Potential of hyperspectral remote sensing to estimate the yield of a Crambe abyssinica Hochst crop

Viana, Octavio Henrique; Mercante, Erivelto; Andrade, Maurício Guy de; Felipetto, Henrique; Cattani, Carlos Eduardo Vizzotto; Bombarda, Filipe Fontes; Boas, Márcio Antônio Vilas

doi:10.1117/1.jrs.12.016023

Cited by 4 publications

(7 citation statements)

References 23 publications

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“…The active sensor GreenSeeker 505 Handheld (Trimble Agriculture, Westminster, CO, USA) and the passive hyperspectral sensor FieldSpec4 Standard-Res model (ASD, Boulder, CO, USA) were used in the study. Few studies used these proximal sensors at the farm level in this study region (Cattani et al, 2017;Prudente et al, 2019;Viana et al, 2018), but they did not compare the NDVI values from different sensors. GreenSeeker 505 Handheld register the reflectance on two regions of the electromagnetic spectrum, red wavelength (RED GScentered at 0.66 µm ±0.0125 µm) and near-infrared (NIR) wavelength (NIR GScentered at 0.78 µm ±0.0125), both with 0.025 µm of spectral resolution.…”

Section: Terrestrial Sensorsmentioning

confidence: 99%

“…For the GreenSeeker 505 Handheld, it was walked approximately 10 m, with a speed average of 0.5 m.s -1 , recording 10 samples per second, totalizing between 150 to 200 samples for each point (Cattani et al, 2017;Sharma, Bu, Denton, & Franzen, 2015;Zheng et al, 2016). Considering the FieldSpec4, three samples were collected for each replicate in the time (one-second peer sample), totalizing nine samples at each point (Prudente et al, 2019;Viana et al, 2018Viana et al, , 2017. As FieldSpec4 is a passive sensor, it was used a perfectly diffuse surface for the calibration process every 10 minutes (ASD, 2015;Viana et al, 2018).…”

Section: Terrestrial Sensorsmentioning

confidence: 99%

“…Considering the FieldSpec4, three samples were collected for each replicate in the time (one-second peer sample), totalizing nine samples at each point (Prudente et al, 2019;Viana et al, 2018Viana et al, , 2017. As FieldSpec4 is a passive sensor, it was used a perfectly diffuse surface for the calibration process every 10 minutes (ASD, 2015;Viana et al, 2018).…”

Section: Terrestrial Sensorsmentioning

confidence: 99%

“…Proximal (or terrestrial) sensors suffering less atmospheric interference than aerial and orbital sensors (Eberhardt et al, 2016;Prudente et al, 2020;Whitcraft, Vermote, Becker-Reshef, & Justice, 2015) and have temporal resolution flexibility (Mulla, 2013). Some studies characterized biophysical crop proprieties using proximal VI from active and passive sensors (Anderson et al, 2016;Cattani et al, 2017;Congalton, Gu, Yadav, Thenkabail, & Ozdogan, 2014;Prudente et al, 2019;Viana et al, 2018;Viana, Mercante, Felipetto, Kusminski, & Bleil Jr, 2017;Yao et al, 2013). Passive sensors depend on an external electromagnetic source, as the sun (Janse & Deshmukh, 2017), and can be grouped as multispectral or hyperspectral (hundreds of bands).…”

Section: Introductionmentioning

confidence: 99%

“…With spectroradiometers (hyperspectral sensors) it is possible to obtain the spectral signature and various VIs (Congalton et al, 2014). FieldSpec4 Standard-Res (ASD, Boulder, CO, USA) is one of the most common hyperspectral sensors used in agricultural research (Cattani et al, 2017;Prudente et al, 2019;Viana et al, 2018Viana et al, , 2017. On the other hand, active sensors emit their energy source and they are less affected by weather conditions (Yao et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Comparison Between Vegetation Index Obtained by Active and Passive Proximal Sensors

Prudente¹,

Mercante²,

Johann³

et al. 2022

JAS

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Proximal sensors have been used to characterize the crop biophysical properties by reflectance values and/or using Vegetation Indices (IV). Our goal with this work is to compare NDVI (Normalized Difference Vegetation Index) spectra-temporal profiles obtained by active (GreenSeeker 505 Handheld) and passive (FieldSpec4 model Standard-Res) proximal sensors to monitor soybeans and beans. It was monitored agricultural fields with soybeans Nidera 5909RG variety and beans Imperador variety, located in the municipality of Cascavel, Parana state, Brazil. The proximal sensors were used to monitor the crop's conditions on different Days After Sowing (DAS). NDVI from FieldSpec4 (NDVI FS) showed a higher correlation with GreenSeeker NDVI (NDVI GS) in the wavelengths of 649 nm and 771 nm for soybeans (rs = 0.9105) and 646 nm and 792 nm for beans (rs = 0.9382). The inter-calibration of NDVI GS values in function of NDVI FS, considering the entire phenological cycle, resulted in RMSE = 0.0520 and dr = 0.8630 for soybeans and RMSE = 0.0636 and dr = 0.8890 for beans. NDVI values showed saturation during the major vegetative development of the crops, interfering in the inter-calibration process. In general, the NDVI GS and NDVI FS were similar in terms of their spectral-temporal pattern. According to our results, the active sensor could be used to crop monitoring, resulting in a lower cost and less climatic interference.

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Section: Terrestrial Sensorsmentioning

confidence: 99%

Section: Terrestrial Sensorsmentioning

confidence: 99%

Section: Terrestrial Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparison Between Vegetation Index Obtained by Active and Passive Proximal Sensors

Prudente¹,

Mercante²,

Johann³

et al. 2022

JAS

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Crambe seed oil: Extraction and reaction with dimethyl carbonate under pressurized conditions

Trentini

Mello

Cabral

et al. 2020

The Journal of Supercritical Fluids

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A critical review on applications of hyperspectral remote sensing in crop monitoring

Kong

Hou

et al. 2022

Ex. Agric.

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Summary Numerous technologies have contributed to the recent development of agriculture, especially the advancement in hyperspectral remote sensing (HRS) constituted a revolution in crop monitoring. The widespread use of HRS to obtain crop parameters suggests the need for a review of research advances in this area. HRS offers new theories and methods for studying crop parameters, but much work needs to be done both experimentally and theoretically before we can truly understand the physical and chemical processes that predict these crop parameters. The study focuses on the following elements: 1) The article provides a relatively comprehensive introduction to HRS and how it can be applied to crop monitoring; 2) Current state-of-the-art techniques are summarized and analyzed to inform further advances in crop monitoring; 3) Opportunities and challenges for crop monitoring applications using HRS are discussed, and future research is summarized. Finally, through a comprehensive discussion and analysis, the article proposes new directions for using HRS to study crop characteristics, such as new data mining techniques including deep learning provide opportunities for efficient processing of large amounts of HRS data; combining the temporal and dynamic characteristics of crop parameters and vegetation growth processes will greatly improve the accuracy of crop parameter detection and monitoring; multidata fusion and multiscale data assimilation will become HRS monitoring. Multidata fusion and multiscale data assimilation will become another research hotspot for HRS monitoring of crop parameters.

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Potential of hyperspectral remote sensing to estimate the yield of a Crambe abyssinica Hochst crop

Cited by 4 publications

References 23 publications

Comparison Between Vegetation Index Obtained by Active and Passive Proximal Sensors

Comparison Between Vegetation Index Obtained by Active and Passive Proximal Sensors

Crambe seed oil: Extraction and reaction with dimethyl carbonate under pressurized conditions

A critical review on applications of hyperspectral remote sensing in crop monitoring

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