Spatial and temporal mismatches between coarse resolution output of global climate models (GCMs) and fine resolution data requirements of crop models are the major obstacles for assessing the site-specific climatic impacts of climate change on the production of winter wheat. Based on the output of IPCC AR4 model and observation data, statistical downscaling of precipitation, minimum temperature, and maximum temperature in North China was analyzed. With the combination crop model and climate mode, the effects of climate change on the winter wheat production of North China were simulated. Some conclusions from the study might be drawn as follows: Under the IPCC-B1 Scenario, the length of winter wheat growing season in North China would be shortened from 2010 to 2099, and its yield would be decreased.
Vegetation phenology was corresponding to the date of obvious change of vegetation.Regional vegetation phenology data were the basis for estimating the yield, monitoring vegetation growth and predicting disaster and so on.The objective of this paper was to establish a method of monitoring regional vegetation phenology based on MODIS.Data used in this research were 10-day composite NDVI from MODIS with 250m spatial resolution in 2004.Savitzky-Golay filter was used to smooth the multi-temporal NDVI data to eliminate noises.Through analyzing the vegetation of study area, three key plant phenological variables were selected, including the start of growing season, end of growing season, durative time of max coverage in growing season.The results showed that MODIS could monitor the regional vegetation phenophase.
Synthetic aperture radar (SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.
Leaf area index is an important vegetation canopy structure parameter for vegetation monitoring, climate change, ecological process and data assimilation system. The method of LAI inversion is one of the focuses of quantitative remote sensing research. In this study, the global optimal algorithm SCE-UA (Shuffled Complex Evolution method developed at the University of Arizona) was integrated into canopy reflectance model ACRM (A Two-Layer Canopy Reflectance Model) to improve the process of inversion LAI. A global sensitivity analysis method EFAST (Extended Fourier Amplitude Sensitivity Test) was used firstly to analyze sensitivity of parameters in ACRM. The most sensitive parameters were optimized iteratively until difference between simulated and measured canopy reflectance was minimized in order to obtain optimal LAI. Accuracy verification and feasibility analysis were carried out with simultaneous observation data on field sites and regional scale. The results show that LAI, chlorophyll content Cab, leaf structure parameter N, Markov (clumping) parameter Sz, relative leaf size SL and soil reflectance parameter rsl1 are optimized variables which are most sensitive for the four bands of HJ CCD image. The acceptable and desired inversion of winter wheat LAI was performed successfully. On field sites, coefficient of determination R 2 is 0.88, Normalized Root Mean Square Error (NRMSE) is 9.95%, and the Relative Error (RE) is 8.45%. On regional scales, R 2 for LAI is 0.66, NRMSE is 26.13%, and RE is 20.62%. The proposed inversion approach of vegetation canopy LAI based on ACRM is feasible and effective, and will be most potential method of application.
Garlic and onion are the most important vegetable crops in Korea. The accurate estimation of cultivation area for garlic and onion acreage is critical to predict the production of vegetable crops, adjust agricultural planting plan and ensure an effective supply of farm products. However, the plots cultivated with garlic and onion are very fragmentary and dispersive because the cross cropping commonly occurs in the two crops. Therefore, it is very difficult to establish the accurate identification of garlic and onion using satellite-based remotely sensed images alone. In case of tracking Hapcheon Gun, Korea as the sampling site, objective of this study was to formulate a spatial sampling scheme through combining satellite-based, unmanned aerial vehicle (UAV) remotely sensed images and the stratified sampling method for improving the estimated accuracy to cultivation area of garlic and onion. The results are shown that there was almost no classification error, when UAV remotely sensed image was used to retrieve the cultivation area of garlic and onion. The error found in the two crops classification using Rapid Eye satellite-based images, and the classification error for garlic was the larger than that of onion;. The variance for cultivation area of garlic and onion within each stratum can be significantly decreased, when the proportion of the cultivation area for two crops is accounted for one sampling unit. It was observed that the required sample size for meeting the designed extrapolation accuracy decreased with the stratification number of the sampled population. Comprehensively considering population extrapolating accuracy, sampling survey cost and rationality, 10 strata was the optimum stratification number. It was appeared that the spatial stratified sampling scheme combining satellite-based and UAV remotely sensed images had a high accuracy and stability for estimation of cultivation areas for the two crops, because both the relative error and CV of population extrapolation using this scheme was less than 10%. Results of population extrapolation and error estimation for garlic acreage in the study area in 2017. Stratum number Nh N Wh nh fh (%) (m2) sh2 1 198 422 0.4692 28 0.1414 10272.57 111059383.60 2 78 422 0.1848 10 0.1282 23347.15 441781199.13 3 47 422 0.1114 6 0.1277 53480.35 217588913.35 4 41 422 0.0972 5 0.1220 72042.97 598687783.10 5 24 422 0.0569 3 0.1250 101778.39 85000902.09 6 17 422 0.0403 3 0.1765 116648.67 1006782408.64 7 7 422 0.0166 2 0.2857 143419.00 765673525.59 8 4 422 0.0095 2 0.5000 144819.77 2796755176.20 9 3 422 0.0071 2 0.6667 269129.69 10129222962.02 10 3 422 0.0071 2 0.6667 261057.10 1065556621.53 r (%) 7.22 CV (%) 5.12
Assimilating external data into a crop growth model to improve accuracy of crop growth monitoring and yield estimation has been a research focus in recent years. In this paper, the shuffled complex evolution (SCE-UA) global optimization algorithm was used to assimilate field measured LAI into EPIC model to simulate yield, sowing date and nitrogen fertilizer application amount of summer maize in Huanghuaihai Plain in China. The results showed that RMSE between simulated yield and field measured yield of summer maize was 0.84 t ha -1 and the R 2 was only 0.033 without external data assimilation. While the performances of EPIC model of simulating yield, sowing date and nitrogen fertilizer application amount of summer maize was better through assimilating field measured LAI into the EPIC model. The RMSE of between simulated yield and field measured yield of summer maize was 0.60 t ha -1 and the R 2 was 0.5301. The relative error between simulated sowing date and real sowing date of summer maize was 2.28%. On the simulation of nitrogen fertilizer application rate, the relative error was -6.00% compared with local statistical data. These above accuracy could meet the need of crop growth monitoring and yield estimation at regional scale. It proved that assimilating field measured LAI into crop growth model based on SCE-UA optimization algorithm to monitor crop growth and estimate crop yield was feasible.
In order to obtain the spatial information of winter wheat harvest index (HI) at regional scale, according to the definition of crop harvest index, depending on MODIS-NDVI time-series images to calculate accumulated NDVI at stage of after anthesis and before anthesis, the authors structured the parameter HI NDVI-SUM which meant the ratio of accumulated 10-day NDVI at stages of after anthesis to accumulated 10-day NDVI at stages of before anthesis. Then, the relationship between the parameter HI NDVI-SUM and the field measured HI of winter wheat was established. Finally, the authors used this relationship to get the spatial crop harvest index information. After validation of retrieved HI, it was shown that the accuracy of the retrieved HI of winter wheat was high and satisfied at large scale. The mean relative error of retrieved HI of winter wheat was only 2.40% and RMSE was only 0.02. It was proved that the method of structuring parameters of HI NDVI-SUM and extracting harvest index for winter wheat based on NDVI time-series images was reasonable and feasible.
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