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

干旱区地理 ›› 2023, Vol. 46 ›› Issue (8): 1291-1302.doi: 10.12118/j.issn.1000-6060.2022.496

基于PlanetScope影像的典型绿洲土壤盐渍化数字制图

李科^1,²(),丁建丽^1,²(),韩礼敬^1,²,葛翔宇^1,²,顾永昇^1,²,周倩^1,²,吕阳霞³

1.新疆大学地理与遥感科学学院/智慧城市与环境建模自治区普通高校重点实验室，新疆乌鲁木齐 830046
2.新疆大学绿洲生态教育部重点实验室，新疆乌鲁木齐 830046
3.新疆阿克苏地区渭干河流域管理局，新疆阿克苏 842000

收稿日期:2022-09-30 修回日期:2022-10-29 出版日期:2023-08-25 发布日期:2023-09-21
通讯作者: 丁建丽（1974-），男，博士，教授，主要从事干旱区遥感与GIS应用研究. E-mail: watarid@xju.edu.cn
作者简介:李科（1996-），男，硕士研究生，主要从事干旱区遥感与GIS应用研究. E-mail: dixinlike@stu.xju.edu.com
基金资助:
国家自然科学基金项目(41961059);新疆维吾尔自治区自然科学基金重点项目(2021D01D06);新疆维吾尔自治区研究生科研创新项目(XJ2022G002)

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

LI Ke^1,²(),DING Jianli^1,²(),HAN Lijing^1,²,GE Xiangyu^1,²,GU Yongsheng^1,²,ZHOU Qian^1,²,LYU Yangxia³

1. Xinjiang Common University Key Lab of Smart City and Environment Stimulation, College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, Xinjiang, China
2. Key Laboratory of Oasis Ecology under Ministry of Education, Xinjiang University, Urumqi 830046, Xinjiang, China
3. Groundwater and Salt Monitoring Station, Ogan River Basin Management Office, Aksu 842000, Xinjiang, China

Received:2022-09-30 Revised:2022-10-29 Online:2023-08-25 Published:2023-09-21

摘要/Abstract

摘要：

干旱半干旱地区急需高分辨率的土壤盐度图用于显示盐度空间分布的细微变化，指导盐渍化区域和潜在盐渍化区域制定土地资源管理政策和水资源管理政策，防止土壤进一步退化，保障农业经济可持续发展和粮食安全生产。基于PlanetScope影像，提取植被光谱指数和土壤盐度指数，共计21个变量，将其输入装袋回归（Bootstrap aggregating，Bagging）算法中，构建了土壤盐度预测模型Model-Ⅰ；使用最相关最小冗余（Max-relevance and min-redundancy，mRMR）方法筛选特征变量，将其输入Bagging中，构建了土壤盐度预测模型Model-Ⅱ，使用野外采样数据来辅助建模并进行验证。通过模型评价指标对Model-Ⅰ和Model-Ⅱ进行评估。结果表明：Model-Ⅱ的预测性能优于Model-Ⅰ（验证集决定系数为0.66，均方根误差为18.00 dS·m^-1，四分位数的相对预测误差为3.21），mRMR有效降低了多维特征冗余问题。PlanetScope影像结合mRMR方法成功绘制了高分辨率土壤盐度图，提供了更详细的土壤盐度空间分布信息，研究结果对利用PlanetScope数据监测土壤盐渍化信息起推动作用。

关键词: PlanetScope, mRMR, Bagging, 土壤盐渍化, 数字制图技术

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

李科, 丁建丽, 韩礼敬, 葛翔宇, 顾永昇, 周倩, 吕阳霞. 基于PlanetScope影像的典型绿洲土壤盐渍化数字制图[J]. 干旱区地理, 2023, 46(8): 1291-1302.

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.

图/表 12

图1

表1

表2

基于PlanetScope影像光谱指数计算"

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]

表2

图2

表3

图3

表4

图4

图5

表5

图6

图7

参考文献 54

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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

基于PlanetScope影像的典型绿洲土壤盐渍化数字制图

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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