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Maximizing dataset variability in agricultural surveys with spatial sampling based on MaxVol matrix approximationстатья
Статья опубликована в высокорейтинговом журнале
Статья опубликована в журнале из списка Web of Science и/или Scopus- Авторы: Petrovskaia Anna, Gasanov Mikhail, Nikitin Artyom, Tregubova Polina, Oseledets Ivan
- Журнал: Precision Agriculture
- Том: 26
- Номер: 1
- Год издания: 2024
- Издательство: Springer Nature
- Местоположение издательства: Switzerland
- Первая страница: 1573
- Последняя страница: 1618
- Номер статьи: 9
- DOI: 10.1007/s11119-024-10197-y
- Аннотация: Soil sampling is crucial for capturing soil variability and obtaining comprehensive soil information for agricultural planning. This article evaluates the potential of MaxVol, an optimal design method for soil sampling based on selecting locations with significant dissimilarities. We compared MaxVol with conditional Latin hypercube sampling (cLHS), simple random sampling (SRS) and Kennard-Stone algorithm (KS) to evaluate their ability to capture soil data distribution. We modeled spatial distributions of soil properties using simple kriging (SK) and regression kriging (RK) interpolation techniques and assessed the interpolation quality using Root Mean Square Error. According to the results, MaxVol performs similarly or better than popular sampling designs in describing soil distributions, particularly with a smaller number of points. This is valuable for costly and time-consuming field surveys. Both MaxVol and Kennard-Stone are deterministic algorithms, unlike cLHS and random sampling, providing a reliable sampling scheme. Thus, the proposed MaxVol algorithm enables obtaining soil property distributions based on environmental features.
- Добавил в систему: Гасанов Михаил Эльдарович