Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods

Confalonieri, Roberto; Foi, Marco; Casa, Raffaele; Aquaro, Stefano; Tona, E.; Peterle, M.; Boldini, A.; Carli, G.; Ferrari, Alberto; Finotto, G.; Guarneri, Tommaso; Manzoni, V.; Movedi, Ermes; Nisoli, A.; Paleari, Livia; Radici, I.; Suardi, Marco; Veronesi, Davide; Bregaglio, Simone; Cappelli, G.; Chiodini, Marcello Ermido; Dominoni, P.; Francone, Caterina; Frasso, N.; Stella, Tommaso; Acutis, Marco

doi:10.1016/j.compag.2013.04.019

Cited by 137 publications

(102 citation statements)

References 30 publications

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“…PocketLAI estimates can reproduce destructive LAI measurements with acceptable results in terms of both reliability and accuracy [31]. PocketLAI allows the acquisition of in situ LAI measurements at an affordable cost both in computational and human resources and aligned well with estimates obtained using plant canopy analyzers and DHP (digital hemispherical photography) techniques [32].…”

Section: Field Campaignsmentioning

confidence: 87%

“…LAI measurements were acquired using a dedicated smartphone app (PocketLAI), which uses both smartphone's accelerometer and camera to acquire images at 57.5 • below the canopy and compute LAI estimates through an internal segmentation algorithm [31]. PocketLAI estimates can reproduce destructive LAI measurements with acceptable results in terms of both reliability and accuracy [31].…”

Section: Field Campaignsmentioning

confidence: 99%

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Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

et al. 2017

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This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R 2 > 0.93) and good accuracies (RMSE < 0.83, rRMSE m < 23.6% and rRMSE r < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring.

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Section: Field Campaignsmentioning

confidence: 87%

Section: Field Campaignsmentioning

confidence: 99%

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

et al. 2017

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“…In turn, non-destructive methods are based on simplified models of light transmission into the canopy and have been applied for validating remote sensing high-resolution LAI estimates in many works [13][14][15]23,24]. Non-destructive methods include the use of classical instrumentation (i.e., plant canopy analyzers, ceptometers, and digital hemispherical photography (DHP)) [33,34] as well as the employment of new technologies (e.g., PocketLAI smartphone application [35]). Over rice crops, PocketLAI estimates align well with acquisitions obtained using other classical non-destructive instrumentation [25,36], although they present a slight underestimation which may reach up to 0.5 in dense rice canopies.…”

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confidence: 99%

A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

et al. 2018

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Leaf area index (LAI) is a key biophysical variable fundamental in natural vegetationand agricultural land monitoring and modelling studies. This paper is aimed at comparing, validating and discussing different LAI satellite products from operational services and customized solution based on innovative Earth Observation (EO) data such as Landsat-7/8 and Sentinel-2A. The comparison was performed to assess overall quality of LAI estimates for rice, as a fundamental input of different scale (regional to local) operational crop monitoring systems such as the ones developed during the "An Earth obseRvation Model based RicE information Service" (ERMES) project. We adopted a multiscale approach following international recognized protocols of the Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) guidelines in different steps: (1) acquisition of representative field sample measurements, (2) validation of decametric satellite product (10-30 m spatial resolution), and (3) exploitation of such data to assess quality of medium-resolution operational products (~1000 m). The study areas were located in the main European rice areas in Spain, Italy and Greece. Field campaigns were conducted during three entire rice seasons (2014, 2015 and 2016-from sowing to full-flowering) to acquire multi-temporal ground LAI measurements and to assess Landsat-7/8 LAI estimates. Results highlighted good correspondence between Landsat-7/8 LAI estimates and ground measurements revealing high correlations (R 2 ≥ 0.89) and low root mean squared errors (RMSE ≤ 0.75) in all seasons. Landsat-7/8 as well as Sentinel-2A high-resolution LAI retrievals, were compared with satellite LAI products operationally derived from MODIS (MOD15A2), Copernicus PROBA-V (GEOV1), and the recent EUMETSAT Polar System (EPS) LAI product. Good agreement was observed between high-and medium-resolution LAI estimates. In particular, the EPS LAI product was the most correlated product with both Landsat/7-8 and Sentinel-2A estimates, revealing R 2 ≥ 0.93 and RMSE ≤ 0.53 m 2 /m 2 . In addition, a comparison exercise of EPS, GEOV1 and MODIS revealed high correlations (R 2 ≥ 0.90) and RMSE ≤ 0.80 m 2 /m 2 in all cases and years. The temporal assessment shows that the three satellite products capture well the seasonality during the crop phenological cycle. Discrepancies are observed mainly in absolute values retrieved for the peak of rice season. This is the first study that provides a quantitative assessment on the quality of available operational LAI product for rice monitoring to both the scientific community and users of agro-monitoring operational services.

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“…As imaging methods, LAISmart [19] and PocketLAI [15] capture canopy photos using a smartphone camera sensor, then an automatic classification algorithm is used to separate image pixels into leaf and background to calculate canopy gap fraction. While calculating leaf area index from gap fraction, both methods share a common canopy light attenuation theory that describes how light transits from the top of the canopy to the sensors placed underneath.…”

Section: Estimation Of Lai Using a Smartphone Camera Sensormentioning

confidence: 99%

“…Both smartphone sensor methods first captured the canopy images, after which the gap fraction was retrieved from each image. An automated segmentation method was used to classify all the pixels in blue band into sky or vegetation pixels [15,34]. The gap fraction was calculated as the ratio of the number of sky pixels to the total number of pixels.…”

Section: Field Experimentsmentioning

confidence: 99%

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method

Qu¹,

Wang²,

Song³

et al. 2017

Forests

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Forest leaf area index (LAI) is a key characteristic affecting a field canopy microclimate. In addition to traditional professional measuring instruments, smartphone-based methods have been used to measure forest LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5 • angle. To further validate the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that the method with the smartphone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regards to four aspects that can affect LAI: gap fraction; leaf projection ratio; sensor field of view (FOV); and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5 • method to overestimate the gap fraction and hence underestimate conifer LAI. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

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Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods

Cited by 137 publications

References 30 publications

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method

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