Abstract: Near-real-time, high-spatial-resolution leaf area index (LAI) maps would enable producers to monitor crop health and growth status, improving agricultural practices such as fertiliser and water management. LAI retrieval methods are numerous and can be divided into statistical and physically based methods. While statistical methods are generally subject to high site-specificity but possess high ease of implementation and use, physically based methods are more transferable, albeit more complex to use in operational settings. In addition, statistical methods need a large amount of data for calibration and subsequent validation, and this is only seldom feasible. Techniques based on predictive equations (PEphysical) represent a viable alternative, allowing the partial combination of statistical and physical methods merits while minimising their shortcomings. In this paper, predictive equation-based techniques were compared with four other methods: two radiative transfer model (RTM) inversion methods, one based on neural network (NNET) and one based on a look-up table (LUT), and two empirical methods (one using empirical models based on vegetation indices and in situ data and one based on empirical models found in the scientific literature). The methods were chosen based on common use. To evaluate the performance of the studied methods, the coefficient of determination (R2), root mean square error (RMSE), and normalised root mean square error (nRMSE, %) between the estimates and in situ LAI measurements were reported. The best PEphysical results, achieved by the OSAVI index (RMSE = 0.84 m2 m􀀀2), provided better performance for LAI recovery than the NNET-based RTM inversions (0.86 m2 m􀀀2) or the estimates made by LUT (0.94 m2 m􀀀2). Furthermore, the best PEphysical produced accuracies comparable to the best empirical model (RMSE = 0.71 m2 m􀀀2), calibrated through in situ data, and similar to the best literature model (RMSE = 0.76 m2 m􀀀2). These results indicated that PEphysical can be used to recover LAI with transferability comparable to literature models.

RTM Inversion through Predictive Equations for Multi-Crop LAI Retrieval Using Sentinel-2 Images / Croci, Michele; Impollonia, Giorgio; Marcone, Andrea; Antonucci, Giulia; Letterio, Tommaso; Colauzzi, Michele; Vignudelli, Marco; Ventura, Francesca; Anconelli, Stefano; Amaducci, Stefano. - In: AGRONOMY. - ISSN 2073-4395. - ELETTRONICO. - 12:11(2022), pp. 2835.1-2835.17. [10.3390/agronomy12112835]

RTM Inversion through Predictive Equations for Multi-Crop LAI Retrieval Using Sentinel-2 Images

Vignudelli, Marco
Membro del Collaboration Group
;
Ventura, Francesca
Membro del Collaboration Group
;
2022

Abstract

Abstract: Near-real-time, high-spatial-resolution leaf area index (LAI) maps would enable producers to monitor crop health and growth status, improving agricultural practices such as fertiliser and water management. LAI retrieval methods are numerous and can be divided into statistical and physically based methods. While statistical methods are generally subject to high site-specificity but possess high ease of implementation and use, physically based methods are more transferable, albeit more complex to use in operational settings. In addition, statistical methods need a large amount of data for calibration and subsequent validation, and this is only seldom feasible. Techniques based on predictive equations (PEphysical) represent a viable alternative, allowing the partial combination of statistical and physical methods merits while minimising their shortcomings. In this paper, predictive equation-based techniques were compared with four other methods: two radiative transfer model (RTM) inversion methods, one based on neural network (NNET) and one based on a look-up table (LUT), and two empirical methods (one using empirical models based on vegetation indices and in situ data and one based on empirical models found in the scientific literature). The methods were chosen based on common use. To evaluate the performance of the studied methods, the coefficient of determination (R2), root mean square error (RMSE), and normalised root mean square error (nRMSE, %) between the estimates and in situ LAI measurements were reported. The best PEphysical results, achieved by the OSAVI index (RMSE = 0.84 m2 m􀀀2), provided better performance for LAI recovery than the NNET-based RTM inversions (0.86 m2 m􀀀2) or the estimates made by LUT (0.94 m2 m􀀀2). Furthermore, the best PEphysical produced accuracies comparable to the best empirical model (RMSE = 0.71 m2 m􀀀2), calibrated through in situ data, and similar to the best literature model (RMSE = 0.76 m2 m􀀀2). These results indicated that PEphysical can be used to recover LAI with transferability comparable to literature models.
2022
RTM Inversion through Predictive Equations for Multi-Crop LAI Retrieval Using Sentinel-2 Images / Croci, Michele; Impollonia, Giorgio; Marcone, Andrea; Antonucci, Giulia; Letterio, Tommaso; Colauzzi, Michele; Vignudelli, Marco; Ventura, Francesca; Anconelli, Stefano; Amaducci, Stefano. - In: AGRONOMY. - ISSN 2073-4395. - ELETTRONICO. - 12:11(2022), pp. 2835.1-2835.17. [10.3390/agronomy12112835]
Croci, Michele; Impollonia, Giorgio; Marcone, Andrea; Antonucci, Giulia; Letterio, Tommaso; Colauzzi, Michele; Vignudelli, Marco; Ventura, Francesca; Anconelli, Stefano; Amaducci, Stefano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/909905
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