Advantages of retrieving pigment content [μg/cm2] versus concentration [%] from canopy reflectance

Kattenborn, Teja; Schiefer, Felix; Zarco‐Tejada, Pablo J.; Schmidtlein, Sebastian

doi:10.1016/j.rse.2019.05.014

Cited by 41 publications

(24 citation statements)

References 28 publications

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“…Random forest regression (RFR) models for barley whole crop dry biomass (DM) and leaf area index (LAI) built from UAV-borne images achieved good prediction accuracies. LAI could be predicted with high accuracy which is in line with previous findings [75][76][77] and was expected due to the strong correlation of reflectance in the visible spectrum and leaf pigments per area [78]. Different crop density near tree rows, indirectly reflected by LAI, thus was accurately depicted by the presented models.…”

Section: Model Performancesupporting

confidence: 89%

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

et al. 2021

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Agroforestry systems (AFS) can provide positive ecosystem services while at the same time stabilizing yields under increasingly common drought conditions. The effect of distance to trees in alley cropping AFS on yield-related crop parameters has predominantly been studied using point data from transects. Unmanned aerial vehicles (UAVs) offer a novel possibility to map plant traits with high spatial resolution and coverage. In the present study, UAV-borne red, green, blue (RGB) and multispectral imagery was utilized for the prediction of whole crop dry biomass yield (DM) and leaf area index (LAI) of barley at three different conventionally managed silvoarable alley cropping agroforestry sites located in Germany. DM and LAI were modelled using random forest regression models with good accuracies (DM: R² 0.62, nRMSEp 14.9%, LAI: R² 0.92, nRMSEp 7.1%). Important variables for prediction included normalized reflectance, vegetation indices, texture and plant height. Maps were produced from model predictions for spatial analysis, showing significant effects of distance to trees on DM and LAI. Spatial patterns differed greatly between the sampled sites and suggested management and soil effects overriding tree effects across large portions of 96 m wide crop alleys, thus questioning alleged impacts of AFS tree rows on yield distribution in intensively managed barley populations. Models based on UAV-borne imagery proved to be a valuable novel tool for prediction of DM and LAI at high accuracies, revealing spatial variability in AFS with high spatial resolution and coverage.

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Section: Model Performancesupporting

confidence: 89%

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

et al. 2021

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“…Equally as important, for in-field conditions, the SWIR region did not consistently improve protein estimations, either when expressed as CP m or as %CP (Figure 8-SWIR and FS), which does not justify its use for CP estimation. These results extend the findings of Kattenborn et al [25], where the authors explicitly state that chlorophyll could be best estimated in its µg.cm -2 form rather than on a %DM basis.…”

Section: Discussionsupporting

confidence: 90%

“…In contrast to transformations, reflectance measurements can be executed in sparse bands, with broader bandwidths and coarser radiometric resolution. Hence, a plausible alternative is the (ii) estimation of biochemical attributes in terms of mass per area through reflectance measurements [25].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level

et al. 2020

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Crude protein estimation is an important parameter for perennial ryegrass (Loliumperenne) management. This study aims to establish an effective and affordable approach for a non-destructive, near-real-time crude protein retrieval based solely on top-of-canopy reflectance. The study contrasts different spectral ranges while selecting a minimal number of bands and analyzing achievable accuracies for crude protein expressed as a dry matter fraction or on a weight-per-area basis. In addition, the model’s prediction performance in known and new locations is compared. This data collection comprised 266 full-range (350–2500 nm) proximal spectral measurements and corresponding ground truth observations in Australia and the Netherlands from May to November 2018. An exhaustive-search (based on a genetic algorithm) successfully selected band subsets within different regions and across the full spectral range, minimizing both the number of bands and an error metric. For field conditions, our results indicate that the best approach for crude protein estimation relies on the use of the visible to near-infrared range (400–1100 nm). Within this range, eleven sparse broad bands (of 10 nm bandwidth) provide performance better than or equivalent to those of previous studies that used a higher number of bands and narrower bandwidths. Additionally, when using top-of-canopy reflectance, our results demonstrate that the highest accuracy is achievable when estimating crude protein on its weight-per-area basis (RMSEP 80 kg.ha−1). These models can be employed in new unseen locations, resulting in a minor decrease in accuracy (RMSEP 85.5 kg.ha−1). Crude protein as a dry matter fraction presents a bottom-line accuracy (RMSEP) ranging from 2.5–3.0 percent dry matter in optimal models (requiring ten bands). However, these models display a low explanatory ability for the observed variability (R2 > 0.5), rendering them only suitable for qualitative grading.

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“…This response can be capitalized on to estimate LAI value. However, leaf reflectance in the 400-700 nm wavelength interval is influenced primarily by the pigments chlorophyll and carotenoid [47,48]. To avoid noise from interfering factors and adverse variation to LAI, the usage of visible wavelength needs to be decreased.…”

Section: New Vegetation Index Proposed In This Researchmentioning

confidence: 99%

A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index

et al. 2019

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Leaf area index (LAI) is a key parameter in plant growth monitoring. For several decades, vegetation indices-based empirical method has been widely-accepted in LAI retrieval. A growing number of spectral indices have been proposed to tailor LAI estimations, however, saturation effect has long been an obstacle. In this paper, we classify the selected 14 vegetation indices into five groups according to their characteristics. In this study, we proposed a new index for LAI retrieval-transformed triangular vegetation index (TTVI), which replaces NIR and red bands of triangular vegetation index (TVI) into NIR and red-edge bands. All fifteen indices were calculated and analyzed with both hyperspectral and multispectral data. Best-fit models and k-fold cross-validation were conducted. The results showed that TTVI performed the best predictive power of LAI for both hyperspectral and multispectral data, and mitigated the saturation effect. The R 2 and RMSE values were 0.60, 1.12; 0.59, 1.15, respectively. Besides, TTVI showed high estimation accuracy for sparse (LAI < 4) and dense canopies (LAI > 4). Our study provided the value of the Red-edge bands of the Sentinel-2 satellite sensors in crop LAI retrieval, and demonstrated that the new index TTVI is applicable to inverse LAI for both low-to-moderate and moderate-to-high vegetation cover.LAI remote sensing retrieval methods have been widely investigated for several decades. During the past years, researchers have conducted studies on different vegetation types like broadleaf forest, coniferous forest and crop including soybean, maize and winter wheat [4][5][6]. LAI values vary with different vegetation types for different phenology. According to previous studies, LAI retrieval methods can be classified into three groups: (1) Physical methods like radiative transfer model (RTM), PROSPECT, SAIL models which study the physical mechanisms between light and vegetation to describe the light transmission on inner leaf [7,8] or canopy level [9,10]; (2) vegetation indices-based empirical methods, which engage on the relationships between spectral reflectance data and biophysical or biochemical parameters using statistical models [11][12][13][14][15]; and (3) the new research frontiers like machine learning methods, including artificial neural network and support vector machine to map LAI on large scales [16][17][18][19][20][21][22]. Among these approaches, vegetation indices-based empirical model has been widely used because of its simplicity and computational efficiency.Crop canopy reflectance is dependent both on biophysical parameters like LAI and biochemical parameters like chlorophyll content [23]. To avoid influence from interfering factors including external factors like atmospheric effect, soil background and intrinsic factors like leaf pigment content, leaf inclination angle, saturation effect, and other structural parameters, substantial efforts were conducted in improving classical VIs and developing new indices. Therefore, indices for different purposes were created. A...

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Advantages of retrieving pigment content [μg/cm2] versus concentration [%] from canopy reflectance

Cited by 41 publications

References 28 publications

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level

A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index

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