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Open AccessArticle

Prediction of Soil Nutrient Contents Using Visible and Near-Infrared Reflectance Spectroscopy

by Yiping Peng 1, Li Zhao 1, Yueming Hu 1,2,3,4,5,*, Guangxing Wang 1,6, Lu Wang 1 and Zhenhua Liu 1,*
1
College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
2
Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China
3
Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China
4
Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
5
College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China
6
Department of Geography and Environmental Resources, College of Liberal Arts, Southern Illinois University Carbondale (SIUC), Carbondale, IL 62901, USA
*
Authors to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(10), 437; https://doi.org/10.3390/ijgi8100437
Received: 25 July 2019 / Revised: 16 September 2019 / Accepted: 30 September 2019 / Published: 5 October 2019
Quickly and efficiently monitoring soil nutrient contents using remote sensing technology is of great significance for farmland soil productivity, food security and sustainable agricultural development. Current research has been conducted to estimate and map soil nutrient contents in large areas using hyper-spectral techniques, however, it is difficult to obtain accurate estimates. In order to improve the estimation accuracy of soil nutrient contents, we introduced a GA-BPNN method, which combined a back propagation neural network (BPNN) with the genetic algorithm optimization (GA). This study was conducted in Guangdong, China, based on soil nutrient contents and hyperspectral data. The prediction accuracies from a partial least squares regression (PLSR), BPNN and GA-BPNN were compared using field observations. The results showed that (1) Among three methods, the GA-BPNN provided the most accurate estimates of soil total nitrogen (TN), total phosphorus (TP) and total potassium (TK) contents; (2) Compared with the BPNN models, the GA-BPNN models significantly improved the estimation accuracies of the soil nutrient contents by decreasing the relative root mean square error (RRMSE) values by 15.9%, 5.6% and 20.2% at the sample point level, and 20.1%, 16.5% and 47.1% at the regional scale for TN, TP and TK, respectively. This indicated that by optimizing the parameters of BPNN, the GA-BPNN provided greater potential to improving the estimation; and (3) Soil TK content could be more accurately mapped by the GA-BPNN method using HuanJing-1A Hyperspectral Imager (HJ-1A HSI) (manufacturer: China Aerospace Science and Technology Corporation; Beijing, China) data with a RRMSE value of 20.37% than the soil TN and TP with the RRMSE values of 40.41% and 34.71%, respectively. This implied that the GA-BPNN model provided the potential to map the soil TK content for the large area. The research results provided an important reference for high-accuracy prediction of soil nutrient contents. View Full-Text
Keywords: soil nutrient contents; hyperspectral; accuracy improvement; BPNN; GA-BPNN soil nutrient contents; hyperspectral; accuracy improvement; BPNN; GA-BPNN
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Peng, Y.; Zhao, L.; Hu, Y.; Wang, G.; Wang, L.; Liu, Z. Prediction of Soil Nutrient Contents Using Visible and Near-Infrared Reflectance Spectroscopy. ISPRS Int. J. Geo-Inf. 2019, 8, 437.

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