Partial least-squares regression for soil salinity mapping in Bangladesh

被引:7
|
作者
Sarkar, Showmitra Kumar [1 ]
Rudra, Rhyme Rubayet [1 ]
Nur, Md. Sadmin [1 ]
Das, Palash Chandra [1 ]
机构
[1] Khulna Univ Engn Technol KUET, Dept Urban & Reg Planning, Khulna 9203, Bangladesh
关键词
Bangladesh; Partial least-squares regression; GIS; Remote sensing; Salinity indices; MODELS; SUM;
D O I
10.1016/j.ecolind.2023.110825
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Estimating the salinity of the soil along the coast of south-western Bangladesh is the focus of this study. Thirteen soil salinity indicators were computed using the Landsat OLI images, and 241 soil salinity samples were gathered from secondary sources. In this research, partial least-squares regression was used to estimate soil salinity. Soil salinity zones were then divided into five classes based on Jenks natural breaks. There is a strong relationship between soil salinity and several indices, including B2 (Blue band), B5 (Near-Infrared band), SI_1 (Salinity index 1), and SI_2 (Salinity index 2). The correlation between B2 and soil salinity is particularly high, hovering around 0.92. The majority of other factors, however, have a negative or barely positive correlation of -0.260 to 0.032. Very high and high levels of salinity are estimated to cover 29.02% and 13.45% of the study area, respectively, based on the soil salinity map. Finally, cross-validation has been used to see how well the model works to predict soil salinity for a specified weight. Iteration has been done three times in order to assess the model's performance on various subsets of data and produce a more accurate prediction. In the third iteration, PRESS, RSS, and Q2cum values were 1.844826, 3.354687, and 0.9705131, correspondingly, which indicates a higher predictive accuracy. The findings of the study will be useful as a tool for policymakers in the south-western coastal area of Bangladesh who are responsible for integrated coastal zone management.
引用
收藏
页数:9
相关论文
共 50 条
  • [31] CONTINUUM REGRESSION - CROSS-VALIDATED SEQUENTIALLY CONSTRUCTED PREDICTION EMBRACING ORDINARY LEAST-SQUARES, PARTIAL LEAST-SQUARES AND PRINCIPAL COMPONENTS REGRESSION
    STONE, M
    BROOKS, RJ
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL, 1990, 52 (02): : 237 - 269
  • [32] Robust partial least-squares regression: A modular neural network approach
    McDowall, TM
    Ham, FM
    [J]. APPLICATIONS AND SCIENCE OF ARTIFICIAL NEURAL NETWORKS III, 1997, 3077 : 344 - 355
  • [33] On partial least-squares estimation in scalar-on-function regression models
    Saricam, Semanur
    Beyaztas, Ufuk
    Asikgil, Baris
    Shang, Han Lin
    [J]. JOURNAL OF CHEMOMETRICS, 2022, 36 (12)
  • [34] PARTIAL LEAST-SQUARES REGRESSION ON DESIGN VARIABLES AS AN ALTERNATIVE TO ANALYSIS OF VARIANCE
    MARTENS, H
    IZQUIERDO, L
    THOMASSEN, M
    MARTENS, M
    [J]. ANALYTICA CHIMICA ACTA, 1986, 191 : 133 - 148
  • [35] Partial least-squares regression for the quantitation of pharmaceutical dosages in control analyses
    Blanco, M
    Coello, J
    Elaamrani, M
    Iturriaga, H
    Maspoch, S
    [J]. JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS, 1996, 15 (03) : 329 - 338
  • [36] Using partial least-squares regression to develop a model of rice evapotranspiration
    Fu, Q
    Wang, LK
    Song, L
    Xing, ZX
    [J]. JOURNAL OF EXPERIMENTAL BOTANY, 2003, 54 : 42 - 42
  • [37] A Harmonic Measurement Mode Based on Partial Least-squares Regression Method
    Zhang Yunyan
    Cai Wei
    Deng Chun
    Xu Yonghai
    Gao Lin
    [J]. 2008 IEEE ELECTRICAL POWER AND ENERGY CONFERENCE, 2008, : 553 - +
  • [38] Using partial least-squares regression to develop a model of rice evapotranspiration
    Fu, Q
    Wang, LK
    Song, L
    Xing, ZX
    [J]. WATER-SAVING AGRICULTURE AND SUSTAINABLE USE OF WATER AND LAND RESOURCES, VOLS 1 AND 2, PROCEEDINGS, 2004, : 550 - 557
  • [39] Coal Price Index Forecast by a New Partial Least-Squares Regression
    Zhang, Bo
    Ma, Junhai
    [J]. CEIS 2011, 2011, 15
  • [40] Prediction-weighted partial least-squares regression method (PWPLS)
    Tominaga, Y
    Fujiwara, I
    [J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1997, 38 (02) : 139 - 144