Henan Institute of Meteorological Science

Zhengzhou, China

Henan Institute of Meteorological Science

Zhengzhou, China
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Cheng L.,Henan Institute of Meteorological science | Liu R.-H.,CMA Henan Key Laboratory of Agrometeorological Ensuring and Applied Technique | Liu R.-H.,Henan Institute of Meteorological science
Chinese Journal of Ecology | Year: 2012

Continuous rain during florescence has direct impact on the high and stable yielding of summer maize, while the study of disaster risk zoning can provide basis for the management of disaster risk. By using the daily weather data in the period from heading to spinning stage of summer maize and the yield data of summer maize in Henan Province from 1961 to 2010, this paper statistically calculated the average occurrence frequency and scope of continuous rain during the florescence of summer maize, established the series of disaster risk intensity of continuous rain, and extracted the yield loss rate caused by continuous rain with the method of Lagrange' s interpolation. Finally, the risk zoning index for continuous rain was set up to make zoning of continuous rain during florescence of summer maize in the Province. The results showed that the relatively low risk area of continuous rain during the florescence of summer maize with a risk index below 0. 25 was mainly distributed in the most part of north of Yellow River, local part of middle Henan, and east part of Nanyang basin, occupying about 33.3% of the whole Province, whereas the high risk area with a risk index higher than 0. 5 was in the north part of north Henan and most part of east Henan and south Huaihe River, occupying about 14. 8% of the whole Province.

Yang F.,China Meteorological Administration Training Center | Zhu Y.,China Meteorological Administration Training Center | Liu W.,Henan Institute of Meteorological Science
Journal of Natural Disasters | Year: 2013

Based on the index of the occurrence of dryhot wind in winter wheat region, an index of its occurrence intensity was established and the occurrence rule of dry-hot wind was inverted for the main winter wheat producing areas in the North China Plain since 1961. According to the results, the winter wheat area of the middle and south of Hebei, the west and north of Shandong and the north of Henan are the high risk zones of dry-hot wind, and the occurrence has reduced since the beginning of 1980' s. Thus, using the winter wheat observatory meteorological data, production structure material, yield, growth phase and other data, a dry-hot wind risk assessment model was established after the construction of the dry-hot wind risk evaluation index system that cover the dry-hot wind strength risk index and integrated disaster resisting capability index of the winter wheat. The dry-hot risk in main winter wheat producing areas of north China was then assessed using the model, and the dry-hot wind risk zoning assessment map was made for the areas. Assessment results show that, southeast of Hebei and northwest of Shandong are areas with high risk of the dry-hot wind influence, while south of Henan, east of Shandong and Hebei are low risk areas.

Wang L.,Nanjing University of Information Science and Technology | Chen H.,Henan Institute of Meteorological science | Li Q.,Nanjing University of Information Science and Technology | Yu W.,Henan Institute of Meteorological science
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2010

Of many environmental factors, the climate is the most important and active factor influencing plant phenology and its changes. In this paper, we first of all examine the relationship among the plant phenology, the climate and the climate change. Since numerical modeling is an important tool to quantify this relationship, we have further documented the recent research advances at home and abroad in areas of plant phenology and phenology simulation. It is shown that among climate variables, temperature is the most important factor to impact on the plant phenology. When water become a stress factor, its effect on the phenology is also significant. During the recent 50 years, the worldwide plant phenologies tend to have spring phenology advance and autumn phenology delay or slight delay, resulting in the growing season prolong for most plants. As a result of global warming, the temperature rising in winter and spring mainly leads to the spring phenology advance and the growing season prolong thereby. To further understand quantitative relationships between climate forcing and phenology response, we suggest further researches to be conducted in the areas such as, but not limited to, phenology mechanism, phenology relationships with climates and their changes,phenology modeling, and remote sensing applications.

Ma J.,China Agricultural University | Ma J.,CAS Institute of Atmospheric Physics | Liu Y.,China Agricultural University | Yang X.,China Agricultural University | And 3 more authors.
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2010

In the past few decades, significant changes of climate resources had taken place across the North China Plain (NCP), corresponding to global climate changes. Climate change has caused far-reaching impacts to agricultural production in the North China Plain, where is one of the most important food production regions in China. In this study, characteristics of regional distribution of climate resources during the five different decades from 1961 to 2007 were analyzed by using daily weather data from 59 typical agro-meteorological stations located the North China Plain. The major results showed: (1) heat resources in the North China Plain were significantly enriched during the study period due to global climatic warming, such as ≤0°C and ≤10°C acumulative air temperature markedly increased, specially, in north and east. (2) Annual precipitation decreased about 20 mm per decade from 1961 to 2007. Seasonal precipitation in summer and autumn also decreased with rate ranging from 25 mm/10 a to 40 mm/10 a, however, slightly increased in spring and winter. In addition, yearly mean of reference crop evapotranspiration (ET0) was declined in the North China Plain. The decreasing rate of rainfall was a little more than that of ET0. (3) Sunshine hours in the North China Plain showed a significant declination, especially in large and middle size urban area.

Xue C.,Key Laboratory of Agrometeorological Safeguard and Applied Technique of CMA Henan Province | Xue C.,Henan Institute of Meteorological Science | Ma Z.,Henan Institute of Meteorological Science | Hu C.,Henan Institute of Meteorological Science
Journal of Natural Disasters | Year: 2016

Drought is the main agro-meteorological disaster to restrict the summer maize production in the Huang-Huai-Hai area. It is of great important to clarify the spatiotemporal distribution law of agricultural drought in main food production area for the prevention and reduction of disasters. Based on the crop water deficit index ( CWDI ) and drought grade index, the spatiotemporal change law of drought during the summer maize growing season in past 40 years was analyzed. Results show that, the CWDI and probability of drought are the largest at the sowing-emergence stage. Other than the sowing-emergence stage, probability of drought decreases with the increase of drought grade. The sowing-emergence stage is when the probability for the special drought grade was the highest. There was no significant temporal trend for CWDI at each stage, but there are large fluctuations between years, es-pecially at the mid-late stages. Drought is most serious in 1997 in the recent 40 years with long time duration and widely affected areas. Spatial distribution range of CWDI varies with the law of reduction-enlargement-reduction from 1971-1980 to 2001-2010. The drought was the most serious in 1991-2000 and the lightest in 2001-2010. From the spatial distribution, CWDI and drought grade basically present the gradually increasing trend from east to west and from south to north. The most part of Hebei, the north and west parts of Henan and mid-west of Shandong were the areas with high probability of drought. Due to the increasing fluctuations between years, extreme drought events would occur in a larger probability during the mid-late season of summer maize. It is strongly advised to strengthen the prediction and prevention of drought, especially in the north and west parts of Huang-Huai-Hai area.

Liu Y.,Key Laboratory of Agricultural Environment | Liu Y.,Chinese Academy of Agricultural Sciences | Liu Y.,China Agricultural University | Yang X.,China Agricultural University | And 2 more authors.
Regional Environmental Change | Year: 2014

El Niño-Southern Oscillation (ENSO) contributes to climate anomalies, especially those related to regional rainfall, which affect crop production. Although the North China Plain (NCP) is the most important agricultural production region in China, the impact of ENSO events on local climate and crop production has received only limited attention. Therefore, the impact of different phases of ENSO on local climate and production of winter wheat and summer maize, both rain fed and irrigated, was investigated at three sites using the agricultural production systems simulator model. Data on daily temperature, precipitation, and sunshine hours for 50 years (1956-2006) were analysed to build climate scenarios for three categories of ENSO: years with El Niño events, years with La Niña events, and neutral years. The pattern of climate change was generally similar across the three sites: annual precipitation decreased slightly and annual mean sunshine hours decreased significantly, whereas annual mean minimum temperature increased significantly, leading to a significant increase in mean air temperature. Precipitation decreased and temperature and sunshine hours increased in both El Niño and La Niña years but remained stable in neutral years. Under full irrigation, the probability of exceeding distribution that crop yield would be higher was not markedly affected (P > 0.05), although the yields in both El Niño and La Niña years differed markedly from those in neutral years, especially in maize. Under rain-fed conditions, the yield of maize was decreased greatly (P < 0.05), the probability distribution of such reduction being the highest in La Niña years at all the sites (P < 0.05). At the provincial level, yields from well-managed fields differed (P > 0.05) with the ENSO category: production of maize was more vulnerable than that of wheat in El Niño and La Niña years. El Niño and La Niña had similar effects on climatic variables across the NCP: low yields in El Niño and La Niña years due to lower precipitation and high yields in neutral years due to longer sunshine hours and additional irrigation. © 2013 The Author(s).

Li Y.,Zhengzhou University | Wang J.,Zhengzhou University | Li Y.,CMA Technologies, Inc. | Li Y.,Henan Institute of Meteorological science
Yaogan Xuebao/Journal of Remote Sensing | Year: 2016

Remote-sensing technology features and the environmental elements of surface complexity together determine mixed pixels in remote- sensing images. Many mature methods of hyper spectral mixed-pixel decomposition are available, but research on the multispectral decomposition of mixed pixels are rare. The purpose of this study is to decompose mixed pixels based on their multispectral imaging characteristics. Hyperspectral images with high spectral resolution may benefit from the spectral unmixing of end-members. By contrast, FY3 multispectral (MERSI) image shavea lower spectral resolution but a higher temporal resolution. Thus, MERSI-EVI time series is introduced in this paper to decompose mixed pixels. The basic parameters of the experiment areas are as follows: study area: Hebi City, Henan Province, China; data: 79 MERSI images acquired from May 1, 2013 to October 15, 2013 (89 days had no data) and a Landsat 8 OLI image of the year; purpose: extraction of 2013 corn acreage from the data images. First, the remote-sensing images were processed, and the support-vector-machine classification method was used to extract information on farmlands with the use of a Landsat 8 OLI image. Then, SG-filtered MERSI time-series images were used to calculate EVI; the EVI growth curves of the mixed pixels and the crop end-numbers were then generated. The end-members were determined by field investigation. Corn is the main crop in the area. A total of 14 corn end-members were evenly selected in the space. Then, using the traditional method, the 14 corn end-members were combined with other end-members for unmixing. Finally, the spectral angle matching (SAM) method was used to improve the accuracy of the decomposition and adaptively select the most similar corn end-member with mixed pixels. In this case, a growth curve was used instead of a spectral curve. The results of the traditional decomposition methods vary widely; the extracted corn acreage ranges from 191.90 km2 to 574.83 km2, whereas the generated corn acreage of the new decomposition method is 589.95 km2. The 2013 summer corn acreage in Hebi City is 780.39 km2. Thus, compared with the best result generated by the traditional methods, the relative accuracy of the new method is improved by 2%. This study shows that using vegetation growth curves to decompose mixed pixels is effective for multispectral images. Of course, this study focused on plains, where crop planting structure is relatively simple. For areas with complex geographical environments and/or planting structures, the performance of the proposed method has yet to be confirmed. © 2016, Science Press. All right reserved.

Guo P.,Henan Institute of Meteorological science
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010

Sea surface temperature (SST) is one of the most important variables related to the global ocean-atmosphere system, which play an important role in studies of air-sea heat exchange, upper ocean processes, and weather forecast. SST data are routinely measured from ships, buoys and offshore platforms. In this paper, the weekly 4 km resolution AVHRR SST data (1985-2006), the weekly 4 km resolution MODIS SST data (2002-2007) and the daily 25 km resolution AMSR-E SST data (2002-2007) are chosen for merging. These SST data are derived from different Remote Sensors with different spatial and temporal resolution. By merging these SST data, we can get a new SST product and obtain more information. The bayesian hierarchical model using Markov Chain Monte Carlo (MCMC) simulation methods was used to merging the thermal infrared MODIS SST data and passive microwave AMSR-E SST data. The results show that merged SST data have a better completeness than MODIS SST and AMSR-E SST products. Comparing merged SST data with drift buoy SST, the validation result shows that the bias is 0.32118K and RMSE is 0.8026K. © 2010 Copyright SPIE - The International Society for Optical Engineering.

Meng L.,Wuhan University | Duan H.,Wuhan University | Huang C.,Wuhan University | Li Y.,Henan Institute of Meteorological science
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2014

Based on the XML data streaming transformation technology, a streaming transformation method is proposed for transforming GML to GeoOWL. Firstly, an element mapping transformation model is built by analyzing the mapping between GML elements and GeoOWL elements, then it is described using STX, finally the method is implemented using streaming transformation technology. Experiments show that our method is accurate, efficient and less memory is consumed, thus suitable for large GML file transformation.

Li Y.,CMA Henan Key Laboratory of Agrometeorological Support | Li Y.,Henan Institute of Meteorological science | Chen H.,CMA Henan Key Laboratory of Agrometeorological Support
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

In recent years, remote sensing images obtained by different types of optical sensors from a ground platform are applied to crop-weed discrimination and serve variable-rate technology in precision agriculture. Classification accuracy in remote sensing is influenced by spatial scale, so choosing the optimal spatial scale can be helpful for field data acquisition. Influences of spatial scale on classification accuracy in remote sensing are mainly originated from two factors: one factor is mixed-pixel and the other factor is spectral variability. Both aggravated mixed pixel caused by a larger spatial scale and aggravated spectral variability caused by a smaller spatial scale will result in classification accuracy reduction in remote sensing. For geographic entities in remote sensing images have inherent spatial attribute and spectral attribute, a spatial scale exists which can minimize the net effect of both mixed-pixel and spectral variability. Under this spatial scale, pixels can have optimal spectral identifiability. An approach for the selection of optimal spatial scale using a spectral angle mapper to measure the net effect of both mixed-pixel and spectral variability was proposed for crop-weed discrimination. The basic thinking of optimal spatial scale selection based on spectral angle mapper is as follows: using the average spectra calculated from a great amount of pure pixels belonging to one kind of ground object as the reference spectra for this kind of ground object, the spectra of each pixel could be regarded as the sum of its reference spectra and the net effect of mixed-pixel and spectral variability. Then, the spectral angle between the pixel spectra under different spatial resolutions and its reference spectra might be calculated to measure the net effect of mixed-pixel and spectral variability. The pixel will have optimal spectral identifiability when the net effect is least, and in this case, the spatial scale is the optimal scale. The proposed approach was realized in one field image. The geographic entities in the image were objectified. The optimal spatial scale was 0.48 cm by using the spatial scale selection method based on a spectral angle mapper. The relationship between the area and shape indexes of the target object and its optimal spatial scale was analyzed theoretically. For other field scenes, the finding can provide a reference for optimal spatial scale selection by calculating the area and shape indexes of plant objects.

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