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Mapping Above-Ground Biomass of Winter Oilseed Rape Using High Spatial Resolution Satellite Data at Parcel Scale under Waterlogging Conditions

机译:利用高空间分辨率卫星数据在涝灾条件下绘制冬季油菜地上生物量

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Oilseed rape ( Brassica napus L.) is one of the three most important oil crops in China, and is regarded as a drought-tolerant oilseed crop. However, it is commonly sensitive to waterlogging, which usually refers to an adverse environment that limits crop development. Moreover, crop growth and soil irrigation can be monitored at a regional level using remote sensing data. High spatial resolution optical satellite sensors are very useful to capture and resist unfavorable field conditions at the sub-field scale. In this study, four different optical sensors, i.e., Pleiades-1A, Worldview-2, Worldview-3, and SPOT-6, were used to estimate the dry above-ground biomass (AGB) of oilseed rape and track the seasonal growth dynamics. In addition, three different soil water content field experiments were carried out at different oilseed rape growth stages from November 2014 to May 2015 in Northern Zhejiang province, China. As a significant indicator of crop productivity, AGB was measured during the seasonal growth stages of the oilseed rape at the experimental plots. Several representative vegetation indices (VIs) obtained from multiple satellite sensors were compared with the simultaneously-collected oilseed rape AGB. Results showed that the estimation model using the normalized difference vegetation index (NDVI) with a power regression model performed best through the seasonal growth dynamics, with the highest coefficient of determination ( R 2 = 0.77), the smallest root mean square error (RMSE = 104.64 g/m 2 ), and the relative RMSE (rRMSE = 21%). It is concluded that the use of selected VIs and high spatial multiple satellite data can significantly estimate AGB during the winter oilseed rape growth stages, and can be applied to map the variability of winter oilseed rape at the sub-field level under different waterlogging conditions, which is very promising in the application of agricultural irrigation and precision agriculture.
机译:油菜(Brassica napus L.)是中国三种最重要的油料作物之一,被认为是耐旱的油料作物。但是,它通常对涝灾敏感,这通常是指限制作物生长的不利环境。此外,可以使用遥感数据在区域一级监测作物生长和土壤灌溉。高空间分辨率的光学卫星传感器对于捕获和抵抗子场规模的不利场条件非常有用。在这项研究中,使用了四个不同的光学传感器,即Pleiades-1A,Worldview-2,Worldview-3和SPOT-6,来估算油菜的干燥地上生物量(AGB)并跟踪季节生长动态。此外,从2014年11月至2015年5月,在中国浙江省北部的不同油菜油菜生长阶段进行了三个不同的土壤水分田间试验。作为农作物生产力的重要指标,在试验区油菜的季节性生长阶段测量了AGB。从多个卫星传感器获得的几种代表性植被指数(VI)与同时采集的油菜油菜AGB进行了比较。结果表明,使用归一化植被指数(NDVI)和幂回归模型的估计模型在季节生长动态方面表现最佳,确定系数最高(R 2 = 0.77),最小均方根误差(RMSE = 104.64 g / m 2)和相对RMSE(rRMSE = 21%)。结论是,使用选定的VI和高空间多卫星数据可以显着估计冬季油菜油菜生长阶段的AGB,并可以用于绘制不同油涝条件下子田水平的冬季油菜油菜的变异性,这在农业灌溉和精准农业的应用中非常有前途。

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