The effects of climatic change on winter wheat yield in Shangqiu City are quantitatively analyzed with the predictive results of future climate change. The results show that winter wheat yield in Shangqiu City presented a fluctuated increase for overall trend. Principal component analysis indicates that air temperature, precipitation, evaporation and extreme temperatures are the main factors affecting winter wheat yield, and excessive evaporation and extremely-low temperatures are unfavorable for wheat production. The warm-and-wet climate in Shangqiu is beneficial for improvement of winter wheat production, while the cold-and-dry climate is unbeneficial.
Carbon to nitrogen ratio is the key factor of wheat growth. In this paper, the hyperspectral image（HSI）of environment and disaster monitoring and forecasting of small satellite constellation A (HJ-1A) in wheat turning green stage was acquired, and the in-situ wheat canopy spectral reflectance, leaf nitrogen content, leaf sugar content, as well as others physical and chemical parameters of winter wheat in Beijing were measured synchronously. The red edge parameters were extracted from the in-situ canopy spectral reflectance, and the correlation analysis between the red edge parameters and physical and chemical parameters of winter wheat were carried on. The models based on red edge position and red edge width to predict leaf sugar content and leaf nitrogen content respectively were constructed. Further, the red edge parameters were extracted from the HSI reflectance which simulated by inverted Gaussian model. Then, the models were used to predict the leaf nitrogen content, leaf sugar content in HSI. Finally, the map of carbon to nitrogen ratio was obtained combining with winter wheat planting map to realize the growth monitoring of winter wheat in Beijing.
In this paper, the analysis methods, which used to descript the winter wheat growing features, have been found of satellite remote sensing coupled with the data of LAI, dry matter weight, etc. the results showed that the revised rate of remote sensing to in-situ observation is different in different developmental stages of winter wheat. Mainly manifested in the following aspects (1) In the early stage of growth and development of winter wheat (in March), the leaf area index LAI of winter wheat is small, due to the impact of soil background, the winter wheat NDVI which retrieved from MODIS data (leaf area index LAI can be calculated from NDVI) are vary greatly from in-situ observations, the revised coefficient is relatively large. (2) In the rapid vegetative growth stage (April), the ground was completely covered by winter wheat, the influence of soil background decreased, and LAI which retrieved from remote sensing closing to the data in-situ observation accordingly and the revised coefficient is smaller than in the early stage of winter wheat. (3) The LAI decreased sharply in the later stage of winter wheat. So the LAI accuracy of remote sensing retrieval as well as reduced. The differences are largest between the remote sensing retrieval and in-situ observation, and the revised coefficient is largest in all growing stage.
Using experimental data on winter wheat from Bushland, Texas and Zhengzhou, Henan, analysis is performed of the distribution of its root system in 0-50 (50~100) cm soil depth, with the density of root length and its weight making up 57.7% (23.4%) and 66.7% (18.7%), respectively, thereby constructing the model of the roots growth and also by the distribution of the root system and its water absorption, study is undertaken of the appropriate depth of background field moisture for growing winter wheat.
Effects of soil water on crop growth and yield are performed on the changes of crop growing conditions and biomass growth. In this paper, long-term field experiment data at Zhengzhou Experiment Station were used to statistically analyze the relationships between crop growing conditions and biomass growth at current stage and soil water at previous stage. And the relationships between soil water and yield were also set up. Subsequently, optimum soil water and drought indexes were determined for different growth stages of winter wheat. All these results lay the foundation for dynamic evaluation of drought in winter wheat.
Winter wheat is mainly planted in water shortage area, such as North China and Northwest China. As a key field management measure, irrigation plays an important role in the production of winter wheat. This paper focuses on the improvement of regional winter wheat yield estimation technique in county scale by adjusting the irrigation management measure in crop growth model. The WOFOST (World Food Study) model was used by dividing the whole county into a number of EMUs (Elementary Mapping Units) and then running the model in each unit in sequence. While running, the measured soil moisture and LAI were used to rate the irrigation parameters. Finally, the calibrated irrigation parameters were used to run the model again. The results showed that the simulated winter wheat growth process was normal. During the whole growing period of winter wheat, the change trends of the time series of soil moisture and LAI were basically consistent with that of the measured. The precision of simulated yield was between 87.26 % and 98.68 % among the 5 units, and the average of the precision was 94.56 %. The precision of simulated winter wheat yield was well, and could meet the needs of winter wheat yield estimation in county-wide. This study may provide basis for estimating crop yield in regional area by using the crop growth model.
In order to acquire the spatial information of winter wheat harvest index (HI), depending on the crop growth profile of time-series MODIS-NDVI to calculate mean slope of NDVI curve and accumulated NDVI at stage of before anthesis and after anthesis, the authors made full use of remote sensing information and structured two parameters HI_(NDVI-k) and HI_(NDVI-SUM) according to the definition of HI of crop. Then relationships between the two parameters and field measured HI of winter wheat were established respectively. After validation of retrieved winter wheat HI, it was shown that the accuracy of the retrieved HI of winter wheat was high and satisfied at large scale in study region of Huanghuaihai Plain in China. The mean relative errors of the retrieved HI were 3.60% and 2.40% and RMSE were 0.04 and 0.02 respectively. It was proved that the method of structuring parameters of HI_(NDVI-k) and HI_(NDVI_SUM) and extracting harvest index for winter wheat based on time-series MODIS-NDVI was reasonable and feasible.
In order to rapidly and accurately acquire winter wheat growing information and nitrogen content, a non-destructive testing method was developed combined with multi-spectral imaging technique and remote sensing technology to research wheat growing and nutrition status. Firstly, a 2-CCD multi-spectral image collecting platform was developed to acquire visible image and NIR image synchronously, meanwhile, the canopy spectral reflectance and the nitrogen content of wheat leaves were measured and analyzed to research the characteristics of the canopy spectral reflectance. Secondly, using calibration panels the experiential linear calibration model was established between image gray value and spectral reflectance. Thirdly, NIR image was processed to segment wheat canopy from soil and then gray value of wheat leaves was achieved by image processing of Red, Green, and Blue channels. Finally, the gray value of wheat leaves was transformed into spectral reflectance by aforementioned experiential linear model, and the vegetation index were calculated and analyzed to research the winter wheat growing and nitrogen content status. Experiment results showed that it was reasonable to diagnose nitrogen content of winter wheat based on multi-spectral imaging system and experiential linear model. There existed remarkable correlation between vegetation index (NDVI, GNDVI) and nitrogen content of winter wheat, and the correlation coefficients (R~2) were 0.633 and 0.6.
An uneven growing winter wheat will be slower to reach full ground cover and will be lead to uneven yield and quality for cropland. The traditional investigation of crop uniformity is mainly depends on manpower. Remote sensing technique is a potentially useful tool for monitoring the crop uniformity status for it can provide an area global view for entire field within the crop growth season with scathelessness. The objective of this study was to use remote sensing imagery to evaluate the crop growth uniformity, as well as the yield and grain quality variation for a winter wheat study area. One Quickbird image on winter wheat booting stage was collected and processed to monitoring the uniformity of wheat growth. The results indicated that the spectrum parameters of Quickbird image can reflect the spatial uniformity of winter wheat growth in the study areas. Meanwhile the spatial uniformity of wheat growth in early stage can reflect the uniformity of yield and grain quality. The wheat growth information at the booting stage has strong positive correlations with yield, and strong negative correlation with grain protein. The correlation coefficient between OSAVI (optimized soil adjusted vegetation index) and wheat yield was 0.536. It was -0.531 for GNDVI (Greeness-normalized difference vegetation index) and grain protein content. The study also indicated that diverse spectrum parameters had different sensitivity to the wheat growth spatial variance. So it is feasible to use remote sensing data to investigate the crop growth and quality spatial uniformity.
Life cycle analysis method was used to establish an inventory of a winter wheat production system that employed the Soil Testing and Formulated Fertilization Program in Linqing county, China, after which the net resource conservation and emission reduction benefits were calculated, evaluated and compared to the winter wheat production system in a conventional fertilization area. The results revealed a great reduction in resource consumption and emissions of the winter wheat production system in the program demonstration area. From 2006 to 2010, the life cycle reduction potentials of eutrophication, and acidification potential per ton of winter wheat accounted for 12.09-30.31% and 1.40-4.52% of the relevant environmental impact potential per capita worldwide in 2000, respectively. The Program significantly decreased the environmental burdens of the winter wheat production system, and farmer's fertilization behavior tended to become rational.