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Int. J. Environ. Res. Public Health 2016, 13(10), 980;

Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations

School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210046, China
Nanjing Nanyuan Land Development and Utilization Consulting Co. Ltd., Nanjing 210008, China
Author to whom correspondence should be addressed.
Academic Editor: Jamal Jokar Arsanjani
Received: 10 July 2016 / Revised: 22 September 2016 / Accepted: 27 September 2016 / Published: 30 September 2016
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With China’s rapid economic development, the reduction in arable land has emerged as one of the most prominent problems in the nation. The long-term dynamic monitoring of arable land quality is important for protecting arable land resources. An efficient practice is to select optimal sample points while obtaining accurate predictions. To this end, the selection of effective points from a dense set of soil sample points is an urgent problem. In this study, data were collected from Donghai County, Jiangsu Province, China. The number and layout of soil sample points are optimized by considering the spatial variations in soil properties and by using an improved simulated annealing (SA) algorithm. The conclusions are as follows: (1) Optimization results in the retention of more sample points in the moderate- and high-variation partitions of the study area; (2) The number of optimal sample points obtained with the improved SA algorithm is markedly reduced, while the accuracy of the predicted soil properties is improved by approximately 5% compared with the raw data; (3) With regard to the monitoring of arable land quality, a dense distribution of sample points is needed to monitor the granularity. View Full-Text
Keywords: land evaluation; simulated annealing; soil sample points land evaluation; simulated annealing; soil sample points

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Wang, J.; Wang, X.; Zhou, S.; Wu, S.; Zhu, Y.; Lu, C. Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations. Int. J. Environ. Res. Public Health 2016, 13, 980.

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