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• Meroni, M
• Marinho, E
• Sghaier, N
• Verstrate, MM.
• Leo, O
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• Meroni, M
• Marinho, E
• Sghaier, N
• Verstrate, MM.
• Leo, O
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• Meroni, M
• Marinho, E
• Sghaier, N
• Verstrate, MM.
• Leo, O

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Remote Sens. 2013, 5(2), 539-557; doi:10.3390/rs5020539

Article

Remote Sensing Based Yield Estimation in a Stochastic Framework — Case Study of Durum Wheat in Tunisia

Michele Meroni ^1,†,* , Eduardo Marinho ^1,†,* , Nabil Sghaier ² , Michel M. Verstrate ¹  and Olivier Leo ¹

¹ Institute for Environment and Sustainability, Joint Research Centre, European Commission, V. Fermi 2749, I-21027 Ispra (VA), Italy ² National Centre for Cartography and Remote Sensing (CNCT), BP. 200, Tunis Cedex, Tunisia ^† These authors contributed equally to this work.

* Authors to whom correspondence should be addressed.

Received: 3 December 2012; in revised form: 15 January 2013 / Accepted: 16 January 2013 / Published: 28 January 2013

(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)

Download PDF Full-Text [1233 KB, uploaded 28 January 2013 09:19 CET]

Abstract: Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in a framework constrained by information availability, remote sensing data to yield conversion parameters are to be estimated. Statistical models are suitable for this purpose, given their ability to deal with statistical errors. This paper explores the performance in yield estimation of various remote sensing indicators based on varying degrees of bio-physical insight, in interaction with statistical methods (linear regressions) that rely on different hypotheses. Performances in estimating the temporal and spatial variability of yield, and implications of data scarcity in both dimensions are investigated. Jackknifed results (leave one year out) are presented for the case of wheat yield regional estimation in Tunisia using the SPOT-VEGETATION instrument. Best performances, up to 0.8 of R², are achieved using the most physiologically sound remote sensing indicator, in conjunction with statistical specifications allowing for parsimonious spatial adjustment of the parameters.

Keywords: optical remote sensing; multitemporal observations; yield; statistical models; SPOT-VGT

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