Entity

Time filter

Source Type


Wang P.,China Agricultural University | Sun H.,China Agricultural University | Wang L.,China Agricultural University | Xie Y.,China Agricultural University | And 2 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2016

Vegetation temperature condition index (VTCI) combines the main parameters of normalized difference vegetation index (NDVI) and land surface temperature (LST), and is applicable to a more accurate monitoring of droughts in the Guanzhong Plain, Shaanxi, China. VTCI also provides a scientific basis for drought relief and crop yield estimation by using remotely sensed data. This study chose Guanzhong Plain as the study area, and was to combine the remote sensed VTCI and simulated soil surface moisture by the CERES-Wheat (Crop environment resource synthesis for wheat) model to get a high regional yield estimation accuracy by using the four-dimensional variational (4D-VAR) data assimilation approach. The improved analytic hierarchy process, the entropy method and the joint the two weighting methods were used to establish winter wheat yield estimation models by using the monitored VTCI and the assimilated ones respectively. The optimal model for estimating winter wheat yields in the study area from 2008 to 2014 was selected, and the measured wheat yield of the year 2011 was used to validate the accuracies of the optimal model. The results showed that no matter at the sampling sites or at the regional scale, the assimilated VTCIs were all better able to respond the monitored VTCIs and the surface moisture data, and the texture of assimilated VTCI images was better and more consistent with the regional drought distribution. Compared the yield estimation models with the monitored VTCIs, the accuracies of the yield estimation models with the assimilated VTCIs were improved, and the correlation coefficients of the optimal yield estimation model with the weighted VTCIs of 0.784 (P<0.001). The optimal yield estimation model was applied to estimate wheat yields in 29 counties of the Guanzhong Plain, and the results showed that except for the Pucheng County, the estimated yields' relative errors of other 28 counties in Guanzhong Plain were less than 15%, and the errors were less than 10% in 16 counties of Guanzhong Plain. In general, the average relative error of the estimated yields was 8.68%, and the root mean square error was 421.9 kg/hm2, indicating the optimal yield estimation model had a better performance. The yearly estimated yields from 2008 to 2014 were in an increasing trend with fluctuation in Guanzhong Plain. For the spatial distribution of the yields, the yields were the highest in the central of Guanzhong Plain, and the yields in the west were higher than those in the east. © 2016, Chinese Society of Agricultural Machinery. All right reserved. Source


Gong Z.-W.,Nanjing University of Information Science and Technology | Li L.-S.,Nanjing University of Information Science and Technology | Luo H.,Shaanxi Provincial Meteorological Bureau | Yao T.-X.,Nanjing University of Information Science and Technology
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | Year: 2010

The research on aggregation method for group decision making with grey interval information is given. The grey interval judgment information is firstly transformed to the equivalent three-tuple grey preference, and then, this three-tuple is regarded as a probability distribution. Based on the consistent relation between the probability of collective grey preference and the probability of individual grey preference, the optimal relative entropy models for aggregating the judgment information of group decision making is developed, and the corresponding decision making procedures are also proposed. The ability evaluation of integrated service for weather bureau is provided to show that the relative entropy aggregating method can effectively avoid the information distortion for decision making. Source


Wang P.,China Agricultural University | Sun H.,China Agricultural University | Xie Y.,China Agricultural University | Wang L.,China Agricultural University | And 2 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2016

Data assimilation (DA) provides a way for effective combination of model simulation and observation, and improves accuracy of winter wheat yield estimation. Among various DA methods, the particle filter (PF) is not constrained by the conditions of linear models and Gaussian error distribution, and receives more attention and application of DA. Currently, most researchers adopt single remotely sensed data source and single variable assimilation strategy, which cannot accurately reflect the interactive process among radiation, temperature and water, and limit the performance of data assimilation systems. To improve accuracy of winter wheat yield estimation, a particle filter algorithm was proposed, which was based on a sequential important sampling procedure of assimilating leaf area index (LAI) and vegetation temperature condition index (VTCI) retrieved from MODIS data into the CERES-Wheat model (Crop environment resource synthesis for wheat) to estimate winter wheat yield from 2008 to 2014 in Guanzhong Plain, Shaanxi, China. In order to determine effects of the assimilated variables on winter wheat yield estimation under different management practices, eight typical rainfed farming sites and four irrigation sites were selected, and the assimilated LAI or VTCI or both of them were used to establish winter wheat yield estimation models. The results showed that the assimilated LAI had good temporal and spatial continuity, and the sharp changing points of seasonal LAI were decreased after applying the particle filter assimilation algorithm. The peak and seasonal trend of the assimilated LAI were basically in agreements with those of the remotely sensed LAI, and the problem of low values of MODIS-LAI was solved to a certain degree after assimilation. The seasonal change of assimilated VTCI was in good agreement with those of both the remotely sensed VTCI and the simulated VTCI, and the assimilated VTCI was a good index for indicating crop water stress of winter wheat. These results suggested that the assimilation of LAI and VTCI might be preferable when the study areas were vulnerable to water stress. At the rainfed farming sites, the determination coefficient of the yield estimation model with assimilated LAI and VTCI was the highest as 0.531 (P<0.001), and the determination coefficients of the yield estimation models with assimilated LAI or VTCI were 0.428 and 0.475, respectively, which were both at the significance level of P<0.001. However, at the irrigation sites the determination coefficient of the yield estimation model with assimilated LAI was the highest as 0.539 (P<0.001), the coefficient of the yield estimation model with assimilated LAI and VTCI was 0.457 (P<0.01), and the coefficient of the yield estimation model with assimilated VTCI was the lowest as 0.243 (P<0.10). In conclusion, the LAI and crop water stress were the important factors that affected winter wheat yield in rainfed farming areas, while the LAI became the important factor in irrigation areas. The study could provide a reference for crop yield estimation by using data assimilation algorithms which combined multi-source remotely sensed variables with crop growth model. © 2016, Chinese Society of Agricultural Machinery. All right reserved. Source


Tian M.,China Agricultural University | Wang P.,China Agricultural University | Han P.,China Agricultural University | Zhang S.,Shaanxi Provincial Meteorological Bureau
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2013

Based on the time series of drought monitoring results of vegetation temperature condition index (VTCI), the seasonal autoregressive integrated moving average (SARIMA) models were applied to forecast agricultural droughts in the Guanzhong plain of China. The droughts were forecasted from early April to late May, 2009, and there were six step-1 forecasting results, six step-2 forecasting results and six step-3 forecasting results. The results show that the forecasting accuracies of the SARIMA models are gradually decreased with the increase of the forecasting steps. The distributions of absolute errors of the six step-1 forecasting results were basically in unimodal distributions and the errors were mainly in the range from -0.2 to 0.2. The six step-2 absolute errors were in bimodal distributions, and the errors of the step-3 were more scattered and larger. After analysis of drought spatial and temporal distributions in the Guanzhong plain, the droughts have obvious regional characteristics, and the forecasting drought spatial of step-1 and step-2 and temporal distributions are consistently better to the monitoring ones. The step-3 forecasting results have more uncertainties. The SARIMA model can be used for drought forecasting of step-1 and step-2 in the Guanzhong plain. Source


Li X.,Gansu Agricultural University | Li X.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Liu L.,Lanzhou University | Duan Z.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Wang N.,Shaanxi Provincial Meteorological Bureau
Environmental Earth Sciences | Year: 2014

The distribution and variability of surface soil moisture at regional scales is still poorly understood in the Loess Plateau of China. Spatial and temporal dynamics of surface soil moisture is important due to its impact on vegetation growth and its potential feedback to atmospheric and hydrologic processes. In this study, we analyzed surface soil moisture dynamics and the impacts of precipitation and evapotranspiration on surface soil moisture using remote sensing data during the growing season in 2011 for the Loess Plateau, which contain surface soil moisture, precipitation, vegetation index and evapotranspiration. Results indicate that the areas with low surface soil moisture are mainly located in the semi-arid region. Under dry surface soil moisture, evapotranspiration temporal persistence has a higher positive correlation (0.537) with surface soil moisture temporal persistence, and evapotranspiration is very sensitive to surface soil moisture. But under wet surface soil moisture regime, surface soil moisture temporal persistence has a higher negative correlation (-0.621) with evapotranspiration temporal persistence. Correlation of surface soil moisture and monthly precipitation, evapotranspiration and vegetation index illustrated that precipitation was a significant factor influencing surface soil moisture spatial variance. The correlation coefficients between monthly surface soil moisture and precipitation was varied in different climatic regions, which was 0.304 in arid, 0.364 in semi-arid, 0.490 in transitional and 0.300 in semi-humid regions. Surface soil moisture is more sensitive to precipitation, evapotranspiration, in transitional regions between dry and wet climates. © 2013 Springer-Verlag Berlin Heidelberg. Source

Discover hidden collaborations