A data fusion algorithm based on the Kalman filter to estimate leaf area index evolution in durum wheat by using field measurements and MODIS surface reflectance data
A data fusion algorithm based on the Kalman filter to estimate leaf area index evolution in durum wheat by using field measurements and MODIS surface reflectance data
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DOI：
10.1080/2150704x.2016.1154219
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发表时间：
2016-03
影响因子：
2.3
通讯作者：
A. Novelli;E. Tarantino;U. Fratino;V. Iacobellis;G. Romano;F. Gentile
中科院分区：
文献类型：
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作者：
A. Novelli;E. Tarantino;U. Fratino;V. Iacobellis;G. Romano;F. Gentile
叶面积指数（LAI）是衡量植被与大气之间能量和气体交换的重要指标，在生态系统和农学研究中具有重要意义。在过去的几十年里，叶面积指数的估计已被广泛的被动遥感数据，但估计结果往往受到噪声和测量不确定性。在本文中，我们提出了一种卡尔曼滤波算法，通过结合现场测量和中分辨率成像光谱仪（MODIS）表面反射率数据来估计叶面积指数的时间演变。利用实测数据推导了动态叶面积指数模型（状态转换模型）的标量方程，并利用MODIS红、近红外和短波红外反射率数据实现了观测模型。利用简化的简单比将反射率数据与叶面积指数联系起来。该方法进行了测试，在位于西北部的普利亚地区（意大利）的实验田。结果表明，通过该算法估算的叶面积指数与从田间数据得到的叶面积指数之间具有良好的一致性，决定系数（R2）为0.96，相应的均方根误差为0.124。
ABSTRACT The use of leaf area index (LAI) is essential in ecosystem and agronomic studies since it measures energy and gas exchanges between vegetation and atmosphere. In the last decades, LAI values have widely been estimated from passive remotely sensed data although estimated results were often affected by noise and measurement uncertainties. In this article, we propose a Kalman filter algorithm in order to estimate the time evolution of LAI by combining field-measured and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. The scalar equation of the dynamic LAI model (state transition model) was derived by the field-measured data while the MODIS red, near-infrared and shortwave infrared reflectance data were used to implement the observation model. The reflectance data were linked to LAI by using the reduced simple ratio. The method was tested in an experimental field located in the north-western part of the Apulia region (Italy). The results showed a good agreement between the LAI estimated through the algorithm and the LAI derived from field data, with a coefficient of determination (R2) of 0.96 and a corresponding root mean square error of 0.124.