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Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery
Year:
2021
Source of publication :
remote sensing (source)
Authors :
Kaplan, Gregoriy
;
.
Lukyanov, Victor
;
.
Manivasagam, V. S.
;
.
Rozenstein, Offer
;
.
Tanny, Josef
;
.
Volume :
13
Co-Authors:

 Gregoriy Kaplan

Lior Fine

Victor Lukyanov

V. S. Manivasagam

Nitzan Malachy

Josef Tanny

 Offer Rozenstein

 

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Total pages:
1
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Abstract:

Crop monitoring throughout the growing season is key for optimized agricultural production. Satellite remote sensing is a useful tool for estimating crop variables, yet continuous high spatial resolution earth observations are often interrupted by clouds. This paper demonstrates overcoming this limitation by combining observations from two public-domain spaceborne optical sensors. Ground measurements were conducted in the Hula Valley, Israel, over four growing seasons to monitor the development of processing tomato. These measurements included continuous water consumption measurements using an eddy-covariance tower from which the crop coefficient (Kc) was calculated and measurements of Leaf Area Index (LAI) and crop height. Satellite imagery acquired by Sentinel-2 and VENµS was used to derive vegetation indices and model Kc, LAI, and crop height. The conjoint use of Sentinel-2 and VENµS imagery facilitated accurate estimation of Kc (R2 = 0.82, RMSE = 0.09), LAI (R2 = 0.79, RMSE = 1.2), and crop height (R2 = 0.81, RMSE = 7 cm). Additionally, our empirical models for LAI estimation were found to perform better than the SNAP biophysical processor (R2 = 0.53, RMSE = 2.3). Accordingly, Sentinel-2 and VENµS imagery was demonstrated to be a viable tool for agricultural monitoring. 

Note:
Related Files :
Crop coefficient
eddy covariance
LAI
LAI (leaf area index)
Sentinel-2
Vegetation indices
VENµS
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Related Content
More details
DOI :
10.3390/rs13061046
Article number:
0
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
54427
Last updated date:
02/03/2022 17:27
Creation date:
06/04/2021 16:20
Scientific Publication
Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery
13

 Gregoriy Kaplan

Lior Fine

Victor Lukyanov

V. S. Manivasagam

Nitzan Malachy

Josef Tanny

 Offer Rozenstein

 

Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery

Crop monitoring throughout the growing season is key for optimized agricultural production. Satellite remote sensing is a useful tool for estimating crop variables, yet continuous high spatial resolution earth observations are often interrupted by clouds. This paper demonstrates overcoming this limitation by combining observations from two public-domain spaceborne optical sensors. Ground measurements were conducted in the Hula Valley, Israel, over four growing seasons to monitor the development of processing tomato. These measurements included continuous water consumption measurements using an eddy-covariance tower from which the crop coefficient (Kc) was calculated and measurements of Leaf Area Index (LAI) and crop height. Satellite imagery acquired by Sentinel-2 and VENµS was used to derive vegetation indices and model Kc, LAI, and crop height. The conjoint use of Sentinel-2 and VENµS imagery facilitated accurate estimation of Kc (R2 = 0.82, RMSE = 0.09), LAI (R2 = 0.79, RMSE = 1.2), and crop height (R2 = 0.81, RMSE = 7 cm). Additionally, our empirical models for LAI estimation were found to perform better than the SNAP biophysical processor (R2 = 0.53, RMSE = 2.3). Accordingly, Sentinel-2 and VENµS imagery was demonstrated to be a viable tool for agricultural monitoring. 

Scientific Publication
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