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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 (RMSE2013=0.288 and RMSE2014=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.
Jian Wang; Jindi Wang; Yuechan Shi; Hongmin Zhou; Limin Liao. A Recursive Update Model for Estimating High-Resolution LAI Based on the NARX Neural Network and MODIS Times Series. Remote Sensing 2019, 11, 219 .
AMA StyleJian Wang, Jindi Wang, Yuechan Shi, Hongmin Zhou, Limin Liao. A Recursive Update Model for Estimating High-Resolution LAI Based on the NARX Neural Network and MODIS Times Series. Remote Sensing. 2019; 11 (3):219.
Chicago/Turabian StyleJian Wang; Jindi Wang; Yuechan Shi; Hongmin Zhou; Limin Liao. 2019. "A Recursive Update Model for Estimating High-Resolution LAI Based on the NARX Neural Network and MODIS Times Series." Remote Sensing 11, no. 3: 219.
High-resolution leaf area index (LAI) maps from remote sensing data largely depend on empirical models, which link field LAI measurements to the vegetation index. The existing empirical methods often require the field measurements to be sufficient for constructing a reliable model. However, in many regions of the world, there are limited field measurements available. This paper presents a prior knowledge-based (PKB) method to derivate LAI with limited field measurements, in an effort to improve the accuracy of empirical model. Based on the assumption that the experimental sites with the same vegetation type can be represented by similar models, a priori knowledge for crops was extracted from the published models in various cropland sites. The knowledge, composed of an initial guess of each model parameter with the associated uncertainty, was then combined with the local field measurements to determine a semi-empirical model using the Bayesian inversion method. The proposed method was evaluated at a cropland site in the Huailai region of Hebei Province, China. Compared with the regression method, the proposed PKB method can effectively improve the accuracy of empirical model and LAI estimation, when the field measurements were limited. The results demonstrate that a priori knowledge extracted from the universal sites can provide important auxiliary information to improve the representativeness of the empirical model in a given study area.
Yuechan Shi; Jindi Wang; Jian Wang; Yonghua Qu. A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements. Remote Sensing 2016, 9, 13 .
AMA StyleYuechan Shi, Jindi Wang, Jian Wang, Yonghua Qu. A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements. Remote Sensing. 2016; 9 (1):13.
Chicago/Turabian StyleYuechan Shi; Jindi Wang; Jian Wang; Yonghua Qu. 2016. "A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements." Remote Sensing 9, no. 1: 13.
Upscaling in situ leaf area index (LAI) measurements to the footprint scale is important for the validation of medium resolution remote sensing products. However, surface heterogeneity and temporal variation of vegetation make this difficult. In this study, a two-step upscaling algorithm was developed to obtain the representative ground truth of LAI time series in heterogeneous surfaces based on in situ LAI data measured by the wireless sensor network (WSN) observation system. Since heterogeneity within a site usually arises from the mixture of vegetation and non-vegetation surfaces, the spatial heterogeneity of vegetation and land cover types were separately considered. Representative LAI time series of vegetation surfaces were obtained by upscaling in situ measurements using an optimal weighted combination method, incorporating the expectation maximum (EM) algorithm to derive the weights. The ground truth of LAI over the whole site could then be determined using area weighted combination of representative LAIs of different land cover types. The algorithm was evaluated using a dataset collected in Heihe Watershed Allied Telemetry Experimental Research (HiWater) experiment. The proposed algorithm can effectively obtain the representative ground truth of LAI time series in heterogeneous cropland areas. Using the normal method of an average LAI measurement to represent the heterogeneous surface produced a root mean square error (RMSE) of 0.69, whereas the proposed algorithm provided RMSE = 0.032 using 23 sampling points. The proposed ground truth derived method was implemented to validate four major LAI products.
Yuechan Shi; Jindi Wang; Jun Qin; Yonghua Qu. An Upscaling Algorithm to Obtain the Representative Ground Truth of LAI Time Series in Heterogeneous Land Surface. Remote Sensing 2015, 7, 12887 -12908.
AMA StyleYuechan Shi, Jindi Wang, Jun Qin, Yonghua Qu. An Upscaling Algorithm to Obtain the Representative Ground Truth of LAI Time Series in Heterogeneous Land Surface. Remote Sensing. 2015; 7 (10):12887-12908.
Chicago/Turabian StyleYuechan Shi; Jindi Wang; Jun Qin; Yonghua Qu. 2015. "An Upscaling Algorithm to Obtain the Representative Ground Truth of LAI Time Series in Heterogeneous Land Surface." Remote Sensing 7, no. 10: 12887-12908.