33 Prediction of Winter Wheat Yield Loss Caused By Dry-Hot Wind Based on Remote Sensing

Monday, 8 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Ying Li, China Meteorological Administration/Henan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou, China; and H. Chen, X. Wang, and H. Zhang
Manuscript (338.0 kB)

Handout (48.9 MB)

Dry-hot wind occurring during the late spring and early summer is one of the important causes of the reduction in winter wheat yield in the North China Plain. The extent of damage to physiological status of winter wheat caused by dry-hot wind in the same degree is greatly affected by soil moisture. For soil moisture has distinctive spatial distribution, the dry-hot wind damage to winter wheat has a dramatic variation in area. Therefore, the study about the prediction of winter wheat yield loss based on remote sensing monitoring of soil moisture can provide quantitative spatial distribution information of the yield loss caused by dry-hot wind. In this paper, firstly, the simulation experiment of dry-hot wind in milky stage of winter wheat was implemented. The ground spectral curves of winter wheat canopy before and after dry-hot wind conducted in different degrees by using artificial climate chambers were measured and simulated as EOS/MODIS channel data, and LAI, chlorophyll, leaf water potential, 1000-grain weight data were synchronously measured. Then, the yield data was also obtained in the harvest period. Through experimental analysis, the wide-band NDVI was selected as the spectral index sensitizing to the change of agronomic parameters of wheat. Then the relationship between NDVI variation and agronomic parameters and the evaluation model of wheat yield loss based on NDVI variation were established successively. After that, Henan Province was selected as the study area, and MODIS images and meteorological data from more than one hundred meteorological stations in two typical dry-hot wind years (2014 and 2017) was used, then a regression model was established among MODIS NDVI variation and three meteorological factors including temperature, humidity and wind speed and perpendicular drought index (PDI) calculated by the MODIS images before dry-hot wind occurred. Finally, above two models were combined based on the connection variable (NDVI), and then a new prediction model for winter wheat yield loss caused by dry-hot wind was deduced by using remote sensing drought index and three meteorological factors as independent variables. The spatial distribution map of winter wheat yield loss caused by dry-hot wind was produced by using the established prediction model, which was verified by ground observation data, and RMSE was 203.77 kg/hm2. The proposed method in this paper considered the difference of yield loss caused by atmospheric drought under different soil moisture. Besides, it is possible to predict the spatial distribution of winter wheat yield loss by using the model in combination with weather forecast data and remote sensing drought index before dry-hot wind occurs, which can provide decision support information of disaster response for the agricultural administrative departments.
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