Monitoring Winter Wheat Phenology Using Time Series of Remote Sensing Data

2009 ◽  
Author(s):  
Yuqin Huang ◽  
Linlin Lu
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Winter Wheat ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series

2019 ◽  
Vol 11 (4) ◽  
pp. 449 ◽  
Author(s):  
Yang Song ◽  
Jing Wang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Winter Wheat ◽  
A Priori ◽  
Remote Sensing Data ◽  
A Priori Knowledge ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Phenological Metrics ◽  
Crop Planting

Crop planting area mapping and phenology monitoring are of great importance to analyzing the impacts of climate change on agricultural production. In this study, crop planting area and phenology were identified based on Sentinel-1 backscatter time series in the test region of the North China Plain, East Asia, which has a stable cropping pattern and similar phenological stages across the region. Ground phenological observations acquired from a typical agro-meteorological station were used as a priori knowledge. A parallelepiped classifier processed VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving) backscatter signals in order to map the winter wheat planting area. An accuracy assessment showed that the total classification accuracy reached 84% and the Kappa coefficient was 0.77. Both the difference ( σ d ) between VH and VV and its slope were obtained to contrast with a priori knowledge and then used to extract the phenological metrics. Our findings from the analysis of the time series showed that the seedling, tillering, overwintering, jointing, and heading of winter wheat may be closely related to σ d and its slope. Overall, this study presents a generalizable methodology for mapping the winter wheat planting area and monitoring phenology using Sentinel-1 backscatter time series, especially in areas lacking optical remote sensing data. Our results suggest that the main change in Sentinel-1 backscatter is dominated by the vegetation canopy structure, which is different from the established methods using optical remote sensing data, and it is available for phenological metrics extraction.

scholarly journals Coupling Hyperspectral Remote Sensing Data with a Crop Model to Study Winter Wheat Water Demand

2019 ◽  
Vol 11 (14) ◽  
pp. 1684 ◽  
Author(s):  
Chao Zhang ◽  
Jiangui Liu ◽  
Taifeng Dong ◽  
Elizabeth Pattey ◽  
Jiali Shang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Winter Wheat ◽  
Water Demand ◽  
Stress Condition ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Actual Evapotranspiration ◽  
Sensing Data ◽  
Water Stress Condition

Accurate information of crop growth conditions and water status can improve irrigation management. The objective of this study was to evaluate the performance of SAFYE (simple algorithm for yield and evapotranspiration estimation) crop model for simulating winter wheat growth and estimating water demand by assimilating leaf are index (LAI) derived from canopy reflectance measurements. A refined water stress function was used to account for high crop water stress. An experiment with nine irrigation scenarios corresponding to different levels of water supply was conducted over two consecutive winter wheat growing seasons (2013–2014 and 2014–2015). The calibration of four model parameters was based on the global optimization algorithms SCE-UA. Results showed that the estimated and retrieved LAI were in good agreement in most cases, with a minimum and maximum RMSE of 0.173 and 0.736, respectively. Good performance for accumulated biomass estimation was achieved under a moderate water stress condition while an underestimation occurred under a severe water stress condition. Grain yields were also well estimated for both years (R2 = 0.83; RMSE = 0.48 t∙ha−1; MRE = 8.4%). The dynamics of simulated soil moisture in the top 20 cm layer was consistent with field observations for all scenarios; whereas, a general underestimation was observed for total water storage in the 1 m layer, leading to an overestimation of the actual evapotranspiration. This research provides a scheme for estimating crop growth properties, grain yield and actual evapotranspiration by coupling crop model with remote sensing data.

Estimation of winter wheat yield by using remote sensing data and crop model

2012 ◽  
Author(s):  
Jianmao Guo ◽  
Tengfei Zheng ◽  
Qi Wang ◽  
Jia Yang ◽  
Junyi Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Sensing Data ◽  
Winter Wheat Yield
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