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Effects of crop residue cover resulting from tillage practices on LAI estimation of wheat canopies using remote sensing

机译:耕作方式造成的农作物残茬覆盖对小麦冠层LAI遥感估算的影响

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Much research has been conducted seeking to reduce the noise interference from soil in remote sensing of vegetation, and thus more accurately predict plant biophysical parameters by developing different kinds of vegetation indices. However, few studies have examined the effect of residue cover on the estimation of leaf area index (LAI) using remote sensing techniques. Using the WinSail model along with ground measurements, this study sought to quantify the differences in spectral reflectance and commonly used ratio-based (NDVI and RVI), soil-adjusted (TSAVI and SAVI2) and hyperspectral (dRE and REIP) vegetation indices between wheat canopies with bare soil and rice residue as backgrounds and to identify the vegetation indices that represented the best combination of low sensitivity to residue cover and high sensitivity to variation in LAI. We found large variations in residue cover in wheat fields, with average cover of 6.70% and 45.9% for intensively tilled and untilled fields, respectively. Changing the background from soil to residue resulted in substantial changes in both reflectance and vegetation indices of canopies when LAI varied between 0.1 and 1.0, with average absolute values of relative percent differences (|RPD|) ranging from 4.21% to 19.03% for the six vegetation indices used in this study. We found that changes in the background from bare soil to rice residue would result in underestimation of LAI by NDVI, RVI, TSAVI, and SAVI2, and overestimation of LAI by dRE and REIP. More green leaves in the wheat canopy were covered by residue in untilled fields than in intensively tilled fields, with an average 8.57% obscuring rate when green cover was between 1.0% and 85%; this would lead to the underestimation of LAI by remote sensing techniques in untilled fields. Ultimately, we determined that dRE was the best choice of the six indices for predicting LAI because of its significant relationship with LAI and because it was least sensitive to residue effects yet remained sensitive to variation in LAI, underestimating LAI in untilled fields by only 3.70%. The better performance of dRE was attributed to the opposite additive influences of the residue obscuring effect and brightness differences between soil and residue.
机译:已经进行了许多研究,以减少植被遥感中土壤的噪声干扰,从而通过开发不同种类的植被指数来更准确地预测植物的生物物理参数。但是,很少有研究检查了残留物覆盖对使用遥感技术估算叶面积指数(LAI)的影响。这项研究使用WinSail模型和地面测量数据,试图量化小麦之间光谱反射率和常用的基于比率的(NDVI和RVI),土壤调整的(TSAVI和SAVI2)和高光谱(dRE和REIP)植被指数之间的差异。以裸露的土壤和水稻残留物为背景的冠层,并确定植被指数,这些指数代表了对残留物覆盖的低敏感性和对LAI变化的高敏感性的最佳组合。我们发现麦田的残留覆盖率差异很大,深耕和耕作的平均覆盖率分别为6.70%和45.9%。当LAI在0.1到1.0之间变化时,将背景从土壤变为残留物会导致冠层的反射率和植被指数发生显着变化,这六个物种的相对百分比差异的平均绝对值(| RPD |)为4.21%至19.03%本研究中使用的植被指数。我们发现,从裸土到水稻残留物的背景变化将导致NDVI,RVI,TSAVI和SAVI2对LAI的低估,而dRE和REIP对LAI的高估。在耕作的田地中,相比于耕作的耕地,小麦冠层的绿叶覆盖的残渣更多,当绿化覆盖率在1.0%至85%之间时,平均遮盖率为8.57%。这将导致耕作领域的遥感技术对LAI的低估。最终,我们确定dRE是预测LAI的六个指标中的最佳选择,因为它与LAI有显着关系,并且它对残基效应最不敏感,但对LAI的变化仍然敏感,因此仅低估了耕地中LAI的3.70% 。 dRE的更好性能归因于残留物遮盖效果和土壤与残留物之间亮度差异的相反加性影响。

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