Inverting soil salinity of farmland in Xinjiang by integrating Sentinel-1/2 and environmental variables

BA Yalan; ZHANG Zhitao; XIE Pingliang; FAN Shuailong; DU Ruiqi; GUO Fei; QIAN Long; BAI Xuqian; HE Yujie; FAN Shaoshuai

doi:10.11975/j.issn.1002-6819.202401070

Volume 40 Issue 16

Aug. 2024

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Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE) > 2024 > 40(16): 171-179. > DOI: 10.11975/j.issn.1002-6819.202401070

BA Yalan, ZHANG Zhitao, XIE Pingliang, et al. Inverting soil salinity of farmland in Xinjiang by integrating Sentinel-1/2 and environmental variables[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2024, 40(16): 171-179. DOI: 10.11975/j.issn.1002-6819.202401070

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Inverting soil salinity of farmland in Xinjiang by integrating Sentinel-1/2 and environmental variables

1.
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A & F University, Yangling 712100, China
2.
College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling 712100, China

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Received Date: January 09, 2024
Revised Date: June 27, 2024
Available Online: August 04, 2024

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Abstract

Soil salinization is an important factor that jeopardizes agricultural production and ecological environment. Rapid and accurate acquisition of soil salinity information in farmland is instructive for sustainable agricultural development and land resource management. In order to improve the accuracy of soil salinity prediction under vegetation cover conditions by satellite remote sensing, the eighth Agricultural Division of Xinjiang Production and Construction Corps was taken as the study area in this study. The soil surface (0-20 cm) samples were collected under high fractional vegetation cover conditions in July and August 2023, respectively, and synchronized satellite images were acquired. Sentinel-1, Sentinel-2 and environment variables provide 3 different types of explanatory variables. The dataset A (polarization indices, spectral indices), dataset B (polarization indices, environment variables), dataset C (spectral indices, environment variables), and dataset D (polarization indices, spectral indices, environment variables) were constructed separately from different combinations of Sentinel-1 radar information, Sentinel-2 multispectral information and environment variables. Then, three integrated machine learning algorithms, namely adaptive boosting (AdaBoost), gradient boost regression Tree (GBRT) and eXtreme gradient boosting tree (XGBoost), were applied to construct soil salinity inversion models based on different datasets. The results showed that Models constructed from dataset B (polarization indices and environmental variables) and C (spectral indices and environmental variables) achieved higher prediction accuracies compared to dataset A (polarization indices and spectral indices). It is shown that when environmental variables are involved in the prediction of soil salinity, the model effect is more effective than the model constructed by polarization and spectral indices suggesting that the model effects are more effective than those constructed from polarization and spectral indices. When environmental variables were applied to dataset D together with polarization indices and spectral indices, the prediction accuracy of all models constructed based on dataset D are generally higher than those constructed on dataset A, B, and C, and that the synergy of environmental variables with radar data and multispectral data can effectively improve the model accuracy. Radar information, spectral information and environmental variables are complementary in soil salinity prediction. Based on the correlation analysis, it can be seen that radar information, spectral information and environmental variables can be used as effective characteristic variables for soil salinity prediction in the study area. It was worth noting that the correlation between topographic factors and land surface temperature with soil salinity is relatively high, with the highest correlation between elevation and surface soil salinity (r = 0.52). Considering the spatial characteristics of soil salinity distribution in the study area can provide effective characteristic variables for soil salinity prediction under vegetation cover condition. In all datasets, the XGBoost had the best performance, followed by GBRT, and the AdaBoost had a large validation error. The D-XGBoost model having the highest accuracy with a validation set R² of 0.72, an RMSE of 2.40 g/kg, and an MAE of 1.29 g/kg. The integrated learning algorithms based on the combination of multiple source variables has a strong nonlinear fitting ability. XGBoost can better model the complex nonlinear relationship between soil salinity content and remote sensing information, environmental factors, and obtain ideal fitting results. The joint application of multi-source remote sensing data and integrated learning algorithms can obtain the ideal soil salinity inversion accuracy under vegetation cover conditions. This study provides an effective technical means for real-time dynamic monitoring of soil salinity by satellite remote sensing in farmland to optimize irrigation strategies and manage saline soils comprehensively in Xinjiang.
- soils,
- salinity content,
- Sentinel-1/2,
- environment variables,
- integrated machine learning algorithms

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References

[1]	贺玉洁,张智韬,巴亚岚,等. 基于Sentinel-2卫星数据的灌区农田土壤水盐协同反演[J]. 农业工程学报,2023,39(9):111-121. HE Yujie, ZHANG Zhitao, BA Yalan, et al. Synergistic inversion of water and salt in irrigated agricultural soils based on Sentinel-2[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(9): 111-121. (in Chinese with English abstract)
[2]	薛新伟,杨恒山,张瑞富,等. 滴灌对半干旱地区土壤水盐运动和次生盐渍化影响的研究进展[J]. 中国农学通报,2019,35(32):89-94. doi: 10.11924/j.issn.1000-6850.casb18050128 XUE Xinwei, YANG Hengshan, ZHANG Ruifu, et al. Effects of drip irrigation on soil sater and salt movement and secondary salinization in Semi-arid areas: Research progress[J]. Chinese Agricultural Science Bulletin, 2019, 35(32): 89-94. (in Chinese with English abstract) doi: 10.11924/j.issn.1000-6850.casb18050128
[3]	盛丰,张敏,薛如霞,等. 灌溉水中盐分对土壤结构性质及水流运动特征的影响[J]. 水利学报,2019,50(3):346-355. SHENG Feng, ZHANG Min, XUE Ruxia, et al. Effects of salt in irrigation water on soil structural properties and water flow characteristics[J]. Journal of Hydraulic Engineering, 2019, 50(3): 346-355. (in Chinese with English abstract)
[4]	SUN K, SU Q, MING Y. Dust storm remote sensing monitoring supported by MODIS land surface reflectance database[J]. Remote Sensing, 2019, 11(15): 1772. doi: 10.3390/rs11151772
[5]	TRIPATHI A, TIWARI R K. A simplified subsurface soil salinity estimation using synergy of SENTINEL-1 SAR and SENTINEL-2 multispectral satellite data, for early stages of wheat crop growth in Rupnagar, Punjab, India[J]. Land Degradation & Development, 2021, 32(14): 3905-3919.
[6]	马驰,刘晓波. 基于Sentinel-1/2的土壤盐分含量反演研究[J]. 干旱地区农业研究,2022,40(5):252-259. doi: 10.7606/j.issn.1000-7601.2022.05.27 MA Chi, LIU Xiaobo. Inversion of soil salt content based on Sentinel-1/2[J]. Agricultural Research in the Arid Areas, 2022, 40(5): 252-259. (in Chinese with English abstract) doi: 10.7606/j.issn.1000-7601.2022.05.27
[7]	袁玉芸,瓦哈甫·哈力克,关靖云,等. 基于GWR模型的于田绿洲土壤表层盐分空间分异及其影响因子[J]. 应用生态学报,2016,27(10):3273-3282. YUAN Yuyun, WAHAP Halike, GUAN Jingyun, et al. Spatial differentiation and impact factors of Yutian Oasis's soil surface salt based on GWR model[J]. Chinese Journal of Applied Ecology, 2016, 27(10): 3273-3282. (in Chinese with English abstract)
[8]	宋颖,高明秀,王佳凡,等. 基于MGWR的滨海区土壤盐渍化分布空间预测及影响因素分析[J/OL]. 环境科学,1-12 (2023-10-31) [2024-07-04]. https://www.hjkx.ac.cn/ch/reader/view_abstract.aspx?file_no=20240750&flag=1. SONG Ying, GAO Mingxiu, WANG Jiafan, et al. Spatial prediction and influencing factors analysis of soil salinization in coastal area based on MGWR [J/OL]. Environmental Science, 1-12. (2023-10-31) [2024-07-04]. https://www.hjkx.ac.cn/ch/reader/view_abstract.aspx?file_no=20240750&flag=1. (in Chinese with English abstract)
[9]	马国林,丁建丽,韩礼敬,等. 基于变量优选与机器学习的干旱区湿地土壤盐渍化数字制图[J]. 农业工程学报,2020,36(19):124-131. doi: 10.11975/j.issn.1002-6819.2020.19.014 MA Guolin, DING Jianli, HAN Lijing, et al. Digital mapping of soil salinization in arid area wetland based on variable optimized selection and machine learning[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(19): 124-131. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.2020.19.014
[10]	厉彦玲,赵庚星,常春艳,等. OLI与HSI影像融合的土壤盐分反演模型[J]. 农业工程学报,2017,33(21):173-180. doi: 10.11975/j.issn.1002-6819.2017.21.020 LI Yanling, ZHAO Gengxing, CHANG Chunyan, et al. Soil salinity retrieval model based on OLI and HSI image fusion[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(21): 173-180. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.2017.21.020
[11]	TAGHIZADEH-MEHRJARDI R, SCHMIDT K, AMIRIAN-CHAKAN A, et al. Improving the spatial prediction of soil organic carbon content in two contrasting climatic regions by stacking machine learning models and rescanning covariate space[J]. Remote Sensing, 2020, 12(7): 1095. doi: 10.3390/rs12071095
[12]	DU R, CHEN J, XIANG Y, et al. Timely monitoring of soil water-salt dynamics within cropland by hybrid spectral unmixing and machine learning models[J]. International Soil and Water Conservation Research, 2023, 12(3): 726-740.
[13]	姚宝林. 南疆免冬春灌棉田土壤水热盐时空迁移规律与调控研究[D]. 北京:中国农业大学,2017. YAO Baolin. Study on Rules and Rugulation of Soil Water-heat-salt Spatiotemporal Transfer under no Winter and Spring Irrigation Cotton Field in Southern Xinjiang[D]. Beijing: China Agricultural University, 2017. (in Chinese with English abstract)
[14]	MA G, DING J, HAN L, et al. Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms[J]. Regional Sustainability, 2021, 2(2): 177-188. doi: 10.1016/j.regsus.2021.06.001
[15]	WANG Z, ZHANG F, ZHANG X, et al. Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index[J]. Science of the Total Environment, 2021, 775: 145807. doi: 10.1016/j.scitotenv.2021.145807
[16]	马驰. 基于Sentinel-1双极化雷达影像的土壤含盐量反演[J]. 农业工程学报,2018,34(2):153-158. doi: 10.11975/j.issn.1002-6819.2018.02.021 MA Chi. Quantitative retrieval of soil salt content based on Sentinel-1 dual polarization radar image[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(2): 153-158. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.2018.02.021
[17]	刘瑞亮,贾科利,李小雨,等. 组合光学和微波遥感的耕地土壤含盐量反演[J]. 干旱区地理,2024,47(3):433-444. LIU Ruiliang, JIA Keli, LI Xiaoyu, et al. Inversion of soil salt content by combining optical and microwave remote sensing in cultivated land Online First[J]. Arid Land Geography, 2024, 47(3): 433-444. (in Chinese with English abstract)
[18]	WANG J, WU F, SHANG J, et al. Saline soil moisture mapping using Sentinel-1A synthetic aperture radar data and machine learning algorithms in humid region of China's east coast[J]. Catena, 2022, 213: 106189. doi: 10.1016/j.catena.2022.106189
[19]	ZHOU T, GENG Y, CHEN J, et al. High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms[J]. Science of the Total Environment, 2020, 729: 138244. doi: 10.1016/j.scitotenv.2020.138244
[20]	KHAN N M, RASTOSKUEV V V, SATO Y, et al. Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators[J]. Agricultural Water Management, 2005, 77(1-3): 96-109. doi: 10.1016/j.agwat.2004.09.038
[21]	BANNARI A, GUEDON A M, EL-HARTI A, et al. Characterization of slightly and moderately saline and sodic soils in irrigated agricultural land using simulated data of advanced land imaging (EO-1) sensor[J]. Communications in Soil Science and Plant Analysis, 2008, 39(19-20): 2795-2811. doi: 10.1080/00103620802432717
[22]	NICOLAS H, WALTER C. Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data[J]. Geoderma, 2006, 134(1/2): 217-230.
[23]	TRIPATHI N K, RAI B K, DWIVEDI P. Spatial modeling of soil alkalinity in GIS environment using IRS data[C]//Proceedings of the 18th Asian Conference on Remote Sensing, Kuala Lumpur: Malaysia. 1997: 20-24.
[24]	TAGHIZADEH-MEHRJARDI R, MINASNY B, SARMADIAN F, et al. Digital mapping of soil salinity in Ardakan region, central Iran[J]. Geoderma, 2014, 213: 15-28. doi: 10.1016/j.geoderma.2013.07.020
[25]	ROUSE J W, HAAS R H, SCHELL J A, et al. Monitoring vegetation systems in the Great Plains with ERTS[J]. Third NASA Earth Resources Technology Satellite Symposium, 1973, 1: 309-317.
[26]	WANG F, SHI Z, BISWAS A, et al. Multi-algorithm comparison for predicting soil salinity[J]. Geoderma, 2020, 365: 114211. doi: 10.1016/j.geoderma.2020.114211
[27]	CHEN H, ZHAO G, LI Y, et al. Monitoring the seasonal dynamics of soil salinization in the Yellow River delta of China using Landsat data[J]. Natural Hazards and Earth System Sciences, 2019, 19(7): 1499-1508. doi: 10.5194/nhess-19-1499-2019
[28]	WANG N, PENG J, XUE J, et al. A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network[J]. Geoderma, 2022, 409: 115656. doi: 10.1016/j.geoderma.2021.115656
[29]	GUYOT G, BARET F, MAJOR D J. High spectral resolution: Determination of spectral shifts between the red and near infrared[J]. 1988, 11(11):750-760.
[30]	王瑞燕,孔沈彬,许璐,等. 黄河三角洲不同地表覆被类型和微地貌的土壤盐分空间分布[J]. 农业工程学报,2020,36(19):132-141. doi: 10.11975/j.issn.1002-6819.2020.19.015 WANG Ruiyan, KONG Shenbin, XU Lu, et al. Spatial distribution of soil salinity under different surface land cover types and micro-topography in the Yellow River Delta[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(19): 132-141. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.2020.19.015
[31]	李军宏,王军,李楠楠,等. 无膜棉花产量及其根区温湿盐对灌溉量的响应[J]. 农业工程学报,2021,37(12):134-143. doi: 10.11975/j.issn.1002-6819.2021.12.016 LI Junhong, WANG Jun, LI Nannan, et al. Yield of non-film cotton and the response of temperature, humidity and salt in the root zone to irrigation[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(12): 134-143. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.2021.12.016
[32]	PRVOT L, CHAMPION I, GUYOT G. Estimating surface soil moisture and leaf area index of a wheat canopy using a dual-frequency (C and X bands) scatterometer[J]. Remote Sensing of Environment, 1993, 46(3): 331-339. doi: 10.1016/0034-4257(93)90053-Z
[33]	ATTEMA E P W, ULABY F T. Vegetation modeled as a water cloud[J]. Radio Science, 1978, 13(2): 357-364 doi: 10.1029/RS013i002p00357
[34]	赵翔,蔡博诚,王静,等. 基于GBRT模型的湖南县域农村居民点整治潜力预测[J]. 农业工程学报,2023,39(3):198-207. doi: 10.11975/j.issn.1002-6819.202211020 ZHAO Xiang, CAI Bocheng, WANG Jing, et al. Potential prediction of rural settlements reclamation in county level administrative regions of Hunan Province using gradient boosting regression tree model[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(3): 198-207. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.202211020
[35]	刘欣蓓,苏涛,雷波,等. 基于耦合算法的花生叶片光合色素含量反演模型[J]. 农业工程学报,2023,39(16):198-207. doi: 10.11975/j.issn.1002-6819.202303063 LIU Xinbei, SU Tao, LEI Bo, et al. Inverse model for the photosynthetic pigment content of peanut leaves using coupling algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(16): 198-207. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.202303063
[36]	吴彤,李勇,葛莹,等. 利用Stacking集成学习估算柑橘叶片氮含量[J]. 农业工程学报,2021,37(13):163-171. WU Tong, LI Yong, GE Ying, et al. Estimation of nitrogen contents in citrus leaves using Stacking ensemble learning[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(13): 163-171. (in Chinese with English abstract)
[37]	陈实,高超,徐斌,等. 新疆石河子农区土壤含盐量定量反演及其空间格局分析[J]. 地理研究,2014,33(11):2135-2144. CHEN Shi, GAO Chao, XU Bin, et al. Quantitative inversion of soil salinity and analysis of its spatial pattern in agricultural area in Shihezi of Xinjiang[J]. Geographical Research, 2014, 33(11): 2135-2144. (in Chinese with English abstract)
[38]	杨丽萍,任杰,王宇,等. 基于多源遥感数据的居延泽地区土壤盐分估算模型[J]. 农业机械学报,2022,53(11):226-235. doi: 10.6041/j.issn.1000-1298.2022.11.022 YANG Liping, REN Jie, WANG Yu, et al. Soil salinity estimation model in juyanze based on multi-source remote sensing data[J]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(11): 226-235. (in Chinese with English abstract) doi: 10.6041/j.issn.1000-1298.2022.11.022
[39]	HE Y, ZHANG Z, XIANG R, et al. Monitoring salinity in bare soil based on Sentinel-1/2 image fusion and machine learning[J]. Infrared Physics & Technology, 2023, 131: 104656.
[40]	张智韬,贺玉洁,殷皓原,等. 基于Sentinel-1/2改进极化指数和纹理特征的土壤含盐量反演模型[J]. 农业机械学报,2024,5(1):175-185. ZHANG Zhitao, HE Yujie, YIN Haoyuan, et al. Synergistic estimation of soil salinity based on Sentinel-1/2 improved polarization combination index and texture features[J]. Transactions of the Chinese Society for Agricultural Machinery, 2024, 5(1): 175-185. (in Chinese with English abstract)
[41]	CASTALDI F, HUENI A, CHABRILLAT S, et al. Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 147: 267-282. doi: 10.1016/j.isprsjprs.2018.11.026
[42]	赵文举,马芳芳,马宏,等. 基于无人机多光谱影像的土壤盐分反演模型[J]. 农业工程学报,2022,38(24):93-101. doi: 10.11975/j.issn.1002-6819.2022.24.010 ZHAO Wenju, MA Fangfang, MA Hong, et al. Soil salinity inversion model based on the multispectral images of UAV[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(24): 93-101. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.2022.24.010
[43]	陈一凡,李璇,王绍雯,等. 集成学习结合多源数据预测河南省冬小麦单产[J]. 农业工程学报,2024,40(4):177-185. CHEN Yifan, LI Xuan, WANG Shaowen, et al. Predicting winter wheat yield per unit area in Henan Province of China using ensemble learning and multi-source data[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2024, 40(4): 177-185. (in Chinese with English abstract)
[44]	DAS A, BHATTACHARYA B K, SETIA R, et al. A novel method for detecting soil salinity using AVIRIS-NG imaging spectroscopy and ensemble machine learning[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 200: 191-212. doi: 10.1016/j.isprsjprs.2023.04.018
[45]	AZIZI K, GAROSI Y, AYOUBI S, et al. Integration of Sentinel-1/2 and topographic attributes to predict the spatial distribution of soil texture fractions in some agricultural soils of western Iran[J]. Soil and Tillage Research, 2023, 229: 105681. doi: 10.1016/j.still.2023.105681
[46]	LI X, LUO J, JIN X, et al. Improving soil thickness estimations based on multiple environmental variables with stacking ensemble methods[J]. Remote Sensing, 2020, 12(21): 3609. doi: 10.3390/rs12213609

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