干旱区研究

Arid Zone Research >

2022 , Vol. 39 >Issue 2: 646 - 655

DOI: https://doi.org/10.13866/j.azr.2022.02.31

Others

Precise management zoning in arid soil croplands based on multi-source data

  • Jianduo BAI ,
  • Jie PENG ,
  • Zhou SHI ,
  • Yuzhen WANG ,
  • Weiyang LIU ,
  • Hongyi LI
Expand
  • 1. College of Agriculture, Tarim University, Alar 843300, Xinjiang, China
    2. Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310058, Zhejiang, China
    3. College of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330000, Jiangxi, China

Received date: 2021-08-21

  Revised date: 2021-12-03

  Online published: 2022-03-30

Fold

Abstract

According to the spatial heterogeneity of soil salinization, the precise management precise zoning of oasis farmland in the arid area of Southern Xinjiang is of great significance for the adjustment of areas, based on spatial heterogeneity in soil salinization, is important for determining agricultural planting structure structures and fine-scale management. This study selected the topical oasis farmland in arid zone as the study object and used electromagnetic induction data, topographic date, and satellite remote sensing data as for oasis farmland in the data sources. We analyzed arid zone of Southern Xinjiang to analyze the spatial heterogeneity of soil salinization by geostatistical. Geostatistical methods and screened the were used to evaluate vegetation index and salt index in indices for different periods with correlation analysis. The surface apparent electrical conductivity (ECh0.375) of the farmland was used as the main variable, and the deep apparent electrical conductivity (ECh0.75, ECV0.75, ECV1.5), vegetation index (RVI, GRVI, EVI), and soil salinity index (NDSI, S5, SI-T) were used as the auxiliary variables. The study area was partitioned by using an object-oriented multi-scale segmentation algorithm, and the zoning results were evaluated by the mean Coefficient of variation (CV) and Moran’s I. The results showed that there was obvious spatial heterogeneity in the apparent electrical conductivity of each soil layers in the study area, and the auxiliary variables were significantly correlated with the main variable. The average coefficient of variation of each partition was reduced by 60% compared with that of the whole study area, and the. The interval heterogeneity based on multi-source data zoning was higher than that based on single-source data zoning. From the perspective of farmland cultivation, segmentation effects, and zoning evaluation principles, the, management zoning effect that integrated the information of surface and deep soil salinization was the best. And the zoning results were not only consistent with both local farmland management but also met the mechanized operation requirements. The findings can provide certain a technical and methodological reference for precise management zoning of oasis farmland in arid areas of Southern Xinjiang.

Key words： soil salinization; multi-source data; apparent electrical conductivity; object-oriented segmentation; precise management zoning; arid area

Cite this article

Jianduo BAI , Jie PENG , Zhou SHI , Yuzhen WANG , Weiyang LIU , Hongyi LI . Precise management zoning in arid soil croplands based on multi-source data[J]. Arid Zone Research, 2022 , 39(2) : 646 -655 . DOI: 10.13866/j.azr.2022.02.31

References

[1] 杨劲松. 中国盐渍土研究的发展历程与展望[J]. 土壤学报, 2008, 45(5):837-845.
[1] [ Yang Jingsong. Development and prospect of the research on salt-affected soils in China[J]. Acta Pedologica Sinica, 2008, 45(5):837-845. ]
[2] Maas E V, Hoffman G J. Crop salt tolerance: Evaluation of existing data[C]// Managing Saline Water for Irrigation. Proceedings of the International Salinity Conference, 1977.
[3] Serrano J, Shahidian S, Marques da Silva J, et al. Mapping management zones based on soil apparent electrical conductivity and remote sensing for implementation of variable rate irrigation: Case study of corn under a center pivot[J]. Water, 2020, 12(12):3427.
[4] 刘焕军, 殷悦, 鲍依临, 等. 黑土区田块尺度精准管理遥感分区时空格局与成因分析[J]. 农业工程学报, 2021, 37(3):147-154.
[4] [ Liu Huanjun, Yin Yue, Bao Yilin, et al. Spatial-temporal pattern and cause analysis for accurate management of remote sensing zoning at field scale in black soil areas[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(3):147-154. ]
[5] Haghverdi A, Leib B G, Washington-Allen R A, et al. Perspectives on delineating management zones for variable rate irrigation[J]. Computers and Electronics in Agriculture, 2015, 117:154-167.
[6] Córdoba M, Bruno C, Costa J, et al. Subfield management class delineation using cluster analysis from spatial principal components of soil variables[J]. Computers and Electronics in Agriculture, 2013, 97:6-14.
[7] 李茂娜, 孙宇, 严海军, 等. 基于土壤表观电导率的变量灌溉管理分区方法[J]. 农业工程学报, 2020, 36(22):172-180.
[7] [ Li Maona, Sun Yu, Yan Haijun, et al. Method for variable rate irrigation management zone delineation based on apparent soil electrical conductivity[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(22):172-180. ]
[8] 朱昌达, 高明秀, 王文倩, 等. 基于GIS的滨海盐渍化农田土壤空间变异及其分区管理[J]. 生态学报, 2020, 40(19):6982-6990.
[8] [ Zhu Changda, Gao Mingxiu, Wang Wenqian, et al. Spatial variability and zoning management of coastal salinized farmland soil based on GIS[J]. Acta Ecologica Sinica, 2020, 40(19):6982-6990. ]
[9] Zeraatpisheh M, Bakhshandeh E, Emadi M, et al. Integration of PCA and fuzzy clustering for delineation of soil management zones and cost-efficiency analysis in a citrus plantation[J]. Sustainability, 2020, 12(14):5809.
[10] Karydas C, Iatrou M, Iatrou G, et al. Management zone delineation for site-specific fertilization in rice crop using multi-temporal rapidEye imagery[J]. Remote Sensing, 2020, 12(16):2604.
[11] 刘焕军, 邱政超, 孟令华, 等. 黑土区田块尺度遥感精准管理分区[J]. 遥感学报, 2017, 21(3):470-478.
[11] [ Liu Huanjun, Qiu Zhengchao, Meng Linghua, et al. Site-specific management zone of field scale based on remote sensing image in a black soil area[J]. National Remote Sensing Bulletin, 2017, 21(3):470-478. ]
[12] Damian J M, Pias O H C, Cherubin M R, et al. Applying the NDVI from satellite images in delimiting management zones for annual crops[J]. Scientia Agricola, 2020, 77(1):0055. doi: 10.1590/1678-992X-2018-0055.
[13] 刘焕军, 鲍依临, 徐梦园, 等. 基于SOM和NDVI的黑土区精准管理分区对比[J]. 农业工程学报, 2019, 35(13):177-183.
[13] [ Liu Huanjun, Bao Yilin, Xu Mengyuan, et al. Comparison of precision management zoning methods in black soil area based on SOM and NDVI[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(13):177-183. ]
[14] 周林虎, 王昊宇, 张秉来, 等. 硫酸盐渍土表观电导率与水分、盐分及粒径关系研究[J]. 干旱区研究, 2021, 38(4):1020-1030.
[14] [ Zhou Linhu, Wang Haoyu, Zhang Binglai, et al. The relationship between ECa of sulfate saline soil and moisture content, salt content, and particle size[J]. Arid Zone Research, 2021, 38(4):1020-1030. ]
[15] Moral F J, Terrón J M, Da Silva J R M. Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques[J]. Soil and Tillage Research, 2010, 106(2):335-343.
[16] Rhoades J D, van Schilfgaarde J. An electrical conductivity probe for determining soil salinity[J]. Soil Science Society of America Journal, 1976, 40(5):647-651.
[17] Rhoades J D, Lesch S M, LeMert R D, et al. Assessing irrigation/drainage/salinity management using spatially referenced salinity measurements[J]. Agricultural Water Management, 1997, 35(1-2):147-165.
[18] Yao R J, Yang J S, Liu G M. Calibration of soil electromagnetic conductivity in inverted salinity profiles with an integration method[J]. Pedosphere, 2007, 17(2):246-256.
[19] Tetteh G O, Gocht A, Conrad C. Optimal parameters for delineating agricultural parcels from satellite images based on supervised Bayesian optimization[J]. Computers and Electronics in Agriculture, 2020, 178:105696.
[20] Benz U C, Hofmann P, Willhauck G, et al. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2004, 58(3-4):239-258.
[21] Chang D, Zhang J, Zhu L, et al. Delineation of management zones using an active canopy sensor for a tobacco field[J]. Computers and Electronics in Agriculture, 2014, 109:172-178.
[22] 陈春雷, 武刚. 面向对象的遥感影像最优分割尺度评价[J]. 遥感技术与应用, 2011, 26(1):96-102.
[22] [ Chen Cunlei, Wu Gang. Evaluation of optimal segmentation scale with object-oriented method in remote sensing[J]. Remote Sensing Technology and Application, 2011, 26(1):96-102. ]
[23] 彭杰. 荒漠土壤盐渍化遥感监测与开垦方案分析——以空台里克冲积扇为例[D]. 杭州: 浙江大学, 2019.
[23] [ Peng Jie. Salinzation Monitoring and Reclamation Strategy Analysis in of Desert Soil Using Remote Sensing: A Case Study in the Kongtailike Alluvial Fan[D]. Hangzhou: Zhejiang University, 2019. ]
[24] 刘宁. 不同土地利用方式下黄河三角洲土壤特性空间变异研究[D]. 泰安: 山东农业大学, 2007.
[24] [ Liu Ning. Spatial Variability of Soil Characteristics on Different Land Use Types in the Yellow River Delta[D]. Tai’an: Shandong Agricultural University, 2007. ]
[25] 白建铎, 彭杰, 白子金, 等. 干旱区棉田表层土壤盐渍化时空变异研究[J]. 土壤通报, 2021, 52(3):527-534.
[25] [ Bai Jianduo, Peng Jie, Bai Zijin, et al. Clarifying spatial-temporal variability of surface soil salinization in arid cotton fields[J]. Chinese Journal of Soil Science, 2021, 52(3):527-534. ]
[26] 许盼盼. 基于高时空分辨率数据的湿地精细分类研究[D]. 北京: 中国科学院大学, 2018.
[26] [ Xu Panpan. Study on Finer Mapping of Wetlands Based on High Temporal and High Spatial Resolution Date[D]. Beijing: University of Chinese Academy of Sciences, 2018. ]
[27] 黄万里. 基于高分卫星数据多尺度图像分割方法的天山森林小班边界提取研究[D]. 福州: 福建师范大学, 2015.
[27] [ Huang Wanli. Study on Sub-compartment Division of Tiansan Forest Based on High Spatial Resolution Satellite Images and Multi-Scale Image Segmentation Methods[D]. Fuzhou: Fujian Normal University, 2015. ]
Options
Outlines

/

〈 〉