基于无人机遥感的滨海盐碱地土壤空间异质性分析与作物光谱指数响应胁迫诊断

doi:10.12082/dqxxkx.2021.200144

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (3): 536-549.doi: 10.12082/dqxxkx.2021.200144

基于无人机遥感的滨海盐碱地土壤空间异质性分析与作物光谱指数响应胁迫诊断

朱婉雪¹^,²(), 孙志刚¹^,²^,³^,⁴^,^*(), 李彬彬¹, 杨婷³, 刘振³, 彭金榜¹^,², 朱康莹¹^,², 李仕冀¹^,², 娄金勇¹^,³^,⁴, 侯瑞星¹, 李静¹, 于武江⁴, 王永利⁵, 张峰⁵, 刘向冶⁵, 胡华浪⁶, 欧阳竹²^,³^,⁴

1.中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室,北京100101
2.中国科学院大学资源与环境学院,北京100049
3.中国科学院地理科学与资源研究所中国科学院黄河三角洲现代农业工程实验室,北京 100101
4.中科山东东营地理研究院,东营257000
5.东营市现代农业示范区管理中心,东营 257000
6.农业农村部规划设计研究院农业遥感与数字乡村研究所农业农村部耕地利用遥感重点实验室,北京 100125

收稿日期:2020-03-26 修回日期:2020-06-11 出版日期:2021-03-25 发布日期:2021-05-25
通讯作者: *孙志刚（1976- ）,男,江苏盐城人,博士,研究员,研究方向为生态遥感与区域生态。E-mail: sun.zhigang@igsnrr.ac.cn
*孙志刚（1976- ）,男,江苏盐城人,博士,研究员,研究方向为生态遥感与区域生态。E-mail: sun.zhigang@igsnrr.ac.cn
作者简介:朱婉雪（1994- ）,女,四川乐山人,博士生,研究方向为无人机农业遥感应用。E-mail: zhuwx. 16b@igsnrr.ac.cn
基金资助:
中国科学院先导A专项子课题(XDA23050102);中国科学院重点部署项目(KFZD-SW-113);国家重点研发项目课题(2017YFC0503805)

Analysis of Spatial Heterogeneity for Soil Attributes and Spectral Indices-based Diagnosis of Coastal Saline-Alkaline Farmland Stress Using UAV Remote Sensing

ZHU Wanxue¹^,²(), SUN Zhigang¹^,²^,³^,⁴^,^*(), LI Binbin¹, YANG Ting³, LIU Zhen³, PENG Jinbang¹^,², ZHU Kangying¹^,², LI Shiji¹^,², LOU Jinyong¹^,³^,⁴, HOU Ruixing¹, LI Jing¹, YU Wujiang⁴, WANG Yongli⁵, ZHANG Feng⁵, LIU Xiangye⁵, HU Hualang⁶, OUYANG Zhu²^,³^,⁴

1. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3. Laboratory of Modern Agricultural Engineering in the Yellow River Delta, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
4. Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China
5. Management Center of Dongying Modern Agricultural demonstration Zone, Dongying, 257000, China
6. Key Laboratory of Cultivated Land Use/ Ministry of Agriculture and Rural Affairs, Academy of Agricultural Planning and Engineering, Beijing 100125, China

Received:2020-03-26 Revised:2020-06-11 Online:2021-03-25 Published:2021-05-25
Contact: SUN Zhigang
Supported by:
Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23050102);Key Projects of the Chinese Academy of Sciences(KFZD-SW-113);National Key Research and Development Program of China(2017YFC0503805)

摘要/Abstract

摘要：

在已集中连片改造为农田的盐碱地上,开展无人机遥感作物土壤空间异质性分析与光谱指数响应胁迫诊断对于提升盐碱地利用效率、创造更多经济效益与生态价值具有重要意义。本研究以山东省东营市黄河三角洲典型滨海盐碱地集中连片旱作农田的主要作物——高粱和玉米为研究对象,利用固定翼无人机获取400 hm²滨海盐碱地多光谱遥感数据,并结合地面195个采样点的3个土层（0~10 cm、10~20 cm、20~40 cm）的土壤属性数据,对该研究区域内作物生长的土壤环境因子进行空间异质性分析与光谱指数响应胁迫诊断。基于土壤属性数据,利用反距离加权插值法,绘制该研究区域内土壤盐分、pH、有机质、全氮和速效氮共5个指标含量的水平与垂直空间分布图。插值结果显示,5种土壤属性指标存在显著水平和垂直空间异质性。基于随机森林模型,采用递归特征消除法,结合土壤指标对光谱指数的重要性值,探讨影响作物生长的主要土壤环境胁迫因子。结果表明,5种土壤属性因子均会对玉米和高粱生长造成影响,但主要胁迫因子分别为土壤速效氮含量（10~20 cm）和3个土层的盐分含量。本研究为大面积农情胁迫监测提供了一项有效的地面与航空协同监测方案,为盐碱地旱作农田管理与决策提供了理论依据和技术支持。

关键词: 无人机遥感, 多光谱, 光谱指数, 土壤, 农作物, 盐碱地, 精准农业, 玉米, 高粱

Abstract:

Analysis of the spatial heterogeneity of soil attributes and diagnosis of the soil stress for crops cultivated at large-scale saline-alkali farmland based on remote sensing spectral indices are important to improve the land utilization efficiency and contribute to improve economic and ecological benefits. In this study, we conducted an Unmanned Aerial Vehicle (UAV) remote sensing observation and field measurement over a typical coastal saline-alkali farmland (400 hm²) in the Yellow River Delta of Dongying City, Shandong province in China during the growing season of maize and sorghum in 2019. An eBee wing-fixed UAV platform (SenseFly, Cheseaux-Lausanne, Switzerland) equipped with a multiSPEC-4C multispectral camera (SenseFly, Cheseaux-Lausanne, Switzerland) was used to capture the spectral information of crops. Nine Vegetation Indices (VIs) were selected to characterize the growth status of crops. Among the nine VIs, MCARI, TCARI/OSAVI, and NDREI were sensitive to Leaf Chlorophyll Content (LCC); OSVAI, GNDVI, and MSR were sensitive to Above-Ground Biomass (AGB); and NDVI, EVI2, and MSR_RE were sensitive to Leaf Area Index (LAI). Soil sampling (n = 195) at three layers (0~10 cm, 10~20 cm, and 20~40 cm) were implemented evenly across the study area. In total, five soil attributes were measured, including soil salinity (SALT, g/kg), pH, organic matter content (C, g/kg), total nitrogen content (N, g/kg), and available nitrogen content (SN, mg/kg). In our study, we first conducted an interpolation method using Inverse Distance Weighted (IDW) to map the spatial heterogeneity of soil attributes. Our interpolation results show that all the soil attributes showed obvious horizontal spatial heterogeneity, while pH and SALT showed remarkable vertical spatial heterogeneity. Second, we conducted the Pearson Correlation Analysis (PCA) between different soil attributes at each soil layer. The results of PCA showed that SALT and pH had a significantly negative correlation, and these two attributes were not related to SN, N, and C. While SN, N, and C had significantly positive relationships with each other. Finally, the influences of soil attributes on the growth status of maize and sorghum were assessed separately using the Recursive Feature Elimination (RFE) method along with the random forest model based on 3-fold cross validation and 100 times iteration. According to the importance values of soil attributes to VIs, the influence of soil attributes on crop growth from high to low was that SN>N, C>pH>SALT for maize, and SALT>pH>SN, N, C for sorghum. However, the dominant soil attributes that influenced crop growth were SN2 (i.e., SN at 10~20 cm soil layer) and SALT at 0~40 cm soil layer for maize and sorghum, respectively. This study proposes a 'soil-crop growth-VIs' framework for monitoring crop growth status based combining field sampling and UAV remote sensing observations, which is essential for agronomic management in saline-alkali land and contributes to the development of precision agriculture.

Key words: UAV remote sensing, multispectral, vegetation index, soil, crop, saline-alkaline land, precision agriculture, maize, sorghum

朱婉雪, 孙志刚, 李彬彬, 杨婷, 刘振, 彭金榜, 朱康莹, 李仕冀, 娄金勇, 侯瑞星, 李静, 于武江, 王永利, 张峰, 刘向冶, 胡华浪, 欧阳竹. 基于无人机遥感的滨海盐碱地土壤空间异质性分析与作物光谱指数响应胁迫诊断[J]. 地球信息科学学报, 2021, 23(3): 536-549.DOI:10.12082/dqxxkx.2021.200144

ZHU Wanxue, SUN Zhigang, LI Binbin, YANG Ting, LIU Zhen, PENG Jinbang, ZHU Kangying, LI Shiji, LOU Jinyong, HOU Ruixing, LI Jing, YU Wujiang, WANG Yongli, ZHANG Feng, LIU Xiangye, HU Hualang, OUYANG Zhu. Analysis of Spatial Heterogeneity for Soil Attributes and Spectral Indices-based Diagnosis of Coastal Saline-Alkaline Farmland Stress Using UAV Remote Sensing[J]. Journal of Geo-information Science, 2021, 23(3): 536-549.DOI:10.12082/dqxxkx.2021.200144

图/表 9

图1

图2

图3

图 4

表1

研究选用的光谱指数及计算方法

作物参数	光谱指数及公式	公式编号	参考文献
LCC	$MCARI = 1.5 2.5 NIR - R - 1.3 NIR - G (2 NIR + 1) 2 - 6 NIR - 5 R - 0.5$	（1）	[24]
	$TCARI / OSAVI = 3 [E - R - 0.2 (E - G) (E / R)] / [1.16 (NIR - R) / (NIR + R + 0.16)]$	（2）	[25]
	$NDREI = (NIR - E) / (NIR + E)$	（3）	[26]
AGB	$OSAVI = (NIR - R) / (NIR + R + x) (x = 0.16)$	（4）	[22]
	$GNDVI = (NIR - G) / (NIR + G)$	（5）
	$MSR = (NIR / R - 1) (NIR / R + 1)$	（6）
LAI	$NDVI = (NIR - R) / (NIR + R)$	（7）	[23]
	$EVI 2 = 2.5 (NIR - R) / (NIR + 2.4 R + 1)$	（8）
	$MS R RE = (NIR / E - 1) (NIR / E + 1)$	（9）

表1

图5

图6

图7

图 8

参考文献 35

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基于无人机遥感的滨海盐碱地土壤空间异质性分析与作物光谱指数响应胁迫诊断

Analysis of Spatial Heterogeneity for Soil Attributes and Spectral Indices-based Diagnosis of Coastal Saline-Alkaline Farmland Stress Using UAV Remote Sensing

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