The use of landsat for county estimates of crop areas: evaluation of the huddleston-ray and the battese-fuller estimators for &amp; the case of stratified sampling

Walker, Gail; Sigman, Richard

doi:10.1080/03610928408828873

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…Pfeffermann [5] and Rao and Molina [4] provided a comprehensive overview of the development, methods, and application of small-area estimation, including various types of area-level and unit-level models. Walker et al [6] compared the Huddleston-Ray and Battese-Fuller estimators for incorporating survey data and satellite data to improve acreage estimates. Subsequently, the celebrated work of Battese et al [7] introduced the unit-level models for small area estimation based on nested error linear regression using combinations of survey and satellite data.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Estimates for Farm Labor Quantities

Chen

Cruze

Young

2022

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The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) conducts the Farm Labor Survey to produce estimates of the number of workers, duration of the workweek, and wage rates for all agricultural workers. Traditionally, expert opinion is used to integrate auxiliary information, such as the previous year’s estimates, with the survey’s direct estimates. Alternatively, implementing small area models for integrating survey estimates with additional sources of information provides more reliable official estimates and valid measures of uncertainty for each type of estimate. In this paper, several hierarchical Bayesian subarea-level models are developed in support of different estimates of interest in the Farm Labor Survey. A 2020 case study illustrates the improvement of the direct survey estimates for areas with small sample sizes by using auxiliary information and borrowing information across areas and subareas. The resulting framework provides a complete set of coherent estimates for all required geographic levels. These methods were incorporated into the official Farm Labor publication for the first time in 2020.

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Section: Introductionmentioning

confidence: 99%

Model-Based Estimates for Farm Labor Quantities

Chen

Cruze

Young

2022

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Remote sensing and land cover area estimation

Gallego

2004

International Journal of Remote Sensing

249

121

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Using Remote Sensing for Agricultural Statistics

Carfagna¹,

Gallego²

2006

International Statistical Review

105

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Summary Remote sensing can be a valuable tool for agricultural statistics when area frames or multiple frames are used. At the design level, remote sensing typically helps in the definition of sampling units and the stratification, but can also be exploited to optimise the sample allocation and size of sampling units. At the estimator level, classified satellite images are generally used as auxiliary variables in a regression estimator or for estimators based on confusion matrixes. The most often used satellite images are LANDSAT‐TM and SPOT‐XS. In general, classified or photo‐interpreted images should not be directly used to estimate crop areas because the proportion of pixels classified into the specific crop is often strongly biased. Vegetation indexes computed from satellite images can give in some cases a good indication of the potential crop yield.

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The use of landsat for county estimates of crop areas: evaluation of the huddleston-ray and the battese-fuller estimators for & the case of stratified sampling

Cited by 4 publications

References 2 publications

Model-Based Estimates for Farm Labor Quantities

Model-Based Estimates for Farm Labor Quantities

Remote sensing and land cover area estimation

Using Remote Sensing for Agricultural Statistics

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