Spectroscopic estimation of leaf water content in commercial vineyards using continuum removal and partial least squares regression

González-Fernández, Ana Belén; Rodríguez‐Pérez, José Ramón; Marabel, Miguel; Taboada, María Flor Álvarez

doi:10.1016/j.scienta.2015.03.012

Cited by 31 publications

(16 citation statements)

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“…The results show that the areas of interest were obtained using near-infrared reflectance. These findings agree with several studies [8,25,45,46] that computed the most suitable relationship between spectral data and vine water content using wavelengths located in the near-infrared. The reason for this could be that wavelengths with maximum water absorption for crops are usually in the near-infrared [45].…”

Section: Discussionsupporting

confidence: 92%

“…The numerous tests we carried out suggest that the optimum bandwidth to be used around these two absorption peaks spans from immediately before to immediately after the maximums. These zones have traditionally been used for the correlation of many variables related to water in plants [8,25,45,47].…”

Section: Discussionmentioning

confidence: 99%

“…The main difference between PLSR and principal component regression is that, while principal component regression decomposes the spectra into a set of eigenvectors and scores and then regresses them against the response variables, PLRS uses response variable information during the decomposition process [24]. PSLR suitability for estimating water leaf content has been corroborated by authors who have estimated vine leaf water content [25] and water content and biochemical parameters [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

“…CR spectral data can be used to estimate water content using PLSR as a statistical method. Some works showed that hyperspectral data and CR transformation improved methods for estimating water status in individual vines [39], with the most suitable models being those that used spectral data pre-processed by CR and PSLR [25].…”

Section: Introductionmentioning

confidence: 99%

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Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards

et al. 2019

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Water status controls plant physiology and is key to managing vineyard grape quality and yield. Water status is usually estimated by leaf water potential (LWP), which is measured using a pressure chamber; however, this method is difficult, time-consuming, and error-prone. While traditional spectral methods based on leaf reflectance are faster and non-destructive, most are based on vegetation indices derived from satellite imagery (and so only take into account discrete bandwidths) and do not take full advantage of modern hyperspectral sensors that capture spectral reflectance for thousands of wavelengths. We used partial least squares regression (PLSR) to predict LWP from reflectance values (wavelength 350–2500 nm) captured with a field spectroradiometer. We first identified wavelength ranges that minimized regression error. We then tested several common data pre-processing methods to analyze the impact on PLSR prediction precision, finding that derivative pre-processing increased the determination coefficients of our models and reduced root mean squared error (RMSE). The models fitted with raw data obtained their best results at around 1450 nm, while the models with derivative pre-processed achieved their best estimates at 826 nm and 1520 nm.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards

et al. 2019

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“…The water content estimation from the spectral signature has been widely studied for a broad variety of vegetation species. Based on the review of the literature, we selected eighteen vegetation indexes that appear frequently in studies related to the use of VIs for the water content estimation (see Table 2 for VIs nomenclatures, references, and a brief description [59][60][61][62][63][64][65][66][67][68][69][70]). For example, in [59] the MSI, NDWI, TM5/TM7, and WI were used to estimate the leaf FMC, and EWT from remotely sensed reflectance.…”

Section: Water Content and Vegetation Indexesmentioning

confidence: 99%

Foliar Moisture Content from the Spectral Signature for Wildfire Risk Assessments in Valparaíso-Chile

Villacrés

Arevalo-Ramirez

Fuentes

et al. 2019

Sensors

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Fuel moisture content (FMC) proved to be one of the most relevant parameters for controlling fire behavior and risk, particularly at the wildland-urban interface (WUI). Data relating FMC to spectral indexes for different species are an important requirement identified by the wildfire safety community. In Valparaíso, the WUI is mainly composed of Eucalyptus Globulus and Pinus Radiata—commonly found in Mediterranean WUI areas—which represent the 97.51% of the forests plantation inventory. In this work we study the spectral signature of these species under different levels of FMC. In particular, we analyze the behavior of the spectral reflectance per each species at five dehydration stages, obtaining eighteen spectral indexes related to water content and, for Eucalyptus Globulus, the area of each leave—associated with the water content—is also computed. As the main outcome of this research, we provide a validated linear regression model associated with each spectral index and the fuel moisture content and moisture loss, per each species studied.

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Analysis of Plant Stress Response Using Hyperspectral Imaging and Kernel Ridge Regression

Asaari

Mertens

Dhondt

et al. 2022

Lecture Notes in Electrical Engineering

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Spectroscopic estimation of leaf water content in commercial vineyards using continuum removal and partial least squares regression

Cited by 31 publications

References 50 publications

Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards

Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards

Foliar Moisture Content from the Spectral Signature for Wildfire Risk Assessments in Valparaíso-Chile

Analysis of Plant Stress Response Using Hyperspectral Imaging and Kernel Ridge Regression

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