Abstract
Green leaf area index (LAI) is an important variable related to crop growth. Accurate and timely information on LAI is essential for developing suitable field management strategies to mitigate risk and boost yield. Several remote sensing (RS) based methods have been recently developed to estimate LAI at the regional scale. However, the performance of these methods tends to be affected by the quality of RS data, especially when time-series LAI are required. For crop LAI estimation, supplementary growth information from crop model is helpful to address this issue. In this study, we focus on the regional-scale LAI estimations of spring maize for the entire growth season. Using time-series multispectral RS data acquired by an unmanned aerial vehicle (UAV) and the World Food Studies (WOFOST) crop model, three methods were applied at different crop growth stages: empirical method using vegetation index (VI), data assimilation method and hybrid method. The VI-based method and assimilation method were used to generate time-series LAI estimations for the whole crop growth season. Then, a hybrid method specially for the late-stage LAI retrieval was developed by integrating WOFOST model and data assimilation. Using field-collected LAI data in Hongxing Farm in 2014, the performances of these three methods were evaluated. At the early stage, the VI-based method (R2 = 0.63, RMSE = 0.16, n = 36) achieved higher accuracy than the assimilation method (R2 = 0.54, RMSE = 0.52, n = 36), whereas at the mid stage, the assimilation method (R2 = 0.63, RMSE = 0.46, n = 28) showed higher accuracy than the VI-based method (R2 = 0.41, RMSE = 0.51, n = 28). At the late stage, the hybrid method yielded the highest accuracy (R2 = 0.63, RMSE = 0.46, n = 29), compared with the VI-based method (R2 = 0.19, RMSE = 0.43, n = 28) and the assimilation method (R2 = 0.20, RMSE = 0.44, n = 29). Based on the results above, we considered a combination of the three methods, i.e., the VI-based method for the early stage, the assimilation method for the mid stage, and the hybrid method for the late stage, as an ideal strategy for spring-maize LAI estimation for the entire growth season of 2014 in Hongxing Farm, and the accuracy of the combined method over the whole growth season is higher than that of any single method.
Highlights
Leaf area index (LAI) is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as the projected needle leaf area in coniferous canopies [1,2]
The results show that the assimilation can correct World Food Studies (WOFOST)’s LAI simulation to certain extent
The time window used for the three crop growth stages in this study influences the application times of the vegetation index (VI)-based empirical method, assimilation and crop model without data assimilation
Summary
Leaf area index (LAI) is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as the projected needle leaf area in coniferous canopies [1,2]. To achieve accurate LAI estimations, a number of remote sensing (RS) based approaches have been developed over the past few decades. These approaches can be grouped into two broad categories: empirical methods and physical models [6]. A series of optical vegetation indices (VIs) have been developed through combinations of reflectance in two or more bands [8,9] for maize LAI estimation. A normalized difference vegetation index (NDVI) [10] calculated from the reflectance in red and near-infrared bands are commonly used for retrieving canopy biophysical properties of corn [11]
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