基于多光谱影像的阿拉尔垦区棉田土壤盐分反演

doi:10.13866/j.azr.2024.05.16

干旱区研究 ›› 2024, Vol. 41 ›› Issue (5): 894-904.doi: 10.13866/j.azr.2024.05.16 cstr: 32277.14.AZR.20240516

• 农业生态 • 上一篇

基于多光谱影像的阿拉尔垦区棉田土壤盐分反演

洪国军¹^,²^,³(), 谢俊博⁴, 张灵¹, 范振岐²^,³, 喻彩丽⁵, 付仙兵¹, 李旭²^,³()

1.江西科技学院江西省区域发展研究院，江西南昌 330200
2.塔里木大学信息工程学院，新疆阿拉尔 843300
3.塔里木大学塔里木绿洲农业教育部重点实验室，新疆阿拉尔 843300
4.塔里木大学水利与建筑工程学院，新疆阿拉尔 843300
5.汕尾职业技术学院海洋学院，广东汕尾 516600

收稿日期:2023-10-26 修回日期:2023-12-27 出版日期:2024-05-15 发布日期:2024-05-29
通讯作者: 李旭. E-mail: lixu2866@126.com
作者简介:洪国军（1995-），男，硕士研究生，主要从事空间大数据管理与智慧农业研究. E-mail: hgj950603@163.com
基金资助:
国家自然基金地区基金(61662064);国家自然基金地区基金(42061046)

Monitoring soil salinization of cotton fields in the Aral Reclamation Area using multispectral imaging

HONG Guojun¹^,²^,³(), XIE Junbo⁴, ZHANG Ling¹, FAN Zhenqi²^,³, YU Caili⁵, FU Xianbing¹, LI Xu²^,³()

1. Jiangxi University of Science and Technology Jiangxi Provincial Institute of Regional Development, Nanchang 330200, Jiangxi, China
2. School of Information Engineering, Tarim University, Alar 843300, Xinjiang, China
3. Key Laboratory of Tarim Oasis Agricultural Ministry of Education, Tarim University, Alar 843300, Xinjiang, China
4. College of Water Resources and Architectural Engineering, Tarim University, Alar 843300, Xinjiang, China
5. College of Ocean of Shanwei Institute of Technology, Shanwei 516600, Guangdong, China

Received:2023-10-26 Revised:2023-12-27 Published:2024-05-15 Online:2024-05-29

摘要/Abstract

摘要：

针对新疆土壤盐分信息获取困难，无法快速、准确的大范围评估土壤盐渍化情况，本研究以新疆阿拉尔垦区的棉田为研究对象，利用Sentinel-2 SR和Landsat-9 OLI的多光谱遥感影像数据，采用穷举特征组合和交叉验证方法，从20个光谱指数和组合光谱指数构建的高维数据集中筛选出最优特征子集，并比较四种机器学习模型（即XGBoost、随机森林、深度神经网络和K-近邻）在不同特征组合下的土壤盐渍化反演精度，同时分析Sentinel-2 SR和Landsat-9 OLI遥感影像在土壤盐渍化反演中的精度差异。研究结果表明：（1）基于XGBoost算法构建的模型能够实现棉田盐渍化高精度预测，不同特征组合的R²均高于0.74，MSE均低于0.04，MAPE低于0.13。（2）在特征组合1条件下，Sentinel-2 SR（S3+GBNDVI）与Landsat-9 OLI（SI+NDVI）遥感影像使用XGBoost算法均获得了最高预测精度。（3） Sentinel-2 SR影像数据在棉田盐渍化预测中的精度（R²=0.73~0.88）优于Landsat-9 OLI影像数据。本研究实现了新疆阿拉尔垦区棉田土壤盐渍化精准监测，为垦区棉田土壤盐渍化治理和防治提供有效的技术参考。

关键词: 多光谱遥感影像, 光谱指数, 土壤盐渍化, 机器学习模型, 阿拉尔垦区

Abstract:

Given the difficulties in the field measurement of soil salinization in Xinjiang and the difficulty in quickly and broadly evaluating the potential hazards of soil salinization, this study considers cotton fields in the Aral Reclamation Area of Xinjiang as the research object, and uses multispectral remote sensing image data from Sentinel-2 SR and Landsat-9 OLI to construct a high-dimensional data set by comprehensively integrating 20 spectral indices and combining spectral indices. The optimal feature subset is screened using the method of exhaustive feature combination and cross-validation, and the inversion accuracy of soil salinization is compared for four machine learning algorithms (i.e., XGBoost, random forest, deep neural network, and K-nearest neighbor) under different feature combinations. Simultaneously, the difference in accuracy between Sentinel-2 SR and Landsat-9 OLI remote sensing images in soil salinization inversion is analyzed. The results show that: (1) The model constructed based on XGBoost algorithm can achieve high-precision prediction of cotton field salinization, with R² higher than 0.74, MSE lower than 0.04, and MAPE lower than 0.13. (2) Under the condition of feature combination 1, Sentinel-2 SR (S3+GBNDVI) and Landsat-9 OLI (SI+NDVI) remote sensing images achieved the highest prediction accuracy using XGBoost algorithm. (3) Sentinel-2 SR image data in cotton field salinization prediction (R²=0.73-0.88) is better than that of Landsat-9 OLI image data. This study realizes the precise monitoring of soil salinization in cotton fields in the Aral Reclamation Area of Xinjiang, which should provide an effective and timely technical reference for soil salinization control and prevention in cotton fields in reclamation areas.

Key words: multispectral remote sensing images, spectral indices, soil salinization, machine learning algorithms, Aral Reclamation Area

洪国军, 谢俊博, 张灵, 范振岐, 喻彩丽, 付仙兵, 李旭. 基于多光谱影像的阿拉尔垦区棉田土壤盐分反演[J]. 干旱区研究, 2024, 41(5): 894-904.

HONG Guojun, XIE Junbo, ZHANG Ling, FAN Zhenqi, YU Caili, FU Xianbing, LI Xu. Monitoring soil salinization of cotton fields in the Aral Reclamation Area using multispectral imaging[J]. Arid Zone Research, 2024, 41(5): 894-904.

图/表 11

图1

图2

表1

光谱指数的表达式"

光谱指数	表达式	参考文献
SI	$b 2 × b 4$	[15]
SI₁	$b 3 × b 4$	[16]
SI₂	$[(b 2) 2 + (b 3) 2 + (b 8) 2] 2$	[17]
SI₃	$(b 2) 2 + (b 3) 2$	[18]
S₁	$b 2 / b 4$	[19]
S₂	$(b 2 - b 4) / (b 2 + b 4)$	[20]
S₃	$(b 3 × b 4) / b 2$	[19-20]
S₅	$(b 2 × b 4) / b 3$	[19-20]
CRSI	$[(b 8 × b 4) - (b 4 × b 3)] / [(b 8 × b 4) + (b 4 × b 3)]$	[19-20]
MSAVI	$(2 × b 8 - 1) - [(2 × b 8 + 1) 2 - 8 × (b 8 - b 4)] / 2$	[21-22]
NDVI	$(b 8 - b 4) / (b 8 + b 4)$	[23⇓⇓⇓⇓⇓⇓-30]
RVI	$b 8 / b 4$	[26]
DVI	$b 8 - b 4$	[27]
GNDVI	$(b 8 - b 3) / (b 8 + b 3)$	[27⇓-29]
EVI	$(1.5 × b 8 - 1.5 × b 4) / (b 8 + 6 × b 4 - 7.5 × b 2 + 1)$	[30]
OSAVI	$(b 8 - b 4) / (b 8 + b 4 + 0.16)$	[31]
SAVI	$(b 8 - b 4) × 1.5 / (b 8 + b 4 + 0.5)$	[31-32]
NDWI	$(b 8 - b 11) / (b 8 + b 11)$	[33]
GBNDVI	$(b 8 - b 3 - b 2) / (b 8 + b 3 + b 2)$	[34]
GRNDVI	$(b 8 - b 3 - b 4) / (b 8 + b 3 + b 4)$	[27]

表1

表2

表3

表4

图3

图4

图5

图6

图7

参考文献 36

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遥感影像	特征选择	相关性（R²）	组合
Sentinel-2 SR	S3, GBNDVI	0.93	组合1
	S3, GBNDVI, MSAVI, SI1	0.91	组合2
	S3, EVI, GBNDVI, MSAVI	0.90	组合3
Landsat-9 OLI	SI, NDVI	0.87	组合1
	SI, SI3, NDVI	0.85	组合2
	SI, NDVI, GRNDVI	0.84	组合3

数据源	模型	机器学习模型评价指标	组合1	组合2	组合3
Sentinel-2 SR	KNN	R²	0.80	0.78	0.78
		MSE	0.04	0.04	0.04
		MAPE	0.15	0.16	0.16
	RF	R²	0.90	0.87	0.87
		MSE	0.02	0.02	0.03
		MAPE	0.10	0.10	0.11
	DNN	R²	0.85	0.84	0.82
		MSE	0.03	0.03	0.03
		MAPE	0.12	0.14	0.16
	XGB	R²	0.92	0.90	0.88
		MSE	0.02	0.02	0.02
		MAPE	0.09	0.09	0.010

数据源	模型	机器学习模型评价指标	组合1	组合2	组合3
Landsat-9 OLI	KNN	R²	0.77	0.76	0.72
		MSE	0.04	0.04	0.05
		MAPE	0.15	0.15	0.16
	RF	R²	0.82	0.81	0.78
		MSE	0.03	0.04	0.04
		MAPE	0.11	0.12	0.11
	DNN	R²	0.81	0.80	0.76
		MSE	0.04	0.04	0.05
		MAPE	0.14	0.15	0.16
	XGB	R²	0.85	0.84	0.82
		MSE	0.03	0.03	0.04
		MAPE	0.10	0.11	0.11

基于多光谱影像的阿拉尔垦区棉田土壤盐分反演

Monitoring soil salinization of cotton fields in the Aral Reclamation Area using multispectral imaging

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