted. In all power spectral densities for the 47 prefectures, spectral lines were observed at frequency positions corresponding to 1-year and 6-month cycles. Optimum least-squares fitting (LSF) curves calculated with the 1-year and 6-month cycles explained the underlying variation in the mumps data. The LSF curves reproduced bimodal and unimodal cycles that are clearly observed in northern and southern Japan, respectively. In investigating factors associated with the seasonality of mumps epidemics, we defined the contribution ratios of a 1-year cycle (Q1) and 6-month cycle (Q2) as the contributions of amplitudes of 1-year and 6-month cycles, respectively, to the entire amplitude of the time series data. Q1 and Q2 were significantly correlated with annual mean temperature. The vaccine coverage rate of a measles–mumps–rubella vaccine might not have affected the 1-year and 6-month modes of the time series data. The results of the study suggest an association between mean temperature and mumps epidemics in Japan.

scholarly journals Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 581 ◽  
Author(s):  
Yuanyuan Fu ◽  
Guijun Yang ◽  
Xiaoyu Song ◽  
Zhenhong Li ◽  
Xingang Xu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Least Squares ◽  
Aboveground Biomass ◽  
Regression Models ◽  
Digital Images ◽  
Predictive Performance ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Biomass Estimation ◽  
High Definition

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

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