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Research on Inversion of 10-meter Potato Leaf Area Index in Complex Terrain Area of Southwest China
LUXiaoning, XUDandan, LIUKe, XUWeixin, XUBaodong
Chin Agric Sci Bull ›› 2025, Vol. 41 ›› Issue (34) : 147-156.
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Abbreviation (ISO4): Chin Agric Sci Bull
Editor in chief: Yulong YIN
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Research on Inversion of 10-meter Potato Leaf Area Index in Complex Terrain Area of Southwest China
To achieve high-accuracy inversion of potato leaf area index (LAI) in the complex terrain of Southwest China and explain factors potentially affecting retrieval accuracy from the perspectives of surface spatial heterogeneity and model parameters, this study focused on potato cultivation areas in Zhaojue and Butuo Counties, Liangshan Prefecture, Sichuan Province. Based on Sentinel-2 imagery, an LAI inversion algorithm integrating the PROSAIL model and a neural network (NN) within SNAP was employed to achieve simple, efficient, and small-sample 10-meter LAI inversion with field validation. A comparison with the 500-m MODIS LAI product was further conducted to assess the advantages of high-resolution LAI in revealing spatial heterogeneity. The results showed that: (1) the 10-meter LAI retrieved using the PROSAIL+NN algorithm exhibited high overall accuracy, with a coefficient of determination (R2) of 0.86 and a root mean square error (RMSE) of 0.28 when compared with field measurements. Regional differences were evident—the retrieval accuracy in Butuo County sample area (RMSE=0.15) exceeded that in Zhaojue County sample area (RMSE=0.38), primarily associated with more complex topography (slopes 1°-14°) and higher surface heterogeneity. (2) Within 500-m pixels, the 10-meter LAI revealed an average within-pixel LAI variance of 2.17, which was 1.6 times the mean LAI of the study area. Furthermore, the maximum and minimum values of the MODIS LAI product were lower by 0.1 and 0.47, respectively, than the corresponding values from this study, and partial null pixels were present. This confirmed that the 10-meter LAI significantly surpasses the traditional 500-meter MODIS LAI product in quantifying spatial heterogeneity and ensuring data completeness. (3) Retrieval accuracy was also constrained by model input parameters: the SNAP default LAI range significantly exceeded the actual potato LAI range, resulting in some anomalous values. Additionally, insufficient representativeness of soil reflectance (ρsoil) and overestimation of the Band 12 reflectance after atmospheric correction (maximum value 0.7, exceeding the model's valid input range of 0.5) increased uncertainty. The PROSAIL+NN inversion method used in this study enables simple and efficient acquisition of 10-meter LAI products, allowing for precise characterization of the spatial heterogeneity in potato growth across the complex topography of Southwest China. With minimal ground measurements, strong mechanistic underpinnings and transferability, this approach can serve as an effective pathway for crop information acquisition in the mountainous regions of Southwest China, supporting digital agriculture, crop monitoring, and agricultural management.
leaf area index / remote sensing inversion / potatoes / Sentinel-2 / precision validation / southwest mountainous region / digital agriculture
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Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.
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Leaf area index (LAI) and chlorophyll content, at leaf and canopy level, are important variables for agricultural applications because of their crucial role in photosynthesis and in plant functioning. The goal of this study was to test the hypothesis that LAI, leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) of a potato crop can be estimated by vegetation indices for the first time using Sentinel-2 satellite images. In 2016 ten plots of 30 × 30 m were designed in a potato field with different fertilization levels. During the growing season approximately 10 daily radiometric field measurements were used to determine LAI, LCC, and CCC. These radiometric determinations were extensively calibrated against LAI2000 and chlorophyll meter (SPAD, soil plant analysis development) measurements for potato crops grown in the years 2010–2014. Results for Sentinel-2 showed that the weighted difference vegetation index (WDVI) using bands at 10 m spatial resolution can be used for estimating the LAI (R2 of 0.809; root mean square error of prediction (RMSEP) of 0.36). The ratio of the transformed chlorophyll in reflectance index and the optimized soil-adjusted vegetation index (TCARI/OSAVI) showed to be a good linear estimator of LCC at 20 m (R2 of 0.696; RMSEP of 0.062 g·m−2). The performance of the chlorophyll vegetation index (CVI) at 10 m spatial resolution was slightly worse (R2 of 0.656; RMSEP of 0.066 g·m−2) compared to TCARI/OSAVI. Finally, results showed that the green chlorophyll index (CIgreen) was an accurate and linear estimator of CCC at 10 m (R2 of 0.818; RMSEP of 0.29 g·m−2). Results for CIgreen were better than for the red-edge chlorophyll index (CIred-edge, R2 of 0.576, RMSE of 0.43 g·m−2). Our results show that Sentinel-2 bands at 10 m spatial resolution are suitable for estimating LAI, LCC, and CCC, avoiding the need for red-edge bands that are only available at 20 m. This is an important finding for applying Sentinel-2 data in precision agriculture.
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