Modeling grass yields in Qinghai Province, China, based on MODIS NDVI data—an empirical comparison,Frontiers of Earth Science

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Modeling grass yields in Qinghai Province, China, based on MODIS NDVI data—an empirical comparison
Frontiers of Earth Science ( IF 1.8 ) Pub Date : 2020-01-17 , DOI: 10.1007/s11707-019-0780-x
Jianhong Liu , Clement Atzberger , Xin Huang , Kejian Shen , Yongmei Liu , Lei Wang

Qinghai Province is one of the four largest pastoral regions in China. Timely monitoring of grass growth and accurate estimation of grass yields are essential for its ecological protection and sustainable development. To estimate grass yields in Qinghai, we used the normalized difference vegetation index (NDVI) time-series data derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) and a pre-existing grassland type map. We developed five estimation approaches to quantify the overall accuracy by combining four data pre-processing techniques (original, Savitzky-Golay (SG), Asymmetry Gaussian (AG) and Double Logistic (DL)), three metrics derived from NDVI time series (VI_max, VI_season and VI_mean) and four fitting functions (linear, second-degree polynomial, power function, and exponential function). The five approaches were investigated in terms of overall accuracy based on 556 ground survey samples in 2016. After assessment and evaluation, we applied the best estimation model in each approach to map the fresh grass yields over the entire Qinghai Province in 2016. Results indicated that: 1) For sample estimation, the cross-validated overall accuracies increased with the increasing flexibility in the chosen fitting variables, and the best estimation accuracy was obtained by the so called “fully flexible model” with R² of 0.57 and RMSE of 1140 kg/ha. 2) Exponential models generally outperformed linear and power models. 3) Although overall similar, strong local discrepancies were identified between the grass yield maps derived from the five approaches. In particular, the two most flexible modeling approaches were too sensitive to errors in the pre-existing grassland type map. This led to locally strong overestimations in the modeled grass yields.

更新日期：2020-01-17

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