The technology of remote sensing combining sampling is an effective way to estimate
crop acreage (CA) at large scale. Previous research proved that if the crop proportion within
a sampling unit is sufficiently stable from year to year, pixels classified from historical remote
sensing images could offer reliable regression estimators for current CA. However, previous
works explored various approaches for CA estimation using one-year historical data, which
makes it difficult to determine which year has the highest correlation to the targeted year, especially
for a region where background information about the cultivated planting system is scarce.
We estimated the winter wheat acreage of Beijing in 2009 by using two stratification variables
including the coefficient of variance and the mean CA of the sampling units via a two-stage
stratified sampling method with multiple historical remotely sensed data. Results show that:
(1) our method has higher sampling average accuracy and lower standard errors of sample averages
than simple random sampling or the one-stage stratification method with CA as the
auxiliary variable, which are the usual methods employed in the previous studies. (2) Fewer
samples are required to get the predefined accuracy, which reduces costs. (3) Generally,
using mean CA derived from multiyear historical remotely sensed data as the auxiliary variable
has a higher accuracy than those data using CA derived from one-year historical remote sensing
images as the auxiliary variable in one-stage sampling.
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