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Delineation and optimization of cotton farmland management zone based on time series of soil-crop properties at landscape scale in south Xinjiang, China

Nan Wang, Dongyun Xu, Jie Xue, Xianglin Zhang, Yongsheng Hong, Jie Peng, Hongyi Li, Abdul Mouazen (UGent) , Yong He and Zhou Shi
(2023) SOIL & TILLAGE RESEARCH. 231.
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Abstract
Traditional farmland management approaches treat farmland as a homogeneous unit, leading to uneven distribution and waste of resources, which is contrary to precision agriculture. Management zones (MZ) delineation is an essential method of site-specific management (SSM) for accomplishing variable rate input of resources in subregions by recognizing the heterogeneity of farmland soils. Aimed at the soil salinization problem in Xinjiang, this study estimates the salt content of soil profiles and essential nutrient content using time series Sentinel-2 images and machine learning methods, and describes crop growth conditions through dynamic time warping. Subsequently, geographically weighted principal component analysis and possibilistic fuzzy C-means algorithm were employed to outline MZ based on soil-crop characteristics. Three scales (10 m, 100 m, and field scales divided by ridges) were examined to obtain the optimal scale of delineation. Soil-crop properties were estimated at a spatial resolution of 10 m using machine learning methods with R2 > 0.65. In the zoning process, the field scale was selected with accuracy and practicality in agricultural practices, with comparatively higher homogeneity within zones and variation between zones. The soils in the study area were categorized into three zones at field scale with a fuzzy performance index of 0.68, a normalized classification entropy of 0.71, and fuzzy class of 0.30. Soil function is primarily restricted by salinity and fertility. Fertilizer recommendation strategies were given for the three zones. This study uses time-series remote sensing images to obtain a high-precision estimation of soil-crop characteristics at 10 m, and optimize the scale of farmland management zoning, which provides a valuable technology and scale selection for SSM for cotton management zone delineation in saline farmland in arid and semi-arid regions.
Keywords
Management zones, Remote sensing, Dynamic time warping, Multivariate spatial clustering, Scales optimization, VEGETATION INDEX, ELECTRICAL-CONDUCTIVITY, SPATIAL VARIABILITY, WET SEASONS, SALINITY, YIELD, REGION, MODIS, PHOSPHORUS, PHENOLOGY

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MLA
Wang, Nan, et al. “Delineation and Optimization of Cotton Farmland Management Zone Based on Time Series of Soil-Crop Properties at Landscape Scale in South Xinjiang, China.” SOIL & TILLAGE RESEARCH, vol. 231, 2023, doi:10.1016/j.still.2023.105744.
APA
Wang, N., Xu, D., Xue, J., Zhang, X., Hong, Y., Peng, J., … Shi, Z. (2023). Delineation and optimization of cotton farmland management zone based on time series of soil-crop properties at landscape scale in south Xinjiang, China. SOIL & TILLAGE RESEARCH, 231. https://doi.org/10.1016/j.still.2023.105744
Chicago author-date
Wang, Nan, Dongyun Xu, Jie Xue, Xianglin Zhang, Yongsheng Hong, Jie Peng, Hongyi Li, Abdul Mouazen, Yong He, and Zhou Shi. 2023. “Delineation and Optimization of Cotton Farmland Management Zone Based on Time Series of Soil-Crop Properties at Landscape Scale in South Xinjiang, China.” SOIL & TILLAGE RESEARCH 231. https://doi.org/10.1016/j.still.2023.105744.
Chicago author-date (all authors)
Wang, Nan, Dongyun Xu, Jie Xue, Xianglin Zhang, Yongsheng Hong, Jie Peng, Hongyi Li, Abdul Mouazen, Yong He, and Zhou Shi. 2023. “Delineation and Optimization of Cotton Farmland Management Zone Based on Time Series of Soil-Crop Properties at Landscape Scale in South Xinjiang, China.” SOIL & TILLAGE RESEARCH 231. doi:10.1016/j.still.2023.105744.
Vancouver
1.
Wang N, Xu D, Xue J, Zhang X, Hong Y, Peng J, et al. Delineation and optimization of cotton farmland management zone based on time series of soil-crop properties at landscape scale in south Xinjiang, China. SOIL & TILLAGE RESEARCH. 2023;231.
IEEE
[1]
N. Wang et al., “Delineation and optimization of cotton farmland management zone based on time series of soil-crop properties at landscape scale in south Xinjiang, China,” SOIL & TILLAGE RESEARCH, vol. 231, 2023.
@article{01HFP99E8X6461G1NMVVMQ1VJP,
  abstract     = {{Traditional farmland management approaches treat farmland as a homogeneous unit, leading to uneven distribution and waste of resources, which is contrary to precision agriculture. Management zones (MZ) delineation is an essential method of site-specific management (SSM) for accomplishing variable rate input of resources in subregions by recognizing the heterogeneity of farmland soils. Aimed at the soil salinization problem in Xinjiang, this study estimates the salt content of soil profiles and essential nutrient content using time series Sentinel-2 images and machine learning methods, and describes crop growth conditions through dynamic time warping. Subsequently, geographically weighted principal component analysis and possibilistic fuzzy C-means algorithm were employed to outline MZ based on soil-crop characteristics. Three scales (10 m, 100 m, and field scales divided by ridges) were examined to obtain the optimal scale of delineation. Soil-crop properties were estimated at a spatial resolution of 10 m using machine learning methods with R2 > 0.65. In the zoning process, the field scale was selected with accuracy and practicality in agricultural practices, with comparatively higher homogeneity within zones and variation between zones. The soils in the study area were categorized into three zones at field scale with a fuzzy performance index of 0.68, a normalized classification entropy of 0.71, and fuzzy class of 0.30. Soil function is primarily restricted by salinity and fertility. Fertilizer recommendation strategies were given for the three zones. This study uses time-series remote sensing images to obtain a high-precision estimation of soil-crop characteristics at 10 m, and optimize the scale of farmland management zoning, which provides a valuable technology and scale selection for SSM for cotton management zone delineation in saline farmland in arid and semi-arid regions.}},
  articleno    = {{105744}},
  author       = {{Wang, Nan and Xu, Dongyun and Xue, Jie and Zhang, Xianglin and Hong, Yongsheng and Peng, Jie and Li, Hongyi and Mouazen, Abdul and He, Yong and Shi, Zhou}},
  issn         = {{0167-1987}},
  journal      = {{SOIL & TILLAGE RESEARCH}},
  keywords     = {{Management zones,Remote sensing,Dynamic time warping,Multivariate spatial clustering,Scales optimization,VEGETATION INDEX,ELECTRICAL-CONDUCTIVITY,SPATIAL VARIABILITY,WET SEASONS,SALINITY,YIELD,REGION,MODIS,PHOSPHORUS,PHENOLOGY}},
  language     = {{eng}},
  pages        = {{17}},
  title        = {{Delineation and optimization of cotton farmland management zone based on time series of soil-crop properties at landscape scale in south Xinjiang, China}},
  url          = {{http://doi.org/10.1016/j.still.2023.105744}},
  volume       = {{231}},
  year         = {{2023}},
}
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