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A comparison of harvest index estimation methods of winter wheat based on field measurements of biophysical and spectral data

机译:基于生物物理和光谱数据实地测量的冬小麦收获指数估算方法的比较

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Crop harvest index (HI) is a key parameter in grain yield simulation from indirect biomass estimates and varies with crop growth conditions. How to access the HI still remains an issue to be addressed. Several HI estimation techniques for wheat have been proposed based on the fraction of water transpired after anthesis (theta(E) or theta(Ee)), the fraction of biomass accumulation after anthesis (f(G)) and the ratio of the mean normalized difference vegetation index (NDVI) after anthesis to that of the pre-anthesis period (NDVIpost/NDVIpre). In this paper, efforts were made to test the performances of the above HI estimation methods based on field measurements of winter wheat at different nitrogen (N) levels. Results showed that the HI changed significantly from low-N to high-N treatments, while the theta(E) or theta(Ee) was not sensitive to the N deficiency. In addition, no consistent effect of N fertilizer on f(G) was observed. Using the canopy NDVI to estimate HI seemed workable since strong correlations were found between HI, the term 'NDVIpost/NDVIpre' and the fraction of cumulative NDVI after anthesis. Further analysis suggested a good estimation of HI can be obtained just using the NDVI dataset from anthesis to maturity, especially via binary regression relations of HI, NDVI at anthesis and cumulative NDVI after anthesis. The proposed method in this study may be well oriented to practical application as its short-term dataset is relatively easy to access from satellite platforms
机译:作物收获指数(HI)是间接生物量估算在谷物产量模拟中的关键参数,并随作物生长条件而变化。如何访问HI仍然是一个有待解决的问题。基于花后蒸腾的水分量(theta(E)或theta(Ee)),花后的生物量积累比例(f(G))和均化归一化比率,提出了几种小麦的HI估算技术花后与花前期的植被指数(NDVIpost / NDVIpre)之间的差异植被指数(NDVI)。本文基于冬小麦在不同氮水平下的实地测量,努力测试上述HI估计方法的性能。结果表明,从低氮到高氮处理,HI有显着变化,而theta(E)或theta(Ee)对氮缺乏不敏感。此外,未观察到氮肥对f(G)的持续影响。使用冠层NDVI估算HI似乎可行,因为发现HI,术语“ NDVIpost / NDVIpre”与花后累积NDVI的比例之间存在很强的相关性。进一步的分析表明,仅使用从花期到成熟的NDVI数据集就可以很好地估计HI,尤其是通过HI,花期NDVI和花后累积NDVI的二元回归关系。本研究中提出的方法可能很适合实际应用,因为它的短期数据集相对容易从卫星平台访问

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