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Studying the feasibility of assimilating sentinel-2 and planetscope imagery into the SAFY crop model to predict within-field wheat yield
Year:
2021
Source of publication :
remote sensing (source)
Authors :
בונפיל, דוד
;
.
מניוואסאגם, ו'
;
.
קפלן, גריגורי
;
.
רוזנשטיין, עופר
;
.
Volume :
Co-Authors:
V.S. Manivasagam

yuval Sadeh

Gregoriy Kaplan

David J. Bonfil

Offer Rozenstein

 

Facilitators :
From page:
0
To page:
0
(
Total pages:
1
)
Abstract:

Spatial information embedded in a crop model can improve yield prediction. Leaf area index (LAI) is a well-known crop variable often estimated from remote-sensing data and used as an input into crop models. In this study, we evaluated the assimilation of LAI derived from high-resolution (both spatial and temporal) satellite imagery into a mechanistic crop model, a simple algorithm for yield estimate (SAFY), to assess the within-field crop yield. We tested this approach on spring wheat grown in Israel. Empirical LAI models were derived from the biophysical processor for Sentinel-2 LAI and spectral vegetation indices from Sentinel-2 and PlanetScope images. The predicted grain yield obtained from the SAFY model was compared against the harvester’s yield map. LAI derived from PlanetScope and Sentinel-2 fused images achieved higher yield prediction (RMSE = 69 g/m2) accuracy than that of Sentinel-2 LAI (RMSE = 88 g/m2). Even though the spatial yield estimation was only moderately correlated to the ground truth (R2 = 0.45), this is consistent with current studies in this field, and the potential to capture within-field yield variations using high-resolution imagery has been demonstrated. Accordingly, this is the first application of PlanetScope and Sentinel-2 images conjointly used to obtain a high-density time series of LAI information to model within-field yield variability. 

Note:
Related Files :
PlanetScope
SAFY model
Sentinel-2
wheat
yield modeling
עוד תגיות
תוכן קשור
More details
DOI :
10.3390/rs13122395
Article number:
0
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
55583
Last updated date:
02/03/2022 17:27
Creation date:
13/07/2021 17:38
Scientific Publication
Studying the feasibility of assimilating sentinel-2 and planetscope imagery into the SAFY crop model to predict within-field wheat yield
V.S. Manivasagam

yuval Sadeh

Gregoriy Kaplan

David J. Bonfil

Offer Rozenstein

 

Studying the feasibility of assimilating sentinel-2 and planetscope imagery into the SAFY crop model to predict within-field wheat yield

Spatial information embedded in a crop model can improve yield prediction. Leaf area index (LAI) is a well-known crop variable often estimated from remote-sensing data and used as an input into crop models. In this study, we evaluated the assimilation of LAI derived from high-resolution (both spatial and temporal) satellite imagery into a mechanistic crop model, a simple algorithm for yield estimate (SAFY), to assess the within-field crop yield. We tested this approach on spring wheat grown in Israel. Empirical LAI models were derived from the biophysical processor for Sentinel-2 LAI and spectral vegetation indices from Sentinel-2 and PlanetScope images. The predicted grain yield obtained from the SAFY model was compared against the harvester’s yield map. LAI derived from PlanetScope and Sentinel-2 fused images achieved higher yield prediction (RMSE = 69 g/m2) accuracy than that of Sentinel-2 LAI (RMSE = 88 g/m2). Even though the spatial yield estimation was only moderately correlated to the ground truth (R2 = 0.45), this is consistent with current studies in this field, and the potential to capture within-field yield variations using high-resolution imagery has been demonstrated. Accordingly, this is the first application of PlanetScope and Sentinel-2 images conjointly used to obtain a high-density time series of LAI information to model within-field yield variability. 

Scientific Publication
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