基于随机森林算法的土壤含盐量预测

doi:10.13866/j.azr.2023.08.06

Abstract

Abstract:

Soil salinization caused by natural and anthropogenic factors is an environmental hazard that is especially important in arid and semi-arid regions of the world. The accumulation of salts in soil is a major threat to crop production and global agriculture. Therefore, the rapid and precise detection of salt-affected lands is highly critical for sustaining soil productivity. This paper aims to analyze the performance of the random forest algorithm in mapping soil salinity in the Yinchuan Plain using Landsat-8 OLI, Sentinel-2A satellite images, and ground-based soil salt content (SSC) measurements with the aid of the Google Earth Engine (GEE) platform. We estimated SSC by establishing the relationship between spectral index characteristics and ground-measured soil salt content. The results show that GEE can provide reliable data support for soil salinity prediction. The random forest model established with Sentinel-2A as the data source performed better (R² = 0.789, RMSE = 1.487) than and can therefore be used for the estimation of soil salinity using high-resolution remote sensing, which can provide theoretical support for large-scale soil salinity monitoring.

Key words: soil salt content, Google Earth Engine, random forest, predict, Yinchuan Plain

LI Xiaoyu, JIA Keli, WEI Huimin, CHEN Ruihua, WANG Yijing. Prediction of soil salt content based on the random forest algorithm[J].Arid Zone Research, 2023, 40(8): 1258-1267.

Figures/Tables 8

Fig. 1

Tab. 1

The calculation formula of spectral indexes"

光谱指数	计算公式	参考文献
盐分指数（SI_T）	(Red-NIR)×100	[35]
盐分指数1（SI1）	$G r e e n × R e d$	[36]
盐分指数2（SI2）	$G r e e n 2 + R e d 2 + N I R 2$	[36]
盐分指数3（SI3）	$G r e e n 2 + R e d 2$	[37]
盐分指数4（SI4）	(Blue×Red)/Green	[38]
盐分指数5（SI5）	(Green+Red)/2	[39]
盐渍化指数1（S1）	Blue/Red	[38]
盐渍化指数2（S2）	(Blue-Red)/(Blue+Red)	[38]
盐渍化指数3（S3）	(Green×Red)/Blue	[38]
盐度比值指数（SAIO）	(Red-NIR)/(Green+NIR)	[40]
土壤调节植被指数（SAVI）	[(NIR-Red)×(1+L)]/(NIR+Red+L)；L=0.5	[41]
绿度差值植被指数（GDVI）	(NIR²-Red²)/(NIR²+Red²)	[42]
冠层响应盐度指数（CRSI）	$[(N I R × R e d) – (G r e e n × B l u e)] / [(N I R × R e d) + (G r e e n × B l u e)]$	[43]

Tab. 1

Tab. 2

Fig. 2

Tab. 3

Fig. 3

Fig. 4

Tab. 4

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盐渍化等级	含盐量/(g·kg^-1)	样本数量	含盐量均值/(g·kg^-1)	含盐量最大值/(g·kg^-1)	含盐量最小值/(g·kg^-1)	变异系数/%
非盐渍化	<1	91	0.56	0.98	0.09	39.79
轻度盐渍化	1~2	64	1.46	1.96	1.00	20.09
中度盐渍化	2~4	55	2.82	3.97	2.00	19.42
重度盐渍化	4~6	37	4.90	5.91	4.06	11.97
盐土	>6	50	12.00	20.74	6.14	35.10
总样本		297	3.63	20.74	0.09	138.76

数据源	建模集（n=198）		验证集（n=99）
数据源	R²	RMSE	R²	RSME
Landsat8 OLI	0.615	3.169	0.734	1.698
Sentinel-2A	0.633	2.875	0.789	1.487

盐渍化等级	像元数	面积/km²	占比/%
非盐渍化	29115098	2916.701	41.620
轻度盐渍化	23348676	2332.925	33.290
中度盐渍化	10886719	1086.069	15.498
重度盐渍化	5121100	511.611	7.301
盐土	1609153	160.567	2.291

Prediction of soil salt content based on the random forest algorithm

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