Estimation of crop angle of inclination for lodged wheat using multi-sensor SAR data

S. Chauhan (Corresponding Author), R. Darvishzadeh, Mirco Boschetti, A.D. Nelson

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Abstract

Lodging - the bending of crop stems - reduces the quantity and quality of cereal crop yields. Early quantification of crop lodging is important to prevent further losses and to facilitate harvesting operations. Crop angle of inclination (CAI) is a quantitative measure of the lodging stage and a component of lodging severity/score. CAI is an important structural parameter for lodged crops and very few studies have investigated its estimation using satellite-based remote sensing. In this study, the performance of Sentinel-1 and multi-incidence angle (FQ8-27° and FQ21-41°) RADARSAT-2 data were investigated for estimating CAI. Temporal crop biophysical/structural parameters (CAI and crop height) and meteorological data (rainfall and wind speed) were collected throughout May 1-June 30, 2018 in a very large commercial farm located in Jolanda di Savoia, Ferrara, Italy. Field data were grouped into different crop lodging stages (non-lodged/healthy (H), moderate lodging (ML), severe lodging (SL) and very severe lodging (VSL)) based on CAI. Quantitative relationships were established between field-measured CAI values and the RS-derived metrics for Sentinel-1 and RADARSAT-2 timeseries using support vector regression (SVR) models. The RADARSAT-2 FQ8 model performed most robustly with a R2CV (cross-validated R2) of 0.87 and a RMSECV (cross-validated RMSE) of 8.89° while the performance of the Sentinel-1 and RADARSAT-2 FQ21 models were comparable with an RMSECV of 11.35° and 11.63° respectively. Low incidence angle data were particularly sensitive to high CAI values (VSL) while high incidence angle data were useful for predicting lower CAI (ML and SL). While the RADARSAT-2 FQ-8 model outperformed the other two, the Sentinel-1 model still explained 78% of the CAI variability in the study site, which is important in the context of operational crop lodging stage assessment. This is the first study to demonstrate the utility of SAR remote sensing data for estimating CAI as a measure of the lodging stage and a component of lodging severity.

Original languageEnglish
Article number111488
Pages (from-to)1-14
Number of pages14
JournalRemote sensing of environment
Volume236
DOIs
Publication statusPublished - 1 Jan 2020

Fingerprint

synthetic aperture radar
lodging
Crops
sensors (equipment)
wheat
sensor
crop
Sensors
crops
RADARSAT
angle of incidence
remote sensing
Remote sensing
large farms
commercial farms

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-HYBRID
  • UT-Hybrid-D

Cite this

@article{49157c1da564442a8c3491d36f1a3f79,
title = "Estimation of crop angle of inclination for lodged wheat using multi-sensor SAR data",
abstract = "Lodging - the bending of crop stems - reduces the quantity and quality of cereal crop yields. Early quantification of crop lodging is important to prevent further losses and to facilitate harvesting operations. Crop angle of inclination (CAI) is a quantitative measure of the lodging stage and a component of lodging severity/score. CAI is an important structural parameter for lodged crops and very few studies have investigated its estimation using satellite-based remote sensing. In this study, the performance of Sentinel-1 and multi-incidence angle (FQ8-27° and FQ21-41°) RADARSAT-2 data were investigated for estimating CAI. Temporal crop biophysical/structural parameters (CAI and crop height) and meteorological data (rainfall and wind speed) were collected throughout May 1-June 30, 2018 in a very large commercial farm located in Jolanda di Savoia, Ferrara, Italy. Field data were grouped into different crop lodging stages (non-lodged/healthy (H), moderate lodging (ML), severe lodging (SL) and very severe lodging (VSL)) based on CAI. Quantitative relationships were established between field-measured CAI values and the RS-derived metrics for Sentinel-1 and RADARSAT-2 timeseries using support vector regression (SVR) models. The RADARSAT-2 FQ8 model performed most robustly with a R2CV (cross-validated R2) of 0.87 and a RMSECV (cross-validated RMSE) of 8.89° while the performance of the Sentinel-1 and RADARSAT-2 FQ21 models were comparable with an RMSECV of 11.35° and 11.63° respectively. Low incidence angle data were particularly sensitive to high CAI values (VSL) while high incidence angle data were useful for predicting lower CAI (ML and SL). While the RADARSAT-2 FQ-8 model outperformed the other two, the Sentinel-1 model still explained 78{\%} of the CAI variability in the study site, which is important in the context of operational crop lodging stage assessment. This is the first study to demonstrate the utility of SAR remote sensing data for estimating CAI as a measure of the lodging stage and a component of lodging severity.",
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Estimation of crop angle of inclination for lodged wheat using multi-sensor SAR data. / Chauhan, S. (Corresponding Author); Darvishzadeh, R.; Boschetti, Mirco; Nelson, A.D.

In: Remote sensing of environment, Vol. 236, 111488, 01.01.2020, p. 1-14.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Estimation of crop angle of inclination for lodged wheat using multi-sensor SAR data

AU - Chauhan, S.

AU - Darvishzadeh, R.

AU - Boschetti, Mirco

AU - Nelson, A.D.

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Lodging - the bending of crop stems - reduces the quantity and quality of cereal crop yields. Early quantification of crop lodging is important to prevent further losses and to facilitate harvesting operations. Crop angle of inclination (CAI) is a quantitative measure of the lodging stage and a component of lodging severity/score. CAI is an important structural parameter for lodged crops and very few studies have investigated its estimation using satellite-based remote sensing. In this study, the performance of Sentinel-1 and multi-incidence angle (FQ8-27° and FQ21-41°) RADARSAT-2 data were investigated for estimating CAI. Temporal crop biophysical/structural parameters (CAI and crop height) and meteorological data (rainfall and wind speed) were collected throughout May 1-June 30, 2018 in a very large commercial farm located in Jolanda di Savoia, Ferrara, Italy. Field data were grouped into different crop lodging stages (non-lodged/healthy (H), moderate lodging (ML), severe lodging (SL) and very severe lodging (VSL)) based on CAI. Quantitative relationships were established between field-measured CAI values and the RS-derived metrics for Sentinel-1 and RADARSAT-2 timeseries using support vector regression (SVR) models. The RADARSAT-2 FQ8 model performed most robustly with a R2CV (cross-validated R2) of 0.87 and a RMSECV (cross-validated RMSE) of 8.89° while the performance of the Sentinel-1 and RADARSAT-2 FQ21 models were comparable with an RMSECV of 11.35° and 11.63° respectively. Low incidence angle data were particularly sensitive to high CAI values (VSL) while high incidence angle data were useful for predicting lower CAI (ML and SL). While the RADARSAT-2 FQ-8 model outperformed the other two, the Sentinel-1 model still explained 78% of the CAI variability in the study site, which is important in the context of operational crop lodging stage assessment. This is the first study to demonstrate the utility of SAR remote sensing data for estimating CAI as a measure of the lodging stage and a component of lodging severity.

AB - Lodging - the bending of crop stems - reduces the quantity and quality of cereal crop yields. Early quantification of crop lodging is important to prevent further losses and to facilitate harvesting operations. Crop angle of inclination (CAI) is a quantitative measure of the lodging stage and a component of lodging severity/score. CAI is an important structural parameter for lodged crops and very few studies have investigated its estimation using satellite-based remote sensing. In this study, the performance of Sentinel-1 and multi-incidence angle (FQ8-27° and FQ21-41°) RADARSAT-2 data were investigated for estimating CAI. Temporal crop biophysical/structural parameters (CAI and crop height) and meteorological data (rainfall and wind speed) were collected throughout May 1-June 30, 2018 in a very large commercial farm located in Jolanda di Savoia, Ferrara, Italy. Field data were grouped into different crop lodging stages (non-lodged/healthy (H), moderate lodging (ML), severe lodging (SL) and very severe lodging (VSL)) based on CAI. Quantitative relationships were established between field-measured CAI values and the RS-derived metrics for Sentinel-1 and RADARSAT-2 timeseries using support vector regression (SVR) models. The RADARSAT-2 FQ8 model performed most robustly with a R2CV (cross-validated R2) of 0.87 and a RMSECV (cross-validated RMSE) of 8.89° while the performance of the Sentinel-1 and RADARSAT-2 FQ21 models were comparable with an RMSECV of 11.35° and 11.63° respectively. Low incidence angle data were particularly sensitive to high CAI values (VSL) while high incidence angle data were useful for predicting lower CAI (ML and SL). While the RADARSAT-2 FQ-8 model outperformed the other two, the Sentinel-1 model still explained 78% of the CAI variability in the study site, which is important in the context of operational crop lodging stage assessment. This is the first study to demonstrate the utility of SAR remote sensing data for estimating CAI as a measure of the lodging stage and a component of lodging severity.

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