Estimating Leaf Area Index (LAI) in Vineyards Using the PocketLAI Smart-App

Orlando, Francesca; Movedi, Ermes; Coduto, Davide; Parisi, Simone; Brancadoro, Lucio; Pagani, Valentina; Guarneri, Tommaso; Confalonieri, Roberto

doi:10.3390/s16122004

Cited by 33 publications

(26 citation statements)

References 39 publications

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“…This application has been used in several previous works with satisfactory results, mainly on rice crop studies [84,85] but also on other crop types such as vineyards [86]. In this last study [86], both PocketLAI and the digital hemispherical photography (DHP) instrument were used and compared with destructive LAI values, observing that PocketLAI measurements were closer to the ground-truth (RRMSE = 43.00% for PocketLAI; RRMSE = 99.46% for DHP). Furthermore, in a recent comparative study of different LAI measurement instruments against destructive LAI values [39], the PocketLAI was found to a good alternative for the LAI operational measurement due to its cost-effective and similar results to destructive LAI values, obtaining a RMSE of < 0.65 for alfalfa, broad bean and maize crop types.…”

Section: Discussionmentioning

confidence: 88%

“…In this study, the LAI data from Caserta, Tarquinia and Valencia were taken with the standard LAI instrument but the Bahía Blanca (Argentina) data were obtained with the PocketLAI smartphone application [83]. This application has been used in several previous works with satisfactory results, mainly on rice crop studies [84,85] but also on other crop types such as vineyards [86]. In this last study [86], both PocketLAI and the digital hemispherical photography (DHP) instrument were used and compared with destructive LAI values, observing that PocketLAI measurements were closer to the ground-truth (RRMSE = 43.00% for PocketLAI; RRMSE = 99.46% for DHP).…”

Section: Discussionmentioning

confidence: 99%

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Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach

Pasqualotto

D’Urso

Bolognesi³

et al. 2019

Agronomy

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Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this study performed a comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 > 0.70, root mean square error (RMSE) < 0.86) and CCC (R2 > 0.75, RMSE < 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 > 0.71, RMSE < 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 > 0.67, RMSE < 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 > 0.63 and a RMSE < 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE ≈ 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived from FAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 > 0.90, RMSE < 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas.

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Section: Discussionmentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach

Pasqualotto

D’Urso

Bolognesi³

et al. 2019

Agronomy

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“…Despite this it is worth underlining that our trueness metrics were obtained from artificially generated images, they are better than those achieved by other authors with methods for LAI estimates based on image segmentation (e.g., [ 4 ]). The same consideration is valid for the trueness metrics reported for DHP by [ 30 , 31 ] for forest canopies and by [ 24 ] for vineyards.…”

Section: Resultsmentioning

confidence: 79%

“…Data used for precision determination—described in detail by [ 10 ]—were acquired using a smartphone Samsung GT-i9105 Galaxy S II Plus equipped with a fisheye lens AKASHI ALTLENS4IN1SG2. In the current study, only the images collected by [ 24 ] on broad-leaved canopies were used. Images were acquired using two different protocols: for row-structured plantations, images were acquired moving parallel to the row, whereas in case of sparse or continuous canopies, they were acquired moving around the trunk at a distance of approximately half crown radius.…”

Section: Methodsmentioning

confidence: 99%

Quantifying the Accuracy of Digital Hemispherical Photography for Leaf Area Index Estimates on Broad-Leaved Tree Species

Gilardelli

Orlando

Movedi

et al. 2018

Sensors

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Digital hemispherical photography (DHP) has been widely used to estimate leaf area index (LAI) in forestry. Despite the advancement in the processing of hemispherical images with dedicated tools, several steps are still manual and thus easily affected by user’s experience and sensibility. The purpose of this study was to quantify the impact of user’s subjectivity on DHP LAI estimates for broad-leaved woody canopies using the software Can-Eye. Following the ISO 5725 protocol, we quantified the repeatability and reproducibility of the method, thus defining its precision for a wide range of broad-leaved canopies markedly differing for their structure. To get a complete evaluation of the method accuracy, we also quantified its trueness using artificial canopy images with known canopy cover. Moreover, the effect of the segmentation method was analysed. The best results for precision (restrained limits of repeatability and reproducibility) were obtained for high LAI values (>5) with limits corresponding to a variation of 22% in the estimated LAI values. Poorer results were obtained for medium and low LAI values, with a variation of the estimated LAI values that exceeded the 40%. Regardless of the LAI range explored, satisfactory results were achieved for trees in row-structured plantations (limits almost equal to the 30% of the estimated LAI). Satisfactory results were achieved for trueness, regardless of the canopy structure. The paired t-test revealed that the effect of the segmentation method on LAI estimates was significant. Despite a non-negligible user effect, the accuracy metrics for DHP are consistent with those determined for other indirect methods for LAI estimates, confirming the overall reliability of DHP in broad-leaved woody canopies.

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“…Francone et al [48] compared Pocket-LAI app values with an AccuPAR ceptometer over maize and grassland fields, obtaining differences lower than 0.5 m 2 /m 2 . Orlando et al [49] evaluated Pocket-LAI over vineyard, using as a reference a DHP and direct methods. The accuracy obtained in these cases was ±0.57 m 2 /m 2 when the reference was DHP measurements, and ±0.16 m 2 /m 2 when the reference was a direct method.…”

Section: Pocket-laimentioning

confidence: 99%

Evaluation of Six Directional Canopy Emissivity Models in the Thermal Infrared Using Emissivity Measurements

et al. 2019

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Land surface temperature (LST) is a fundamental physical quantity in a range of different studies, for example in climatological analyses and surface–atmosphere heat flux assessments, especially in heterogeneous and complex surfaces such as vegetated canopies. To obtain accurate LST values, it is important to measure accurately the land surface emissivity (LSE) in the thermal infrared spectrum. In the past decades, different directional emissivity canopy models have been proposed. This paper evaluates six radiative transfer models (FR97, Mod3, Rmod3, 4SAIL, REN15, and CE-P models) through a comparison with in situ emissivity measurements performed using the temperature-emissivity separation (TES) method. The evaluation is done using a single set of rose plants over two different soils with very different spectral behavior. First, using an organic soil, the measurements were done for seven different observation angles, from 0° to 60° in steps of 10°, and for six different values of leaf area index (LAI). Taking into account all LAIs, the bias (and root mean square error, RMSE) obtained were 0.003 (±0.006), −0.004 (±0.005), −0.009 (±0.011), 0.005 (±0.007), 0.004 (±0.007), and 0.005 (±0.007) for FR97, Mod3, Rmod3, 4SAIL, REN 15, and CE-P models, respectively. Second, using an inorganic soil, the measurements were done for six different LAIs but for two different observation angles: 0° and 55°. The bias (and RMSE) obtained were 0.012 (±0.014), 0.004 (±0.007), −0.020 (±0.035), 0.016 (±0.017), 0.013 (±0.015), 0.013 (±0.015) and for FR97, Mod3, Rmod3, 4SAIL, REN15, and CE-P models, respectively. Overall, the Mod3 model appears as the best model in comparison to the TES emissivity reference measurements.

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Estimating Leaf Area Index (LAI) in Vineyards Using the PocketLAI Smart-App

Cited by 33 publications

References 39 publications

Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach

Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach

Quantifying the Accuracy of Digital Hemispherical Photography for Leaf Area Index Estimates on Broad-Leaved Tree Species

Evaluation of Six Directional Canopy Emissivity Models in the Thermal Infrared Using Emissivity Measurements

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