Efficient RTM-based training of machine learning regression algorithms to quantify biophysical &amp; biochemical traits of agricultural crops

Danner, Martin; Berger, Katja; Wocher, Matthias; Mauser, Wolfram; Hank, Tobias

doi:10.1016/j.isprsjprs.2021.01.017

Cited by 104 publications

(69 citation statements)

References 73 publications

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“…This problem limits the vegetation parameter hybrid retrieval based on the PROSAIL simulations, as shown in Sections 3.3 and 3.4 of this paper. Establishing model parameterization schemes according to actual scenes or research objects is an important way to improve the simulation accuracy of vegetation RTMs [19,59,60]. For the PROSAIL model, the ALA is considered to be the structural parameter that has the greatest impact on reflectance, besides the LAI [31,61].…”

Section: Contribution Of Our Proposed Methods To Prosail-based Retrieval Of Crop Parametersmentioning

confidence: 99%

“…Compared to empirical statistical models, machine learning methods have a stronger learning ability to fit complex nonlinear relationships. Machine learning methods, such as the support vector machine (SVM), artificial neural network (ANN), and random forest regression (RFR), have been widely used to retrieve vegetation parameters [16][17][18][19]. Nevertheless, training an SVM with high-dimensional data can be extremely slow [20], while ANN is prone to overfitting, and the parameter setting in ANN is more complicated [21].…”

Section: Introductionmentioning

confidence: 99%

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A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

et al. 2021

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Canopy chlorophyll content (CCC) is an important indicator for crop-growth monitoring and crop productivity estimation. The hybrid method, involving the PROSAIL radiative transfer model and machine learning algorithms, has been widely applied for crop CCC retrieval. However, PROSAIL’s homogeneous canopy hypothesis limits the ability to use the PROSAIL-based CCC estimation across different crops with a row structure. In addition to leaf area index (LAI), average leaf angle (ALA) is the most important canopy structure factor in the PROSAIL model. Under the same LAI, adjustment of the ALA can make a PROSAIL simulation obtain the same canopy gap as the heterogeneous canopy at a specific observation angle. Therefore, parameterization of an adjusted ALA (ALAadj) is an optimal choice to make the PROSAIL model suitable for specific row-planted crops. This paper attempted to improve PROSAIL-based CCC retrieval for different crops, using a random forest algorithm, by introducing the prior knowledge of crop-specific ALAadj. Based on the field reflectance spectrum at nadir, leaf area index, and leaf chlorophyll content, parameterization of the ALAadj in the PROSAIL model for wheat and soybean was carried out. An algorithm integrating the random forest and PROSAIL simulations with prior ALAadj information was developed for wheat and soybean CCC retrieval. Ground-measured CCC measurements were used to validate the CCC retrieved from canopy spectra. The results showed that the ALAadj values (62 degrees for wheat; 45 degrees for soybean) that were parameterized for the PROSAIL model demonstrated good discrimination between the two crops. The proposed algorithm improved the CCC retrieval accuracy for wheat and soybean, regardless of whether continuous visible to near-infrared spectra with 50 bands (RMSE from 39.9 to 32.9 μg cm−2; R2 from 0.67 to 0.76) or discrete spectra with 13 bands (RMSE from 43.9 to 33.7 μg cm−2; R2 from 0.63 to 0.74) and nine bands (RMSE from 45.1 to 37.0 μg cm−2; R2 from 0.61 to 0.71) were used. The proposed hybrid algorithm, based on PROSAIL simulations with ALAadj, has the potential for satellite-based CCC estimation across different crop types, and it also has a good reference value for the retrieval of other crop parameters.

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Section: Contribution Of Our Proposed Methods To Prosail-based Retrieval Of Crop Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

et al. 2021

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“…The emergence of plant phenotyping in recent years has brought new perspectives to horticulture research. By combining advanced sensors and automation technology, more physiological and morphological traits across plant growth and development can be assessed, e.g., daily evapotranspiration 60 , crop biomass and leaf area indices 61 , seed germination 62 , and biophysical and biochemical traits 63 . In particular, it has been shown that many machine learning- and computer vision-based analytic methods improve phenotyping accuracy, reliability, and speed.…”

Section: Discussionmentioning

confidence: 99%

MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

et al. 2021

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Recognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources. Although leaf image-based methods have been widely adopted in plant species identification, they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars. Here, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN). Persistent homology, a multiscale and robust method, was employed to extract the topological signatures of leaf shape, texture, and venation details. A CNN-based algorithm, the Xception network, was fine-tuned for extracting high-level leaf image features. For fruit species, we benchmarked the MFCIS pipeline on a sweet cherry (Prunus avium L.) leaf dataset with >5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%. For annual crop species, we applied the MFCIS pipeline to a soybean (Glycine max L. Merr.) leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods. The identification models for each growth period were trained independently, and their results were combined using a score-level fusion strategy. The classification accuracy after score-level fusion was 91.4%, which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods. To facilitate the adoption of the proposed pipelines, we constructed a user-friendly web service, which is freely available at http://www.mfcis.online.

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“…The physical methods are based on the inversion of canopy radiative transfer models. Among the inversion techniques, the look up tables (LUTs) are widely used in operational algorithms to process large amounts of remote sensing data due to its ability to speed up the inversion process [6]. Hybrid methods combine physical models with the computational efficiency of non-parametric regression methods [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…Among the inversion techniques, the look up tables (LUTs) are widely used in operational algorithms to process large amounts of remote sensing data due to its ability to speed up the inversion process [6]. Hybrid methods combine physical models with the computational efficiency of non-parametric regression methods [6,7]. Machine learning techniques including Neural Networks (NNs) and Gaussian Process Regression (GPR) are widely used.…”

Section: Introductionmentioning

confidence: 99%

Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops

Yin

Liu

et al. 2021

Remote Sensing

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We proposed a direct approach to validate hectometric and kilometric resolution leaf area index (LAI) products that involved the scaling up of field-measured LAI via the validation and recalibration of the decametric Sentinel-2 LAI product. We applied it over a test study area of maize crops in northern China using continuous field measurements of LAINet along the year 2019. Sentinel-2 LAI showed an overall accuracy of 0.67 in terms of Root Mean Square Error (RMSE) and it was used, after recalibration, as a benchmark to validate six coarse resolution LAI products: MODIS, Copernicus Global Land Service 1 km Version 2 (called GEOV2) and 300 m (GEOV3), Satellite Application Facility EUMETSAT Polar System (SAF EPS) 1.1 km, Global LAnd Surface Satellite (GLASS) 500 m and Copernicus Climate Change Service (C3S) 1 km V2. GEOV2, GEOV3 and MODIS showed a good agreement with reference LAI in terms of magnitude (RMSE ≤ 0.29) and phenology. SAF EPS (RMSE = 0.68) and C3S V2 (RMSE = 0.41), on the opposite, systematically underestimated high LAI values and showed systematic differences for phenological metrics: a delay of 6 days (d), 20 d and 24 d for the start, peak and the end of growing season, respectively, for SAF EPS and an advance of −4 d, −6 d and −6 d for C3S.

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Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops

Cited by 104 publications

References 73 publications

A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops

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