Optimal interpolation methods for farmland soil organic matter in various landforms of a complex topography
By J. Long, Yaling Liu, S. Xing, Liming Zhang, Mingkai Qu, Longxia Qiu, Qian Huang, Biqing Zhou, Jinquan Shen. 2020
46 citations recorded by the source corpus.
Abstract Interpolation plays an important role in revealing the spatial variations of soil organic matter (SOM). However, selecting an optimal one from multiple interpolations is challenging, particularly in the complex topography which is characterized by slope-rich terrain. In this work, the study area is a large region of slope-rich and complex topography, encompassing 9 cities and including 3 landforms of plain-platform, valley-basin, and hill-mountain. Based on 188,247 sampling sites, 12 widely used deterministic and geo-statistical interpolations were performed for farmland SOM estimation. Moreover, ancillary variables such as terrain, climate and soil cover patterns were combined with the optimal models of the above interpolations to examine the effects of incorporation of environmental variables on interpolation accuracy. The results showed that interpolation accuracy varied from landform to landform. The prediction accuracy of geo-statistical interpolations was generally higher than that of deterministic methods, and ancillary variables were generally effective to improve the interpolation accuracy. Specifically, for the entire region, simple kriging (SK) was the most accurate of these 12 interpolations, with a root-mean-square prediction error (RMSE) of 6.75 g kg−1 and a correlation coefficient (r) of 0.79 between the predicted and measured values (p IDW > IDW_T > IDW_C. Our findings could inform interpolation of many environmental variables, not tied to SOM, in similar areas characterized by various landforms of a complex topography.
Subfields: Environmental Engineering, Soil Science
View Paper: https://www.semanticscholar.org/paper/ef62d1f66ae37d8ec4fbcf4eefd7c947582b1053
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In Environmental Engineering
- Gerard B. M. Heuvelink
- Mladen Nikolic
- Aleksandar Sekulić
- Branislav Bajat
- M. Kilibarda
- Tomislav Hengl
In Soil Science
Citations in the corpus
- Random Forest Spatial Interpolation (2020, methodology): “…and outperformed all other methods, including STRK and RFsp. Malamos and Koutsoyiannis [54], Liao et al [55], Qiao et al. [56] and Long et al. [57] compared OK and IDW (among other methods) and also reported that IDW had similar performance and sometimes outperformed kriging in real-world…”