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Aboveground biomass estimation of late-stage maize based on the WOFOST model and UAV observations

  • role： First author第一作者
  • Affiliation：

    Institute of Geography, Fujian Normal University, Fuzhou 350007, China

    School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China

    Key Laboratory for Subtropical Mountain Ecology (Funded by Ministry of Science and Technology and Fujian Province), Fujian Normal University, Fuzhou 350007, China

  • Email：chengzq@fjnu.edu.cn
  • Introduction：程志强，1989年生,男,博士研究生, 研究方向为土壤速效养分遥感反演、作物冠层参数遥感监测。E-mail: chengzq@fjnu.edu.cn
CHENG Zhiqiang134,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：mengjh@radi.ac.cn
  • Introduction：蒙继华，1977年生,男,研究员, 研究方向为精准农业遥感应用、农情信息遥感获取。E-mail: mengjh@radi.ac.cn
MENG Jihua2*,  
  • Affiliation：

    Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

JI Fujiang2,  
  • Affiliation：

    Institute of Geography, Fujian Normal University, Fuzhou 350007, China

WANG Yang1,  
  • Affiliation：

    Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

FANG Huiting2,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

YU Lihong5

résumé

Aboveground biomass is one of the most important indicators of crop growth, and timely and accurate information on biomass is critical for crop growth assessment, yield forecast, and farm management. Crop models based on detailed physiological and biochemical information can provide digital descriptions for key vegetation processes and are ideal choices for crop biomass estimation. Given that crop models are generally designed to simulate crop growth at point scale, applying crop models to the regional scale requires knowledge of a large amount of input parameters with high calibration cost. In recent years, researchers have combined Remote Sensing (RS) and crop models by using assimilation methods to improve the ability of regional biomass estimation. However, considering that RS data provide most spatial information during model execution, any errors in these data will influence the biomass estimation accuracy.Under the influence of reflectance saturation, the crop canopy information capture ability for the RS method in the late crop growth stage is lower and errors can be hardly be avoided. In this study, a new combination method for RS data and crop model was proposed to address the saturation issue during late-stage biomass estimation. In the new method, the whole crop growth season was divided into early stage and late stage. At the early stage, the World Food Studies (WOFOST) crop model was adopted to simulate daily crop growth at point scale. Next, the Leaf Area Index (LAI) based on the time-series multispectral RS was assimilated into the WOFOST model to extend model simulation from point scale to regional scale. The RS-based LAI was calculated through a statistical model using multispectral RS data acquired by an Unmanned Aerial Vehicle (UAV). The statistical model was built according to field LAI and UAV based vegetation indices. At the end of the early stage, the key model status parameters, including dry matter of different organs, soil water, and accumulated temperature, were simulated and output by the WOFOST model.Meanwhile, soil-available nitrogen content was also estimated and transformed to the beginning of the late growth stage. At the late stage, the RS data assimilation was halted to avoid unnecessary errors caused by reflectance saturation. The estimated soil-available nitrogen and model status parameters were input to the WOFOST model, and the aboveground biomass was estimated based on the crop growth simulations. Given that the WOFOST model was only used for a short-term crop growth simulation without RS data, the error accumulation could be effectively avoided. In this study, the spring maize plots in Hongxing Farm, northeast China, were selected to apply the proposed new method and the biomass was estimated in 2015. As a comparison, the RS-based LAI for the late stage was also assimilated into the WOFOST model to estimate the spring maize biomass for the same plots. Using the field campaign data, R2 and RMSE were calculated to analyze the estimation accuracies of the two methods. Analysis results show that the accuracy of the new method (R2 = 0.86, RMSE = 2216.79 kg/ha) was higher than that of the method with RS data assimilation for the whole crop growth season (R2 = 0.45, RMSE = 4254.30 kg/ha). In conclusion, the new method can provide reliable biomass results for spring maize in the late stage by taking advantage of the WOFOST mode and UAV data.

mots-clés

remote sensing;crop model;multispectral data;LAI;data assimilation;vegetation index;crop growth stage

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