Optimal spatial sampling techniques for ground truth data in microwave remote sensing of soil moisture

Microwave remote sensing of soil moisture is currently being explored by a series of both active and passive experiments with the sensor output then related to soil moisture laboratory measurements made on field-collected samples taken at the time of microwave data acquisition. In addition to diurnal variation, soil moisture varies widely with surface location and depth; furthermore, the cost of sample extraction increases markedly with depth. Therefore it is desirable to identify sampling techniques which give acceptable statistical validity while minimizing the effort involved in sample extraction. Data from an extensive soil sample collection program carried out in April, 1976 near Perry-Topeka, Kansas were used as input to five statistical sampling tests based on both simple random sampling and stratified sampling. In addition to the relation between desired sample size and depth, the tests were applied to various field cell sizes corresponding to the resolution cell of the microwave remote sensor; field cells ranging from 2.5 to 40 acres in size were considered, and depths to 45 cm were included. If the total number of samples taken during a ground truth mission can be prespecified, then stratified sampling based on optimal allocation is to be preferred; otherwise, simple random sampling should be used. As an example, in the top 0–1 cm layer of a 20-acre field, 35 soil samples would be required using simple random sampling whereas only 19 samples would be required using optimal allocation stratified sampling. This reduction in the number of samples is a consequence of the higher, weighting assigned to the surface layers which exhibit the greatest soil moisture variability with spatial position.