Comparison of leaf area index estimates by ceptometer and PocketLAI smart app in canopies with different structures

Francone, Caterina; Pagani, Valentina; Foi, Marco; Cappelli, G.; Confalonieri, Roberto

doi:10.1016/j.fcr.2013.09.024

Cited by 61 publications

(42 citation statements)

References 19 publications

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“…Since these projects were recently designed, it is too 392 early to evaluate the success of these initiatives in terms of data collection and farmers' satisfaction. (Francone et al, 2014), which aims at measuring the LAI in the field, 413 was not so far coupled with a crowdsourcing system for gathering crop development information. 414 Actually, although crop and animal production (meat or dairy production) is often well-known by the 415 farmers, this information is confidential and is rarely shared.…”

Section: Weeds Pests and Diseasesmentioning

confidence: 99%

Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach

Minet

Curnel

Gobin

et al. 2017

Computers and Electronics in Agriculture

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Section: Weeds Pests and Diseasesmentioning

confidence: 99%

Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach

Minet

Curnel

Gobin

et al. 2017

Computers and Electronics in Agriculture

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“…This property comes from the projection function used to compute gap fraction corresponding to the projection of a PAI unit into a given direction. This mobile application has been shown to perform well in canopies with different structures [25] and has proven its reliability in terms of both trueness and precision [24]. PocketLAI computes the gap fraction using the smartphone's accelerometer and camera.…”

Section: Pocketlaimentioning

confidence: 99%

“…PocketLAI was already successfully tested against Decagon AccuPAR Ceptometer, and it was used to measure LAI both on rice and other crop types also deviating from ideal assumptions of the light transmittance model used [25]. Smartphones are becoming an accessible daily instrument for most of the population.…”

Section: Introductionmentioning

confidence: 99%

Multitemporal Monitoring of Plant Area Index in the Valencia Rice District with PocketLAI

et al. 2016

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Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. Frequently, plant canopy analyzers (LAI-2000) and digital cameras for hemispherical photography (DHP) are used for indirect effective plant area index (PAI e f f ) estimates. Nevertheless, these instruments are expensive and have the disadvantages of low portability and maintenance. Recently, a smartphone app called PocketLAI was presented and tested for acquiring PAI e f f measurements. It was used during an entire rice season for indirect PAI e f f estimations and for deriving reference high-resolution PAI e f f maps. Ground PAI e f f values acquired with PocketLAI, LAI-2000, and DHP were well correlated (R 2 = 0.95, RMSE = 0.21 m 2 /m 2 for Licor-2000, and R 2 = 0.94, RMSE = 0.6 m 2 /m 2 for DHP). Complementary data such as phenology and leaf chlorophyll content were acquired to complement seasonal rice plant information provided by PAI e f f . High-resolution PAI e f f maps, which can be used for the validation of remote sensing products, have been derived using a global transfer function (TF) made of several measuring dates and their associated satellite radiances.

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“…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. Francone et al [37] found an underestimation of PocketLAI with regard to AccuPAR ceptometer for LAI values higher than 5 m 2 /m 2 and RMSE of 0.41, 0.49 and 0.96 m 2 /m 2 for grassland, maize and giant reed, respectively. Over broadleaved crops (maize, soybean, and sorghum), a significant underestimation of PocketLAI with regard to LAI-2200 and saturation at about effective Plant Area Index (PAI eff ) of 3.5 m 2 /m 2 were reported [38].…”

mentioning

confidence: 96%

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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Comparison of leaf area index estimates by ceptometer and PocketLAI smart app in canopies with different structures

Cited by 61 publications

References 19 publications

Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach

Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach

Multitemporal Monitoring of Plant Area Index in the Valencia Rice District with PocketLAI

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

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