scholarly journals Spatial Heterogeneity of Winter Wheat Yield and Its Determinants in the Yellow River Delta, China

2019 ◽  
Vol 12 (1) ◽  
pp. 135
Author(s):  
Lin Chu ◽  
Chong Huang ◽  
Qingsheng Liu ◽  
Chongfa Cai ◽  
Gaohuan Liu
Keyword(s):  
Winter Wheat ◽  
Soil Salinity ◽  
Crop Yields ◽  
Yellow River ◽  
Wheat Yield ◽  
Yellow River Delta ◽  
The Yellow River ◽  
The Yellow River Delta ◽  
Winter Wheat Yield ◽  
Actual Yield

Understanding spatial differences of crop yields and quantitatively exploring the relationship between crop yields and influencing factors are of great significance in increasing regional crop yields, promoting sustainable development of regional agriculture and ensuring regional food security. This study investigates spatial heterogeneity of winter wheat yield and its determinants in the Yellow River Delta (YRD) region. The spatial pattern of winter wheat in 2015 was mapped through time series similarity analysis. Winter wheat yield was estimated by integrating phenological information into yield model, and cross-validation was performed using actual yield data. The geographical detector method was used to analyze determinants influencing winter wheat yield. This study concluded that the overall classification accuracy for winter wheat is 88.09%. The estimated yield agreed with actual yield, with R2 value of 0.74 and root mean square error (RMSE) of 1.02 t ha−1. Cumulative temperature, soil salinity and their interactions were key determinants affecting winter wheat yield. Several measures are recommended to ensure sustainable crop production in the YRD region, including improving irrigation and drainage systems to reduce soil salinity, selecting salt-tolerant winter wheat varieties, and improving agronomy techniques to extend effective cumulative temperature.

scholarly journals Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6521
Author(s):  
Guanghui Qi ◽  
Gengxing Zhao ◽  
Xue Xi
Keyword(s):  
Winter Wheat ◽  
Soil Salinity ◽  
Agricultural Production ◽  
Yellow River ◽  
Yellow River Delta ◽  
The Yellow River ◽  
Inversion Method ◽  
Optimal Model ◽  
The Yellow River Delta ◽  
Uav Images

Soil salinization is an important factor affecting winter wheat growth in coastal areas. The rapid, accurate and efficient estimation of soil salt content is of great significance for agricultural production. The Kenli area in the Yellow River Delta was taken as the research area. Three machine learning inversion models, namely, BP neural network (BPNN), support vector machine (SVM) and random forest (RF) were constructed using ground-measured data and UAV images, and the optimal model is applied to UAV images to obtain the salinity inversion result, which is used as the true salt value of the Sentinel-2A image to establish BPNN, SVM and RF collaborative inversion models, and apply the optimal model to the study area. The results showed that the RF collaborative inversion model is optimal, R2 = 0.885. The inversion results are verified by using the measured soil salt data in the study area, which is significantly better than the directly satellite remote sensing inversion method. This study integrates the advantages of multi-scale data and proposes an effective “Satellite-UAV-Ground” collaborative inversion method for soil salinity, so as to obtain more accurate soil information, and provide more effective technical support for agricultural production.

Potential plant species distribution in the Yellow River Delta under the influence of groundwater level and soil salinity

Ecohydrology ◽  
2010 ◽  
Vol 4 (6) ◽  
pp. 744-756 ◽  
Author(s):  
Xiaomei Fan ◽  
Bas Pedroli ◽  
Gaohuan Liu ◽  
Hongguang Liu ◽  
Chuangye Song ◽  
...  
Keyword(s):  
Plant Species ◽  
Species Distribution ◽  
Soil Salinity ◽  
Groundwater Level ◽  
Yellow River ◽  
Yellow River Delta ◽  
River Delta ◽  
The Yellow River ◽  
The Yellow River Delta
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