Dualex便携式智能气象-叶绿素-花青素-黄酮醇测量仪

摘要

作物的生化参数（即叶绿素和氮含量）对于评估健康状况和监测作物生长至关重要。将近距离传感器数据与卫星数据相结合，为快速、详细、可靠地评估作物生化参数提供了一个新的角度。本研究旨在利用Sentinel-2A多光谱仪器（MSI）卫星数据更高的空间和光谱分辨率，结合近端传感器数据，得出贾坎德兰奇地区雨季（2017年）不同生长阶段水稻的叶绿素（Chl）含量和氮平衡指数（NBI）。主要发现表明，与基于现场的测量相比，卫星测量的水稻Chl含量和NBI被高估。卫星数据和基于经验模型的Chl含量与水稻NBI之间的相关性显示R2为0.92–0.99（p < 0.001）在不同的生长阶段。Chl含量在水稻生长高峰期较高（20–40µg/cm2），随后降低(< 20µg/cm2），在成熟阶段开始时。经验模型与基于卫星的Chl含量之间的均方根误差为7.7至8.5µg/cm2。因此，可以得出结论，具有三个红边带以及可见光和近红外的MSI传感器具有量化作物生化参数的良好能力。然而，需要进行偏差校正才能准确得出Chl含量/NBI。所采用的检索不同生长阶段生化参数的方法将有助于农业监测决策者，并可用于作物生长建模和产量预测。

Synergy of Sentinel-2A and Near-proximal sensor data for deriving biochemical parameters of paddy at different growth stages

Abstract

The biochemical parameters (i.e. chlorophyll and nitrogen content) of crops are essential to evaluate the health status and to monitor crop growth. The incorporation of near-proximal sensor data with satellite data has offered a new angle for rapid, detailed, and reliable assessment of crop biochemical parameters. This study aimed to utilize the higher spatial and spectral resolution of Sentinel-2A multi-spectral instrument (MSI) satellite data in conjunction with near-proximal sensor data to derive the chlorophyll (Chl) content and nitrogen balance index (NBI) of paddy at different growth stages during the monsoon season (2017) in Ranchi district, Jharkhand. The key findings revealed that the satellite-derived Chl content and NBI of paddy were overestimated as compared to the field-based measurements. The correlations between satellite-derived and empirical model-based Chl content and NBI of paddy revealed a R2 of 0.92–0.99 (p < 0.001) at different growth stages. The Chl content was found higher (20–40 µg/cm2) during the peak growing stage of paddy and subsequently, it decreased (< 20 µg/cm2) by the start of the ripening stage. The root-mean-square error between the empirical model and satellite-based Chl content was from 7.7 to 8.5 µg/cm2. Thereby, it can be concluded that the MSI sensor with three red-edge bands along with visible and near-infrared has a virtuous capability to quantify the crop biochemical parameters. However, it needs a bias correction to derive Chl content/NBI accurately. The adopted methods of retrieving biochemical parameters at different growth stages would be beneficial for decision-makers in agriculture monitoring and can be incorporated in crop growth modelling and yield predictions.

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