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,Offer Rozenstein,David J Bonfil,Gregoriy Kaplan

doi:10.3390/rs13122395

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.

Highlights

A priori knowledge of crop yield is useful for stakeholders along the value chain and policymakers in their decision-making process [1]
Out best estimate showed the strongest correlation with Sentinel-2 of cloud-free (S2)-Leaf area index (LAI) (R22 = 0.88, root mean square errorerror (RMSE) = 0.98), followed by Renormalized difference vegetation index (RDVI)
Since Modified soil-adjusted vegetation index (MSAVI) showed better prediction performance, subsequent results are based on MSAVI-based LAI

Summary

Introduction

A priori knowledge of crop yield is useful for stakeholders along the value chain and policymakers in their decision-making process [1]. Studies on crop yield prediction used climate variables (e.g., precipitation, air temperature, solar radiation) to develop empirical models using historical crop yield data. These empirical relationships between crop yield and climate variables vary from region to region. The technological advancement in sensors, computational tools, and remote sensing science provide a vast volume of digital data to monitor and assess the spatial variability of crop yield [4]. Earth observation satellites monitor land cover changes at short intervals [5]

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