Abstract
Leaf area index (LAI) remote sensing data products with a high resolution (HR) and long time series are in demand in a wide variety of applications. Compared with long time series LAI products with 1 km resolution, LAI products with high spatial resolution are difficult to acquire because of the lack of remote sensing observations in long-term sequences and the lack of estimation methods applicable to highly variable land-cover types. To address these problems, we proposed a recursive update model to estimate 30 m resolution LAI based on the updated Nonlinear Auto-Regressive with Exogenous Inputs (NARX) neural network and MODIS time series. First, we used a variety of HR satellite remote sensing observations to produce HR datasets for recent years. Historical low spatial resolution MODIS products were employed as background information and used to calculate the initial parameters of the NARX neural network for each pixel. Subsequently, one year’s reflectance from the HR dataset was used as the new observation that was input into the NARX model to estimate the HR LAI of that year, and the background and HR data were then used for remodeling to update the NARX model parameters. This procedure was recursively repeated year by year until both MODIS background data and all HR data were involved in the modeling. Finally, we obtained an LAI time series with 30 m resolution. In the cropland study area in Hebei Province, China, the results were compared with LAI measurements from ground sites in 2013 and 2014. A high degree of similarity existed between the results for the two study years ( RMSE 2013 = 0.288 and RMSE 2014 = 0.296 ). The HR LAI estimates showed favorable spatiotemporal continuity and were in good agreement with the multisample ground survey LAI measurements. The results indicated that for data with a rapid revisit cycle and high spatial resolution, the recursive update model based on the NARX neural network has excellent LAI estimation performance and fairly strong fault-tolerance capability.
Highlights
The leaf area index (LAI) is defined as the one-sided green leaf area per unit of horizontal ground area [1]
high resolution (HR) satellite data with short revisit intervals are used to predict high spatial resolution LAI values over the area of the Huailai experimental region in Hebei Province, China
A historical low-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) product was used as background information to extract the stable phenological changes in vegetation, which were used to estimate the HR LAI
Summary
The leaf area index (LAI) is defined as the one-sided green leaf area per unit of horizontal ground area [1]. The empirical regression approach normally uses remotely sensed imagery and terrestrial survey data acquired simultaneously to carry out multilinear regression modeling [8] or statistical models [9] to estimate LAI; most of these methods use various vegetation indices [10,11,12,13] as input parameters Because these methods are subject to the availability of terrestrial survey data and to regional limitations, they are very difficult to generalize and use. By comparing validation period results, NARX showed a significant superiority over static neural networks [35] This method has been used for change detection in snow caps [36] and LAI time series estimation [37] based on MODIS data. A recurrent update model was developed in this study and tested in eight pilot sites using HR images
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