[Show abstract][Hide abstract] ABSTRACT: By using retrieved LAI from remotely-sensed imagery, this paper studied the regional winter wheat yield estimation in Huanghuaihai Plain of North China. In order to improve the quality of remotely sensed data for winter wheat yield estimation, a Savitzky-Golay filter was used to smooth the MODIS-NDVI time series data to reduce the cloud contamination and remove the abnormal data. Then, a Gaussian model was used to simulate the daily crop LAI which was corrected by interpolating the measured LAI to get the average LAI values for each phenological stage. Using these LAI data, the relationships between LAI and crop yield at the main phenological stages of winter wheat was established. After optimizing the yield estimation model, the optimal time period and the best model parameters for winter wheat yield estimation in the study area were selected out. Finally, the established model was applied to estimate winter wheat yield based on the retrieved LAI from MODIS-NDVI, and the model accuracy was tested. Through the comparison of the predicted yield with the measured yield in the field, the mean relative error was 1.21%, and the RMSE was 257.33 kg x hm(-2). The model and the method proposed in this study were promising, and could help to get the accurate estimated yield of winter wheat in about 20-30 days ahead of the harvest.
No preview · Article · Nov 2010 · Ying yong sheng tai xue bao = The journal of applied ecology / Zhongguo sheng tai xue xue hui, Zhongguo ke xue yuan Shenyang ying yong sheng tai yan jiu suo zhu ban
[Show abstract][Hide abstract] ABSTRACT: This study used time-series of global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI) datasets at a spatial resolution of 8 km and 15-d interval to investigate the spatial patterns of cropland phenology in China. A smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent processing for identifying cropping systems and extracting phenological parameters, the starting date of growing season (SGS) and the ending date of growing season (EGS) was based on the smoothed NVDI time-series data. The results showed that the cropping systems in China became complex as moving from north to south of China. Under these cropping systems, the SGS and EGS for the first growing season varied largely over space, and those regions with multiple cropping systems generally presented a significant advanced SGS and EGS than the regions with single cropping patterns. On the contrary, the phenological events of the second growing season including both the SGS and EGS showed little difference between regions. The spatial patterns of cropping systems and phenology in Chinese cropland were highly related to the geophysical environmental factors. Several anthropogenic factors, such as crop variety, cultivation levels, irrigation, and fertilizers, could profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.
No preview · Article · Jan 2010 · Agricultural Sciences in China
[Show abstract][Hide abstract] ABSTRACT: Based on the 2004 in situ data of crop yield, remote sensing inversed photosynthetically active radiation (PAR), fraction of photosynthetically active radiation (f(PAR)), climate, and soil moisture in 83 typical winter wheat sampling field of 45 counties in Shijiazhuang, Hengshui, and Xingtai of Hebei Province, a simplified model for calculating the light use efficiency (epsilon) of winter wheat in Huanghuaihai Plain was established. According to the crop accumulated biomass from March to May and corrected by harvest index, the quantitative relationship between crop biomass and crop yield for winter wheat was set up, and applied in the 235 counties in Huanghuaihai Plain region of Hebei Province and Shandong Province and validated by the official crop statistical data at county level in 2004. The results showed that the root mean square error (RMSE) of predicted winter wheat yield in study area was 238.5 kg x hm(-2), and the relative error was 4.28%, suggesting that it was feasible to predict winter wheat yield by crop biomass estimation based on remote sensing data.
No preview · Article · May 2009 · Ying yong sheng tai xue bao = The journal of applied ecology / Zhongguo sheng tai xue xue hui, Zhongguo ke xue yuan Shenyang ying yong sheng tai yan jiu suo zhu ban