基于PlanetScope影像的典型绿洲土壤盐渍化数字制图

doi:10.12118/j.issn.1000-6060.2022.496

Abstract

Abstract:

High-resolution soil salinity maps are urgently needed in arid and semi-arid regions to visualize the subtle spatial variations in salinity distribution. These maps are crucial for guiding the development of land resource management policies and water resource management policies in salt-affected and potentially salt-affected areas, aiming to prevent further soil degradation and ensure sustainable agricultural economic development and food security. Based on PlanetScope imagery, vegetation spectral indices and soil salinity indices were extracted, resulting in a total of 21 variables. These variables were used as input for the Bagging algorithm to construct a soil salinity prediction model, referred to as Model-Ⅰ. The max-relevance and min-redundancy (mRMR) method was employed to select relevant feature variables, which were then inputted into the Bagging algorithm to build a soil salinity prediction model, referred to as Model-Ⅱ. Field sampling data were used to assist in model building and validation. Model-Ⅰ and Model-Ⅱ were evaluated using model evaluation metrics. The results indicate that the prediction performance of Model-Ⅱ is better than that of Moedl-Ⅰ (mean R²=0.66, mean RMSE=18.00 dS·m^-1, mean PRIQ=3.21 for the validation set), and that mRMR effectively reduces the multidimensional feature redundancy. PlanetScope images combined with the mRMR method successfully mapped high-resolution soil salinity, which provided more detailed information on the spatial distribution of soil salinity, and the results of the study promoted the use of PlanetScope data to monitor soil salinity information.

Key words: PlanetScope, mRMR, Bagging, soil salinization, digital mapping techniques

LI Ke, DING Jianli, HAN Lijing, GE Xiangyu, GU Yongsheng, ZHOU Qian, LYU Yangxia. Digital mapping of soil salinization in a typical oasis based on PlanetScope images[J].Arid Land Geography, 2023, 46(8): 1291-1302.

Figures/Tables 12

Fig. 1

Tab. 1

Tab. 2

Spectral indices calculation based on PlanetScope images"

光谱指数	名称	公式	参考
土壤调节植被指数	SAVI	$S A V I = 1.5 × (N I R - R e d) / (N I R + R e d + 0.5)$	[37]
比值植被指数	RVI	$R V I = N I R / R e d$	[38]
增强植被指数	EVI	$E V I = 2.5 × (N I R - R e d) / (N I R + 6 × R e d - 7.5 × B l u e + 1)$	[39]
归一化差值植被指数	NDVI	$N D V I = (N I R - R e d) / (N I R + R e d)$	[40]
归一化差异盐度指数	NDSI	$N D S I = (R e d - N I R) / (R e d + N I R)$	[41]
亮度指数	BI	$B I = R e d 2 + N I R 2$	[41]
盐度指数	SI	$S I = B l u e × R e d$	[41]
盐度指数	SI1	$S I 1 = G r e e n × R e d$	[42]
盐度指数	SI2	$S I 2 = G r e e n 2 + R e d 2 + N I R 2$	[42]
盐度指数	SI3	$S I 3 = G r e e n 2 + R e d 2$	[42]
盐度指数	S1	$S 1 = B l u e / R e d$	[43]
盐度指数	S2	$S 2 = (B l u e - R e d) / (B l u e + R e d)$	[43]
盐度指数	S3	$S 3 = (G r e e n × R e d) / B l u e$	[43]
盐度指数	S4	$S 4 = B l u e × R e d$	[43]
盐度指数	S5	$S 5 = (B l u e × R e d) / G r e e n$	[43]
盐度指数	S6	$S 6 = (R e d × N I R) / G r e e n$	[43]

Tab. 2

Fig. 2

Tab. 3

Fig. 3

Tab. 4

Fig. 4

Fig. 5

Tab. 5

Fig. 6

Fig. 7

References 54

[1]	Hassani A, Azapagic A, Shokri N, et al. Predicting long-term dynamics of soil salinity and sodicity on a global scale[J]. Proceedings of the National Academy of Sciences, 2020, 117(52): 33017-33027. doi: 10.1073/pnas.2013771117
[2]	Schoups G, Hopmans J W, Young C A, et al. Sustainability of irrigated agriculture in the San Joaquin Valley, California[J]. Proceedings of the National Academy of Sciences, 2005, 102(43): 15352-15356. doi: 10.1073/pnas.0507723102
[3]	赵爽, 丁建丽, 韩礼敬, 等. 新疆典型盐渍土微波介电特性响应分析与建模[J]. 干旱区地理, 2022, 45(5): 1534-1546.
	[Zhao Shuang, Ding Jianli, Han Lijing, et al. Response analysis and modeling of microwave dielectric properties of typical saline soil in Xinjiang[J]. Arid Land Geography, 2022, 45(5): 1534-1546.]
[4]	赵其国, 周生路, 吴绍华, 等. 中国耕地资源变化及其可持续利用与保护对策[J]. 土壤学报, 2006, 43(4): 662-672.
	[Zhao Qiguo, Zhou Shenglu, Wu Shaohua, et al. Cultivated land resources and strategies for its sustainable utilization and protection in China[J]. Acta Pedologica Sinica, 2006, 43(4): 662-672.]
[5]	Zaman M, Shahid S A, Heng L, et al. Guideline for salinity assessment, mitigation and adaptation using nuclear and related techniques[M]. Cham: Springer Nature, 2018: 1-42.
[6]	丁建丽, 王飞. 干旱区大尺度土壤盐度信息环境建模——以新疆天山南北中低海拔冲积平原为例[J]. 地理学报, 2017, 72(1): 64-78. doi: 10.11821/dlxb201701006
	[Ding Jianli, Wang Fei. Environmental modeling of large-scale soil salinity information in an arid region: A case study of the low and middle altitude alluvial plain north and south of the Tianshan Mountains, Xinjiang[J]. Acta Geographica Sinica, 2017, 72(1): 64-78.] doi: 10.11821/dlxb201701006
[7]	杨琳, Fahmy Sherif, Hann Sheldon, 等. 基于土壤-环境关系的更新传统土壤图研究[J]. 土壤学报, 2010, 47(6): 1039-1049.
	[Yang Lin, Fahmy Sherif, Hann Sheldon, et al. Updating conventional soil maps using knowledge on soil-environment relationships extracted from the maps[J]. Acta Pedologica Sinica, 2010, 47(6): 1039-1049.]
[8]	Metternicht, Graciela, Alfred Zinck, et al. Remote sensing of soil salinization: Impact on land management[M]. Boca Raton: CRC Press, 2008: 4-56.
[9]	Howari F M, Goodell P C, MiyamotoI S. Spectral properties of salt crusts formed on saline soils[J]. Journal of Environmental Quality, 2002, 31(5): 1453-1461. pmid: 12371161
[10]	Mulder V L, De Bruin S, Schaepman M E, et al. The use of remote sensing in soil and terrain mapping: A review[J]. Geoderma, 2011, 162(1-2): 1-19. doi: 10.1016/j.geoderma.2010.12.018
[11]	Wang J, Ding J, Abulimiti A, et al. Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS-NIR) spectroscopy, Ebinur Lake Wetland, northwest China[J]. PeerJ, 2018, 6: e4703, doi: 10.7717/peerj.4703. doi: 10.7717/peerj.4703
[12]	Elmetwalli A M H, Tyler A N, Hunter P D, et al. Detecting and distinguishing moisture-and salinity-induced stress in wheat and maize through in situ spectroradiometry measurements[J]. Remote Sensing Letters, 2012, 3(4): 363-372. doi: 10.1080/01431161.2011.599346
[13]	Gorji T, Yildirim A, Hamzehpour N, et al. Soil salinity analysis of Urmia Lake Basin using Landsat-8 OLI and Sentinel-2A based spectral indices and electrical conductivity measurements[J]. Ecological Indicators, 2020, 112: 106173, doi: 10.1016/j.ecolind.2020.106173. doi: 10.1016/j.ecolind.2020.106173
[14]	Fernandez-Buces N, Siebe C, Cram S, et al. Mapping soil salinity using a combined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico[J]. Journal of Arid Environments, 2006, 65(4): 644-667. doi: 10.1016/j.jaridenv.2005.08.005
[15]	Ding J, Yu D. Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan-Kuqa Oasis, China, using remote sensing and electromagnetic induction instruments[J]. Geoderma, 2014, 235: 316-322.
[16]	Farifteh J, Van der Meer F, Atzberger C, et al. Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)[J]. Remote Sensing of Environment, 2007, 110(1): 59-78. doi: 10.1016/j.rse.2007.02.005
[17]	Grimm R, Behrens T, Märker M, et al. Soil organic carbon concentrations and stocks on Barro Colorado Island: Digital soil mapping using random forests analysis[J]. Geoderma, 2008, 146(1-2): 102-113. doi: 10.1016/j.geoderma.2008.05.008
[18]	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
[19]	Allbed A, Kumar L, Aldakheel Y Y, et al. Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region[J]. Geoderma, 2014, 230: 1-8.
[20]	Wang F, Yang S, Yang W, et al. Comparison of machine learning algorithms for soil salinity predictions in three dryland oases located in Xinjiang Uygur Autonomous Region (XJUAR) of China[J]. European Journal of Remote Sensing, 2019, 52(1): 256-276. doi: 10.1080/22797254.2019.1596756
[21]	Zhou T, Lu H L, Wang W W, et al. GA-SVM based feature selection and parameter optimization in hospitalization expense modeling[J]. Applied Soft Computing, 2019, 75: 323-332. doi: 10.1016/j.asoc.2018.11.001
[22]	Ding C, Peng H. Minimum redundancy feature selection from microarray gene expression data[J]. Journal of Bioinformatics and Computational Biology, 2005, 3(2): 185-205. doi: 10.1142/s0219720005001004 pmid: 15852500
[23]	Peng J, Biswas A, Jiang Q, et al. Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China[J]. Geoderma, 2019, 337: 1309-1319. doi: 10.1016/j.geoderma.2018.08.006
[24]	马慧琴, 黄文江, 景元书, 等. 基于 AdaBoost模型和mRMR算法的小麦白粉病遥感监测[J]. 农业工程学报, 2017, 33(5): 162-169.
	[Ma Huiqin, Huang Wenjiang, Jing Yuanshu, et al. Remote sensing monitoring of wheat powdery mildew based on AdaBoost model combining mRMR algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(5): 162-169.]
[25]	Schmid T, Koch M, Gumuzzio J, et al. Applications of hyperspectral imagery to soil salinity mapping[J]. Remote Sensing of Soil Salinization, 2008: 113-139.
[26]	Dwivedi R S, Kothapalli R V, Singh A N, et al. Generation of farm-level information on salt-affected soils using IKONOS-II multispectral data[J]. Remote Sensing of Soil Salinization, 2008: 73-90.
[27]	Bouaziz M, Matschullat J, Gloaguen R, et al. Improved remote sensing detection of soil salinity from a semi-arid climate in Northeast Brazil[J]. Comptes Rendus Geoscience, 2011, 343(11-12): 795-803. doi: 10.1016/j.crte.2011.09.003
[28]	Eldeiry A A, Garcia L A. Detecting soil salinity in alfalfa fields using spatial modeling and remote sensing[J]. Soil Science Society of America Journal, 2008, 72(1): 201-211. doi: 10.2136/sssaj2007.0013
[29]	Frazier A E, Hemingway B L. A technical review of planet smallsat data: Practical considerations for processing and using planetscope imagery[J]. Remote Sensing, 2021, 13(19): 3930, doi: 10.3390/rs13193930. doi: 10.3390/rs13193930
[30]	Roy D P, Wulder M A, Loveland T R, et al. Landsat-8: Science and product vision for terrestrial global change research[J]. Remote Sensing of Environment, 2014, 145: 154-172. doi: 10.1016/j.rse.2014.02.001
[31]	Drusch M, Del Bello U, Carlier S, et al. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services[J]. Remote Sensing of Environment, 2012, 120: 25-36. doi: 10.1016/j.rse.2011.11.026
[32]	杨劲松, 姚荣江, 王相平, 等. 中国盐渍土研究: 历程, 现状与展望[J]. 土壤学报, 2022, 59(1): 10-27.
	[Yang Jingsong, Yao Rongjiang, Wang Xiangping, et al. Research on salt-affected soils in China: History, status quo and prospect[J]. Acta Pedologica Sinica, 2022, 59(1): 10-27.]
[33]	Hardie M, Doyle R. Measuring soil salinity[J]. Plant Salt Tolerance, 2012: 415-425.
[34]	Aldeghi A, Carn S, Escobar-Wolf R, et al. Volcano monitoring from space using high-cadence planet CubeSat images applied to Fuego volcano, Guatemala[J]. Remote Sensing, 2019, 11(18): 2151, doi: 10.3390/rs11182151. doi: 10.3390/rs11182151
[35]	Xie B, Ding J, Ge X, et al. Estimation of soil organic carbon content in the Ebinur Lake wetland, Xinjiang, China, based on multisource remote sensing data and ensemble learning algorithms[J]. Sensors, 2022, 22(7): 2685, doi: 10.3390/s22072685. doi: 10.3390/s22072685
[36]	Gorji T, Sertel E, Tanik A, et al. Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey[J]. Ecological Indicators, 2017, 74: 384-391. doi: 10.1016/j.ecolind.2016.11.043
[37]	Huete A R. A soil-adjusted vegetation index (SAVI)[J]. Remote Sensing of Environment, 1988, 25(3): 295-309. doi: 10.1016/0034-4257(88)90106-X
[38]	Major D J, Baret F, Guyot G, et al. A ratio vegetation index adjusted for soil brightness[J]. International Journal of Remote Sensing, 1990, 11(5): 727-740. doi: 10.1080/01431169008955053
[39]	Liu H Q, Huete A. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(2): 457-465. doi: 10.1109/TGRS.36
[40]	Deering D W, Rouse J W, Haas R H, et al. Measuring forage production of grazing units from Landsat MSS data[J]. Remote Sensing of Environment, 1975(11): 1169-1178.
[41]	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
[42]	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
[43]	Abbas A, Khan S. Using remote sensing techniques for appraisal of irrigated soil salinity[C]//International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2007: 2632-2638.
[44]	Jing X, Zou Q, Yan J, et al. Remote sensing monitoring of winter wheat stripe rust based on mRMR-XGBoost algorithm[J]. Remote Sensing, 2022, 14(3): 756, doi: 10.3390/rs14030756. doi: 10.3390/rs14030756
[45]	Breiman L. Bagging predictors[J]. Machine Learning, 1996, 24(2): 123-140.
[46]	Scudiero E, Skaggs T H, Corwin D L. Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA[J]. Geoderma Regional, 2014, 2: 82-90.
[47]	Spies B, Woodgate P. Salinity mapping methods in the Australian context[M]. Canberra: Department of the Environment and Heritage, 2005: 18-45.
[48]	丁建丽, 姚远, 王飞, 等. 干旱区土壤盐渍化特征空间建模[J]. 生态学报, 2014, 34(16): 4620-4631.
	[Ding Jianli, Yao Yuan, Wang Fei, et al. Detecting soil salinization in arid regions using spectral feature space derived from remote sensing data[J]. Acta Ecologica Sinica, 2014, 34(16): 4620-4631.]
[49]	曹雷, 丁建丽, 玉米提·哈力克, 等. 基于国产高分一号卫星数据的区域土壤盐渍化信息提取与建模[J]. 土壤学报, 2016, 53(6): 1399-1409.
	[Cao Lei, Ding Jianli, Halik Umut, et al. Extraction and modeling of regional soil salinization based on data from GF-1 satellite[J]. Acta Pedologica Sinica, 2016, 53(6): 1399-1409.]
[50]	Rao B R M, Sharma R C, Ravi Sankar T, et al. Spectral behaviour of salt-affected soils[J]. International Journal of Remote Sensing, 1995, 16(12): 2125-2136. doi: 10.1080/01431169508954546
[51]	冯娟, 丁建丽, 杨爱霞, 等. 干旱区土壤盐渍化信息遥感建模[J]. 干旱地区农业研究, 2018, 36(1): 266-273.
	[Feng Juan, Ding Jianli, Yang Aixia, et al. Remote sensing modeling of soil salinization information in arid areas[J]. Agricultural Research in Arid Areas, 2018, 36(1): 266-273.]
[52]	Metternicht G I, Zinck J A. Remote sensing of soil salinity: Potentials and constraints[J]. Remote Sensing of Environment, 2003, 85(1): 1-20. doi: 10.1016/S0034-4257(02)00188-8
[53]	丁建丽, 伍漫春, 刘海霞, 等. 基于综合高光谱指数的区域土壤盐渍化监测研究[J]. 光谱学与光谱分析, 2012, 32(7): 1918-1922. pmid: 23016353
	[Ding Jianli, Wu Manchun, Liu Haixia, et al. Study on the soil salinization monitoring based on synthetical hyperspectral index[J]. Spectroscopy and Spectral Analysis, 2012, 32(7): 1918-1922.] pmid: 23016353
[54]	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.
	[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, 2020, 36(19): 124-131.]

波段	波段名	波长/nm
蓝波段	B2	465~515
绿波段Ⅰ	B3	513~549
绿波段II	B4	547~583
黄波段	B5	600~620
红波段	B6	650~680
红边波段	B7	697~713
近红外波段	B8	845~885

数据集	平均值/dS·m^-1	标准差/dS·m^-1	变异系数
训练集A	39.47	33.55	0.85
验证集A	34.86	32.60	0.93
训练集B	39.19	34.45	0.88
验证集B	35.51	30.48	0.86
训练集C	37.88	33.82	0.89
验证集C	38.42	32.21	0.84
全集	38.04	33.13	0.87

数据集	模型	训练集		验证集		RPIQ
数据集	模型	R²	RMSE/dS·m^-1	R²	RMSE/dS·m^-1	RPIQ
A	Model-Ⅰ	0.79	15.26	0.67	18.41	3.34
A	Model-Ⅱ	0.90	10.27	0.76	15.78	3.93
B	Model-Ⅰ	0.79	15.82	0.41	22.95	2.27
B	Model-Ⅱ	0.87	12.48	0.66	17.45	3.00
C	Model-Ⅰ	0.76	16.43	0.49	22.58	2.42
C	Model-Ⅱ	0.87	11.93	0.57	20.75	2.71
平均	Model-Ⅰ	0.78	15.84	0.52	21.32	2.68
平均	Model-Ⅱ	0.88	11.56	0.66	18.00	3.21

Digital mapping of soil salinization in a typical oasis based on PlanetScope images

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模型	模型方法	特征选择	特征变量类型
Model-Ⅰ	Bagging	-	波段反射率数据（B2、B4、B6、B7、B8）+光谱指数（SAVI、RVI、EVI、NDVI、NDSI、BI、SI、SI1、SI2、SI3、S1、S2、S3、S4、S5、S6）
Model-Ⅱ	Bagging	mRMR	波段反射率数据（B4）+光谱指数（EVI、S6、S1、SI2、NDSI、BI）

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[4]	ZHAO Qiaozhen,DING Jianli,HAN Lijing,JIN Xiaoye,HAO Jianping. Exploring the application of MODIS and Landsat spatiotemporal fusion images in soil salinization: A case of Ugan River-Kuqa River Delta Oasis [J]. Arid Land Geography, 2022, 45(4): 1155-1164.
[5]	WANG Shuang, DING Jian-li, WANG lu, NIU Zeng-yi. Remote sensing monitoring of soil salinization based on surface spectral modeling [J]. , 2016, 39(1): 190-198.
[6]	NIU Zeng-yi, DING Jian-li, LI Yan-hua, WANG Shuang, WANG Lu, MA Cheng-xia. Soil salinization information extraction method based on GF-1 image [J]. , 2016, 39(1): 171-181.