Remote sensing‐based retrieval of soil moisture content using stacking ensemble learning models

含水量 随机森林 环境科学 遥感 机器学习 计算机科学 雷达 集成学习 Lasso(编程语言) 堆积 人工智能 地质学 岩土工程 物理 万维网 电信 核磁共振
作者
Sinan Wang,Yingjie Wu,Ruiping Li,Xiuqing Wang
出处
期刊:Land Degradation & Development [Wiley]
卷期号:34 (3): 911-925 被引量:8
标识
DOI:10.1002/ldr.4505
摘要

Abstract Machine learning combined with multisource remote sensing data to assess soil moisture content (SMC) has attracted considerable attention in SMC studies, but the retrieval results still remain uncertain. The purpose of this study is to combine multiple single machine learning models with integrated learning algorithms and propose an SMC retrieval method based on multiple differentiated models under a stacking integrated learning architecture. First, 19 factors, including: radar backscattering coefficient, vegetation index, and drought index, that affect SMC were extracted from SENTINEL‐1, LANDSAT, and terrain factors. Those with the highest importance scores were selected as retrieval factors using the Boruta algorithm combined with four single machine learning methods—classified regression tree, random forest, gradient boosting decision tree (GBDT), and extreme random tree. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and the generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that radar backscattering coefficient, temperature, vegetation drought index, land surface temperature, enhanced vegetation index, and solar local incident angle were the most important environmental variables for soil moisture retrieval. A comparison of the four machine learning methods in April and August showed that the GBDT model revealed the highest SMC retrieval accuracy, with root mean square error values of 1.87% and 1.64%, respectively. The stacking models were more accurate than the optimal single machine learning model, especially when using GBM. The multifactor integrated model constructed using spectral indices, radar backscatter coefficients, and topographic data exhibited high accuracy in soil surface moisture retrieval in an arid zone, providing a reference for land desertification studies and ecological environment management in the study region.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
YZQ发布了新的文献求助10
1秒前
黑咖啡完成签到,获得积分10
1秒前
Liufgui应助可靠的如之采纳,获得10
3秒前
科研通AI2S应助阿俊采纳,获得10
4秒前
5秒前
7秒前
9秒前
9秒前
JamesPei应助YZQ采纳,获得10
10秒前
Orange应助邪恶花生米采纳,获得10
10秒前
weijie发布了新的文献求助10
10秒前
hf完成签到,获得积分10
10秒前
10秒前
12秒前
量子星尘发布了新的文献求助30
13秒前
硅负极完成签到,获得积分10
13秒前
zzt发布了新的文献求助10
13秒前
14秒前
Dr.Yang发布了新的文献求助10
15秒前
17秒前
刻苦的秋柔完成签到,获得积分10
19秒前
意大利种马完成签到,获得积分20
20秒前
orixero应助写得出发的中采纳,获得10
22秒前
刘雨森完成签到 ,获得积分10
23秒前
坦率白萱应助littleblack采纳,获得10
24秒前
香蕉觅云应助意大利种马采纳,获得10
25秒前
ZS完成签到,获得积分10
25秒前
帅哥的事情少管完成签到,获得积分10
26秒前
littlestone完成签到,获得积分10
27秒前
NexusExplorer应助ShuXU采纳,获得10
29秒前
果果完成签到,获得积分10
29秒前
项绝义完成签到,获得积分10
30秒前
30秒前
空古悠浪发布了新的文献求助20
30秒前
30秒前
30秒前
32秒前
所所应助Richard采纳,获得10
32秒前
热心市民小红花应助哈哈采纳,获得50
32秒前
33秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988920
求助须知:如何正确求助?哪些是违规求助? 3531290
关于积分的说明 11253247
捐赠科研通 3269903
什么是DOI,文献DOI怎么找? 1804830
邀请新用户注册赠送积分活动 882027
科研通“疑难数据库(出版商)”最低求助积分说明 809052