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ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Aerial Application Technology Research » Research » Publications at this Location » Publication #359562
Research Project: Aerial Application Technology for Sustainable Crop Production

Location: Aerial Application Technology Research

Title: An index of non-sampling error in area frame sampling based on remote sensing data

Author
item WU, MINGQUAN - Chinese Academy Of Sciences
item PENG, DAILIANG - Chinese Academy Of Sciences
item QIN, YUCHU - Chinese Academy Of Sciences
item NIU, ZHENG - Chinese Academy Of Sciences
item Yang, Chenghai
item LI, WANG - Chinese Academy Of Sciences
item HAO, PENGYU - Chinese Academy Of Agricultural Sciences
item ZHANG, CHUNYANG - Chinese Academy Of Sciences

Submitted to: PeerJ
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/15/2018
Publication Date: 12/29/2018
Citation: Wu, M., Peng, D., Qin, Y., Niu, Z., Yang, C., Li, W., Hao, P., Zhang, C. 2018. An index of non-sampling error in area frame sampling based on remote sensing data. PeerJ. 6:e5824. https://doi.org/10.7717/peerj.5824.
DOI: https://doi.org/10.7717/peerj.5824

Interpretive Summary: Using outdated, or non-updated, remotely sensed data contained in typical agricultural statistic databases may cause significant non-sampling errors due to the change of land cover and use types between updates. This study proposed a novel remote sensing-based index to estimate these non-sampling errors. This index was then used to determine if non-updated area sampling units were usable for area estimation. Validation results with satellite image data in a large cropping region showed the proposed method could more accurately estimate cropping areas than traditional methods. The proposed index can be easily used in agricultural statistics to improve estimation accuracy and reduce survey time.

Technical Abstract: Agricultural areas are often surveyed using area frame sampling. Using non-updated area sampling frame causes significant non-sampling errors when land cover and usage changes between updates. To address this problem, a novel method is proposed to estimate non-sampling errors in crop area statistics. Three parameters used in stratified sampling that are affected by land use changes were monitored using satellite remote sensing imagery: (1) the total number of sampling units; (2) the number of sampling units in each stratum; and (3) the mean value of selected sampling units in each stratum. A new index, called the non-sampling error by land use change index (NELUCI), was defined to estimate non-sampling errors. Using this method, the sizes of cropping areas in Bole, Xinjiang, China, were estimated with a coefficient of variation of 0.0237 and NELUCI of 0.0379. These are 0.0474 and 0.0994 lower, respectively, than errors calculated by traditional methods based on non-updated area sampling frame and selected sampling units.