Bootstrap Assessment of Crop Area Estimates Using Satellite Pixels Counting

Cristiano Ferraz, Jacques Delincé, André Leite and Raydonal Ospina
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Cristiano Ferraz: Computational Agricultural Statistics Laboratory—CASTLab, Federal University of Pernambuco, Recife 50740-540, Brazil
Jacques Delincé: Independent Researcher, 1330 Rixensart, Belgium
André Leite: Computational Agricultural Statistics Laboratory—CASTLab, Federal University of Pernambuco, Recife 50740-540, Brazil
Raydonal Ospina: Computational Agricultural Statistics Laboratory—CASTLab, Federal University of Pernambuco, Recife 50740-540, Brazil

Stats, 2022, vol. 5, issue 2, 1-18

Abstract: Crop area estimates based on counting pixels over classified satellite images are a promising application of remote sensing to agriculture. However, such area estimates are biased, and their variance is a function of the error rates of the classification rule. To redress the bias, estimators (direct and inverse) relying on the so-called confusion matrix have been proposed, but analytic estimators for variances can be tricky to derive. This article proposes a bootstrap method for assessing statistical properties of such estimators based on information from a sample confusion matrix. The proposed method can be applied to any other type of estimator that is built upon confusion matrix information. The resampling procedure is illustrated in a small study to assess the biases and variances of estimates using purely pixel counting and estimates provided by both direct and inverse estimators. The method has the advantage of being simple to implement even when the sample confusion matrix is generated under unequal probability sample design. The results show the limitations of estimates based solely on pixel counting as well as respective advantages and drawbacks of the direct and inverse estimators with respect to their feasibility, unbiasedness, and variance.

Keywords: error matrix; image classification; confusion matrix; resampling; inverse calibration (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
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