Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India

Sinha, Sanjiv; Padalia, Hitendra; Dasgupta, Anindita; Verrelst, Jochem; Rivera, Juan Pablo

doi:10.1016/j.jag.2019.102027

Cited by 47 publications

(25 citation statements)

References 59 publications

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“…Moreover, several authors [76][77][78] highlight that although NDVI is commonly used to retrieve LAI values, it is not always the VI with the highest correlation. In particular, for S-2, the newest of the three satellites, alternative Vis may be considered to improve results [32,[79][80][81].…”

Section: Discussionmentioning

confidence: 99%

Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream

2020

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In recent decades, technological advancements in sensors have generated increasing interest in remote sensing data for the study of vegetation features. Image pixel resolution can affect data analysis and results. This study evaluated the potential of three satellite images of differing resolution (Landsat 8, 30 m; Sentinel-2, 10 m; and Pleiades 1A, 2 m) in assessing the Leaf Area Index (LAI) of riparian vegetation in two Mediterranean streams, and in both a winter wheat field and a deciduous forest used to compare the accuracy of the results. In this study, three different retrieval methods—the Caraux-Garson, the Lambert-Beer, and the Campbell and Norman equations—are used to estimate LAI from the Normalized Difference Vegetation Index (NDVI). To validate sensor data, LAI values were measured in the field using the LAI 2200 Plant Canopy Analyzer. The statistical indices showed a better performance for Pleiades 1A and Landsat 8 images, the former particularly in sites characterized by high canopy closure, such as deciduous forests, or in areas with stable riparian vegetation, the latter where stable reaches of riparian vegetation cover are almost absent or very homogenous, as in winter wheat fields. Sentinel-2 images provided more accurate results in terms of the range of LAI values. Considering the different types of satellite imagery, the Lambert-Beer equation generally performed best in estimating LAI from the NDVI, especially in areas that are geomorphologically stable or have a denser vegetation cover, such as deciduous forests.

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Section: Discussionmentioning

confidence: 99%

Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream

2020

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“…Satellite remote sensing in comparison with other platforms has the advantage of offering readily available, sometimes free of charge, reflectance data over large areas. Satellites can also support monitoring programs through time spans of several years, ensuring long term information continuity through global LAI products (Defourny et al, 2019;Fang et al, 2019;Sinha et al, 2020). Most available global LAI products such as "LAI 300 m", "LAI 1 km" (both from Copernicus Global Land Service), and "MODIS Leaf Area Index" (NASA), however, cannot offer LAI estimates at a spatial resolution higher than 300 m, which makes them unsuitable for precision agriculture.…”

Section: Introductionmentioning

confidence: 99%

Processing of remote sensing information to retrieve leaf area index in barley: a comparison of methods

Rosso

Nendel

Gilardi³

et al. 2022

Precision Agric

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Leaf area index (LAI) is a key variable in understanding and modeling crop-environment interactions. With the advent of increasingly higher spatial resolution satellites and sensors mounted on remotely piloted aircrafts (RPAs), the use of remote sensing in precision agriculture is becoming more common. Since also the availability of methods to retrieve LAI from image data have also drastically expanded, it is necessary to test simultaneously as many methods as possible to understand the advantages and disadvantages of each approach. Ground-based LAI data from three years of barley experiments were related to remote sensing information using vegetation indices (VI), machine learning (ML) and radiative transfer models (RTM), to assess the relative accuracy and efficacy of these methods. The optimized soil adjusted vegetation index and a modified version of the Weighted Difference Vegetation Index performed slightly better than any other retrieval method. However, all methods yielded coefficients of determination of around 0.7 to 0.9. The best performing machine learning algorithms achieved higher accuracies when four Sentinel-2 bands instead of 12 were used. Also, the good performance of VIs and the satisfactory performance of the 4-band RTM, strongly support the synergistic use of satellites and RPAs in precision agriculture. One of the methods used, Sen2-Agri, an open source ML-RTM-based operational system, was also able to accurately retrieve LAI, although it is restricted to Sentinel-2 and Landsat data. This study shows the benefits of testing simultaneously a broad range of retrieval methods to monitor crops for precision agriculture.

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“…Empirical regression models based on VIs are widely used due to their simplicity and robustness. A common practice is modeling from the field vegetation parameters (e.g., Cab or leaf area index) and the remote sensing data (e.g., VIs or reflectance) [18]. Most VIs are calculated from the visible, red-edge, and near-infrared spectral domains, such as the widely used normalized difference vegetation index (NDVI), modified chlorophyll absorption ratio index (MCARI), and the double-peak canopy nitrogen index (DCNI) [19].…”

Section: Introductionmentioning

confidence: 99%

Improving the Estimation of Apple Leaf Photosynthetic Pigment Content Using Fractional Derivatives and Machine Learning

Cheng

Yang

et al. 2022

Agronomy

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As a key functional trait, leaf photosynthetic pigment content (LPPC) plays an important role in the health status monitoring and yield estimation of apples. Hyperspectral features including vegetation indices (VIs) and derivatives are widely used in retrieving vegetation biophysical parameters. The fractional derivative spectral method shows great potential in retrieving LPPC. However, the performance of fractional derivatives and machine learning (ML) for retrieving apple LPPC still needs to be explored. The objective of this study is to test the capacity of using fractional derivative and ML methods to retrieve apple LPPC. Here, the hyperspectral data in the 400–2500 nm domains was used to calculate the fractional derivative order of 0.2–2, and then the sensitive bands were screened through feature dimensionality reduction to train ML to build the LPPC estimation model. Additionally, VIs-based ML methods and empirical regression models were developed to compare with the fractional derivative methods. The results showed that fractional derivative-driven ML methods have higher accuracy than the ML methods driven by the original spectra or vegetation index. The results also showed that the ML methods perform better than empirical regression models. Specifically, the best estimates of chlorophyll content and carotenoid content were achieved using support vector regression (SVR) at the derivative order of 0.2 (R2 = 0.78) and 0.4 (R2 = 0.75), respectively. The fractional derivative maintained a good universality in retrieving the LPPC of multiple phenological periods. Therefore, this study highlights that the fractional derivative and ML improved the estimation of apple LPPC.

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Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India

Cited by 47 publications

References 59 publications

Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream

Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream

Processing of remote sensing information to retrieve leaf area index in barley: a comparison of methods

Improving the Estimation of Apple Leaf Photosynthetic Pigment Content Using Fractional Derivatives and Machine Learning

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