LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands

I. Herrmann; A. Pimstein; A. Karnieli; Y. Cohen; V. Alchanatis; D. J. Bonfil

doi:10.1016/j.rse.2011.04.018

LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands

I. Herrmann, A. Pimstein, A. Karnieli^*, Y. Cohen, V. Alchanatis, D. J. Bonfil

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

222 Scopus citations

Abstract

Leaf Area Index (LAI) is an important variable that governs canopy processes and can be monitored by satellites. The current study aims at exploring the potential and limitations of using the red-edge spectral bands of the forthcoming superspectral satellites, namely-Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2, for assessing LAI in field crops. The research was conducted in experimental plots of wheat and potato in the northwestern Negev, Israel. Continuous spectral data were collected by a field spectrometer and LAI data were obtained by a ceptometer. The spectral data were resampled to the superspectral VENμS and Sentinel-2 resolutions. The data were divided into seven datasets (four seasons, two crops, and one including all data). The LAI prediction abilities by Partial Least Squares (PLS) models for continuous spectra and the resampled spectra were compared and evaluated. For wheat and potato of the continuous, VENμS, and Sentinel-2 data formations, the PLS correlation coefficients (r) values were 0.93, 0.93, and 0.92, respectively. In most cases, the red-edge region was found to be the most important spectral region for the three data formations, according to the Variable Importance in Projection (VIP) analysis. Additionally, Normalized Difference Vegetation Index (NDVI) and the Red-Edge Inflection Point (REIP) were computed for the three data formations in order to observe relation to as well as prediction accuracy in retrieving LAI values. The prediction abilities of the calculated indices by the data formations were compared, peaking for wheat, with r values of 0.91 for the REIP for the three data formations. Therefore, it is concluded that VENμS and Sentinel-2 can spectrally assess LAI as good as a hyperspectral sensor. The REIP was found to be a significantly better predictor than NDVI for wheat data and therefore can potentially be implemented for future LAI monitoring applications by superspectral sensors that contain four red-edge bands.

Original language	English
Pages (from-to)	2141-2151
Number of pages	11
Journal	Remote Sensing of Environment
Volume	115
Issue number	8
DOIs	https://doi.org/10.1016/j.rse.2011.04.018
State	Published - 15 Aug 2011
Externally published	Yes

Bibliographical note

Funding Information:
This research was supported in part by Research Grant Award CA-9102-06 from BARD-AAFC , The United States–Israel Binational Agricultural Research and Development Fund and Agriculture and Agri-Food, Canada ; the Chief Scientist of the Israeli Ministry of Agriculture , and the Israeli Ministry of Science, Culture, and Sport . The authors would like to thank Alexander Goldberg for his indefatigable work in the field and in the lab and to express their appreciation for the vital contributions of Gadi Hadar and Ran Ferdman, the potato growers from Kibbutz Ruhama.

Keywords

Agriculture
LAI
NDVI
PLS
Red-edge
Sentinel-2
VENμS
VIP

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.rse.2011.04.018

Cite this

@article{b56d3d4c0ea544b78d0ac7a7508a5c7e,

title = "LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands",

abstract = "Leaf Area Index (LAI) is an important variable that governs canopy processes and can be monitored by satellites. The current study aims at exploring the potential and limitations of using the red-edge spectral bands of the forthcoming superspectral satellites, namely-Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2, for assessing LAI in field crops. The research was conducted in experimental plots of wheat and potato in the northwestern Negev, Israel. Continuous spectral data were collected by a field spectrometer and LAI data were obtained by a ceptometer. The spectral data were resampled to the superspectral VENμS and Sentinel-2 resolutions. The data were divided into seven datasets (four seasons, two crops, and one including all data). The LAI prediction abilities by Partial Least Squares (PLS) models for continuous spectra and the resampled spectra were compared and evaluated. For wheat and potato of the continuous, VENμS, and Sentinel-2 data formations, the PLS correlation coefficients (r) values were 0.93, 0.93, and 0.92, respectively. In most cases, the red-edge region was found to be the most important spectral region for the three data formations, according to the Variable Importance in Projection (VIP) analysis. Additionally, Normalized Difference Vegetation Index (NDVI) and the Red-Edge Inflection Point (REIP) were computed for the three data formations in order to observe relation to as well as prediction accuracy in retrieving LAI values. The prediction abilities of the calculated indices by the data formations were compared, peaking for wheat, with r values of 0.91 for the REIP for the three data formations. Therefore, it is concluded that VENμS and Sentinel-2 can spectrally assess LAI as good as a hyperspectral sensor. The REIP was found to be a significantly better predictor than NDVI for wheat data and therefore can potentially be implemented for future LAI monitoring applications by superspectral sensors that contain four red-edge bands.",

keywords = "Agriculture, LAI, NDVI, PLS, Red-edge, Sentinel-2, VENμS, VIP",

author = "I. Herrmann and A. Pimstein and A. Karnieli and Y. Cohen and V. Alchanatis and Bonfil, {D. J.}",

note = "Funding Information: This research was supported in part by Research Grant Award CA-9102-06 from BARD-AAFC , The United States–Israel Binational Agricultural Research and Development Fund and Agriculture and Agri-Food, Canada ; the Chief Scientist of the Israeli Ministry of Agriculture , and the Israeli Ministry of Science, Culture, and Sport . The authors would like to thank Alexander Goldberg for his indefatigable work in the field and in the lab and to express their appreciation for the vital contributions of Gadi Hadar and Ran Ferdman, the potato growers from Kibbutz Ruhama.",

year = "2011",

month = aug,

day = "15",

doi = "10.1016/j.rse.2011.04.018",

language = "אנגלית",

volume = "115",

pages = "2141--2151",

journal = "Remote Sensing of Environment",

issn = "0034-4257",

publisher = "Elsevier Inc.",

number = "8",

}

TY - JOUR

T1 - LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands

AU - Herrmann, I.

AU - Pimstein, A.

AU - Karnieli, A.

AU - Cohen, Y.

AU - Alchanatis, V.

AU - Bonfil, D. J.

N1 - Funding Information: This research was supported in part by Research Grant Award CA-9102-06 from BARD-AAFC , The United States–Israel Binational Agricultural Research and Development Fund and Agriculture and Agri-Food, Canada ; the Chief Scientist of the Israeli Ministry of Agriculture , and the Israeli Ministry of Science, Culture, and Sport . The authors would like to thank Alexander Goldberg for his indefatigable work in the field and in the lab and to express their appreciation for the vital contributions of Gadi Hadar and Ran Ferdman, the potato growers from Kibbutz Ruhama.

PY - 2011/8/15

Y1 - 2011/8/15

N2 - Leaf Area Index (LAI) is an important variable that governs canopy processes and can be monitored by satellites. The current study aims at exploring the potential and limitations of using the red-edge spectral bands of the forthcoming superspectral satellites, namely-Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2, for assessing LAI in field crops. The research was conducted in experimental plots of wheat and potato in the northwestern Negev, Israel. Continuous spectral data were collected by a field spectrometer and LAI data were obtained by a ceptometer. The spectral data were resampled to the superspectral VENμS and Sentinel-2 resolutions. The data were divided into seven datasets (four seasons, two crops, and one including all data). The LAI prediction abilities by Partial Least Squares (PLS) models for continuous spectra and the resampled spectra were compared and evaluated. For wheat and potato of the continuous, VENμS, and Sentinel-2 data formations, the PLS correlation coefficients (r) values were 0.93, 0.93, and 0.92, respectively. In most cases, the red-edge region was found to be the most important spectral region for the three data formations, according to the Variable Importance in Projection (VIP) analysis. Additionally, Normalized Difference Vegetation Index (NDVI) and the Red-Edge Inflection Point (REIP) were computed for the three data formations in order to observe relation to as well as prediction accuracy in retrieving LAI values. The prediction abilities of the calculated indices by the data formations were compared, peaking for wheat, with r values of 0.91 for the REIP for the three data formations. Therefore, it is concluded that VENμS and Sentinel-2 can spectrally assess LAI as good as a hyperspectral sensor. The REIP was found to be a significantly better predictor than NDVI for wheat data and therefore can potentially be implemented for future LAI monitoring applications by superspectral sensors that contain four red-edge bands.

AB - Leaf Area Index (LAI) is an important variable that governs canopy processes and can be monitored by satellites. The current study aims at exploring the potential and limitations of using the red-edge spectral bands of the forthcoming superspectral satellites, namely-Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2, for assessing LAI in field crops. The research was conducted in experimental plots of wheat and potato in the northwestern Negev, Israel. Continuous spectral data were collected by a field spectrometer and LAI data were obtained by a ceptometer. The spectral data were resampled to the superspectral VENμS and Sentinel-2 resolutions. The data were divided into seven datasets (four seasons, two crops, and one including all data). The LAI prediction abilities by Partial Least Squares (PLS) models for continuous spectra and the resampled spectra were compared and evaluated. For wheat and potato of the continuous, VENμS, and Sentinel-2 data formations, the PLS correlation coefficients (r) values were 0.93, 0.93, and 0.92, respectively. In most cases, the red-edge region was found to be the most important spectral region for the three data formations, according to the Variable Importance in Projection (VIP) analysis. Additionally, Normalized Difference Vegetation Index (NDVI) and the Red-Edge Inflection Point (REIP) were computed for the three data formations in order to observe relation to as well as prediction accuracy in retrieving LAI values. The prediction abilities of the calculated indices by the data formations were compared, peaking for wheat, with r values of 0.91 for the REIP for the three data formations. Therefore, it is concluded that VENμS and Sentinel-2 can spectrally assess LAI as good as a hyperspectral sensor. The REIP was found to be a significantly better predictor than NDVI for wheat data and therefore can potentially be implemented for future LAI monitoring applications by superspectral sensors that contain four red-edge bands.

KW - Agriculture

KW - LAI

KW - NDVI

KW - PLS

KW - Red-edge

KW - Sentinel-2

KW - VENμS

KW - VIP

UR - http://www.scopus.com/inward/record.url?scp=79957590267&partnerID=8YFLogxK

U2 - 10.1016/j.rse.2011.04.018

DO - 10.1016/j.rse.2011.04.018

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???

AN - SCOPUS:79957590267

SN - 0034-4257

VL - 115

SP - 2141

EP - 2151

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

IS - 8

ER -

LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this