基于稀疏样点的南方丘陵地区耕地土壤有效磷制图

曹佳萍; 张黎明; 邱龙霞; 邢世和; 马丹

doi:10.12357/cjea.20210565

基于稀疏样点的南方丘陵地区耕地土壤有效磷制图

doi: 10.12357/cjea.20210565

福建农林大学资源与环境学院/土壤生态系统健康与调控福建省高校重点实验室　福州　350002

基金项目: 国家自然科学基金项目(41971050)资助

详细信息

作者简介:
曹佳萍, 主要研究方向为土壤属性制图。E-mail: 1361940269@qq.com

通讯作者:
马丹, 主要研究方向为遥感技术及其多学科交叉应用。E-mail: madam_yurou@163.com

中图分类号: S158.9
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出版历程
- 收稿日期: 2021-08-23
- 录用日期: 2021-09-26
- 网络出版日期: 2021-11-10
- 刊出日期: 2022-02-08

Mapping soil available phosphorus of cultivated land in hilly region of southern China based on sparse samples

College of Resources and Environment, Fujian Agriculture and Forestry University / University Key Laboratory of Soil Ecosystem Health and Regulation in Fujian, Fuzhou 350002, China

Funds: This study was founded by the National Natural Science Foundation of China (41971050)

More Information

Corresponding author: E-mail: madam_yurou@163.com

摘要

摘要: 绘制耕地表层土壤有效磷空间分布图对精准农业管理和土壤环境评估具有重要意义。目前土壤磷数字制图研究大多面向充足土壤样点的平坦地区, 基于稀疏样点的南方丘陵地区耕地土壤有效磷制图效果尚不清楚。本文以典型南方丘陵地区福建省建瓯市为研究对象, 基于96个稀疏土壤实测样点, 利用空间分辨率为10 m的Sentinel-2遥感影像获取的遥感变量, 联合气象变量和地形变量建立随机森林(Random Forest, RF)模型预测建瓯市耕地表层土壤(0~20 cm)有效磷含量, 并对比5种不同环境变量组合下的RF模型精度。结果表明, 加入遥感变量后, 地形、气象和pH组合的RF模型预测有效磷含量的精度显著提升[决定系数(R²)从0.36提升至0.59], 联合全部变量(遥感、地形、气象和土壤pH)的RF模型预测精度最佳。遥感变量、气象变量、地形变量和土壤pH分别可以解释土壤有效磷含量的22.87%、30.64%、30.38%和16.11%, 其中年均温、pH、地形湿度指数和高程是影响南方丘陵地区耕地土壤有效磷空间分布的主导因素。因此, 利用遥感、气象、地形和土壤pH组合的RF模型是样点数量有限情况下预测南方丘陵地区县市域耕地土壤有效磷含量的有效方法。
- 南方丘陵地区 /
- 土壤有效磷 /
- 遥感变量 /
- 环境变量 /
- 随机森林 /
- 数字土壤制图 /
- Sentinel-2影像
Abstract: Spatial distribution mapping of topsoil available phosphorus content of cultivated land is essential for precise agricultural management and soil environmental assessment. Most research has focused on sufficient soil samples to map topsoil phosphorus content of cultivated land in flat areas. However, there are few studies on soil available phosphorus mapping based on sparse samples in the hilly areas of southern China. Jian’ou City was selected as the study area, which is a hilly area in southern China and has the largest cultivated land area among all county-level cities in Fujian Province. A total of 96 soil measurements, Sentinel-2 remote sensing data with a spatial resolution of 10 m, and climate and topographical variables were used to predict topsoil (0–20 cm) available phosphorus content. Random forest models with five combinations of environmental variables were constructed and their performance was compared for model prediction. Three assessment criteria, namely the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate the performance of random forest models for five combinations of environmental variables. The results showed that the prediction accuracy of the random forest model using climate variables, topographic variables, and soil pH values significantly improved after adding remote sensing variables, with an R² increase from 0.36 to 0.59 and an RMSE decrease of 20.34%. In addition, the random forest model using all variables (remote sensing, topography, climate, and soil pH) obtained the optimal performance (R²= 0.59, MAE = 19.04 mg∙kg⁻¹, RMSE = 25.26 mg∙kg⁻¹) among five combinations of environmental variables. Therefore, remote sensing variables are of great value for the mapping of soil available phosphorus based on sparse samples, and we suggested that the use of remote sensing variables should be increased in future studies to improve prediction accuracy. Remote sensing variables, climate variables, topographic variables, and soil pH could explain 22.87%, 30.64%, 30.38%, and 16.11% of the topsoil available phosphorus content, respectively. Furthermore, the spatial distribution of soil available phosphorus content in the study area was found to be mainly affected by the mean annual temperature, soil pH, soil moisture index, and elevation. The spatial distribution maps of soil available phosphorus content by the five random forest models were similar. High values of topsoil available phosphorus content were distributed in the central and western regions, whereas low values were distributed in the eastern and southern regions. The spatial variation of the soil available phosphorus in the distribution map produced by the optimal random forest model with total environmental variables was the most precise. Therefore, a random forest model that uses all variables (i.e., soil pH, topographic, remote sensing, and climate) can be used as a robust method to resolve soil available phosphorus content mapping with sparse soil samples in the hilly regions of southern China. Thus, this research can provide some guidance for other researchers interested in mapping the soil available phosphorus content in the hilly regions of China.
- Hilly region of southern China /
- Soil available phosphorus /
- Remote sensing variables /
- Environment variables /
- Random forest /
- Digital soil mapping /
- Sentinel-2 images

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图 1 研究区范围及土壤采样点分布图

Figure 1. Map of the study area and soil sampling points distribution in the study area

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图 2 建瓯市行政区划及耕地管理单元

Figure 2. Administrative division and cultivated land management unit of Jian’ou City, Fujian Province, China

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图 3 全部环境变量下环境因子对预测土壤有效磷空间分布的相对重要性

图中MAT为年平均气温, TWI为地形湿度指数, DEM为高程, MAP为年平均降水量, EVI为增强植被指数, PCA1为第一主成分, B6为红边波段(第6波段)。In the figure, MAT is mean annual temperature; TWI is topographic wetness index; DEM is digital elevation model; MAP is mean annual precipitation; EVI is enhanced vegetation index; PCA1 is first principal component; B6 is red edge (Band 6).

Figure 3. Relative importance of environmental factors for estimation of spatial distribution of soil available phosphorus content under total environmental variables

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图 4 建瓯市耕地土壤有效磷含量空间分布预测图

图a、b、c、d和e分别为模型A、B、C、D和E的空间预测结果, 图f为模型A与模型E预测结果的差值。Fig. a, b, c, d and e are the prediction results of model A, model B, model C, model D and model E, respectively. Fig. f is the difference between the prediction results of model A and model E.

Figure 4. Prediction resultes of spatial distribution of soil available phosphorus (SAP) content of surface soil (0−20 cm) of cultivated land in Jian’ou City, Fujian Province, China

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表 1 土壤有效磷建模辅助变量

Table 1. Auxiliary variables of soil available phosphorus modeling

变量类别 Category of variable	指标 Index
土壤属性 Soil attribute (P)	pH
气象变量 Climate variables (C)	年平均降水量 Mean annual precipitation (MAP)
气象变量 Climate variables (C)	年平均气温 Mean annual temperature (MAT)
地形变量 Topographical variables (T)	数字高程模型 Digital elevation model (DEM)
地形变量 Topographical variables (T)	地形湿度指数 Topographic wetness index (TWI)
遥感变量 Remote sensing variables (S)	红边波段 Red edge (B6)
	增强植被指数 Enhanced vegetation index (EVI)
	第一主成分 First principal component (PCA1)

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表 2 建瓯市耕地表层土壤(0~20 cm)有效磷含量统计分析

Table 2. Statistical analysis of available phosphorus content in surface soil (0−20 cm) of cultivated land in Jian’ou City, Fujian Province, China

样本组 Sample group	样本数目 Number of samples	最小值 Minimum (mg∙kg⁻¹)	最大值 Maximum (mg∙kg⁻¹)	平均值 Mean (mg∙kg⁻¹)	中间值 Median (mg∙kg⁻¹)	标准偏差 SD (mg∙kg⁻¹)	偏度 Skewness	峰度 Kurtosis	变异系数 CV (%)
总样本 Total samples	96	4.42	211.94	54.04	39.49	44.53	1.03	0.62	82.39
训练集 Calibration samples	86	4.42	211.94	53.93	44.11	45.07	1.01	0.67	83.57
验证集 Validation samples	10	23.98	134.72	55.06	36.72	41.80	1.48	0.68	75.92

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表 3 建瓯市耕地表层土壤(0~20 cm)有效磷含量与环境因子的相关性分析

Table 3. Correlation analysis of available phosphorus content and environmental factors in surface soil (0−20 cm) of cultivated land in Jian’ou City, Fujian Province, China

变量 Variable	土壤有效磷 Soil available phosphorus	pH	高程 Digital elevation model (DEM)	红边波段 Red edge (B6)	增强植被指数 Enhanced vegetation index (EVI)	地形湿度指数 Topographic wetness index (TWI)	年均降水量 Mean annual precipitation (MAP)	年均温 Mean annual temperature (MAT)	第一主成分 First principal component (PCA1)
土壤有效磷 Soil available phosphorus	1.000
pH	−0.268^**	1.000
高程 Digital elevation model (DEM)	−0.336^**	0.043	1.000
红边波段 Red edge (B6)	0.292^**	−0.149	−0.239^*	1.000
增强植被指数 Enhanced vegetation index (EVI)	0.252^*	−0.130	−0.148	0.785^**	1.000
地形湿度指数 Topographic wetness index (TWI)	−0.216^*	0.056	−0.002	−0.064	−0.128	1.000
年均降水量 Mean annual precipitation (MAP)	−0.366^**	0.028	0.835^**	−0.174	−0.060	0.002	1.000
年均温 Mean annual temperature (MAT)	0.357^**	−0.066	−0.945^**	0.240^*	0.135	0.002	−0.871^**	1.000
第一主成分 First principal component (PCA1)	0.278^**	−0.041	−0.634^**	0.325^**	−0.024	0.113	−0.612^**	0.639^**	1.000
和分别表示相关性达P<0.05和P<0.01显著水平。 and ** indicate significant relationship at P<0.05 and P<0.01 levels, respectively.

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表 4 5种环境变量组合下表层土壤(0~20 cm)有效磷的随机森林预测结果

Table 4. Performance of random forest under five combinations of environmental variables for estimation of topsoil (0−20 cm) available phosphorus content

预测模型 Prediction model	训练集 Calibration dataset			验证集 Validation dataset
预测模型 Prediction model	R²	MAE (mg∙kg⁻¹)	RMSE (mg∙kg⁻¹)	R²	MAE (mg∙kg⁻¹)	RMSE (mg∙kg⁻¹)
模型A Model A	0.63	23.05	27.39	0.59	19.04	25.26
模型B Model B	0.56	24.39	29.64	0.51	23.06	27.63
模型C Model C	0.45	27.53	33.30	0.36	22.66	31.71
模型D Model D	0.43	28.23	33.89	0.48	22.27	28.57
模型E Model E	0.54	25.28	30.48	0.53	21.71	27.20
R²为决定系数; MAE为平均绝对误差; RMSE为均方根误差。模型A为土壤pH+地形变量+遥感变量+气象变量; 模型B为土壤pH+遥感变量+气象变量; 模型C为土壤pH+地形变量+气象变量; 模型D为土壤pH+地形变量+遥感变量; 模型E为地形变量+遥感变量+气象变量。R² is the coefficient of determination; MAE is the mean absolute error; RMSE is the root mean square error. Model A is with soil pH, topographic variables, remote sensing variables, and climate variables as auxiliary variables; Model B is with soil pH, remote sensing variables and climate variables as auxiliary variables; Model C is with soil pH, topographic variables, and climate variables as auxiliary variables; Model D is with soil pH, topographic variables and remote sensing variables as auxiliary variables; Model E is with topographic variables, remote sensing variables and climate variables as auxiliary variables.

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表 5 建瓯市耕地土壤有效磷含量空间分布面积及其比例统计

Table 5. Statistics of spatial distribution area and proportion of soil available phosphorus content of cultivated land in Jian’ou City, Fujian Province, China

土壤有效磷含量 Soil available phosphorus content (mg∙kg⁻¹)	耕地面积 Cultivated land area (hm²)	面积比例 Area proportion (%)
<30	10 978.8	27.13
30~40	5743.5	14.19
40~50	6168.8	15.24
50~60	7182.4	17.75
60~70	6702.2	16.56
≥70	3695.0	9.13

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表 6 按照建瓯市土壤有效磷含量的气象和地形变量统计

Table 6. Descriptive characteristics of topography and climate conditions described by soil available phosphorus content in Jian’ou City, Fujian Province, China

土壤有效磷含量 Soil available phosphorus content (mg∙kg⁻¹)	平均高程 Mean elevation (m)	年均降水量 Mean annual precipitation (mm)	年均温 Mean annual temperature (℃)
<30	588	146	16.9
30~40	393	142	17.9
40~50	291	140	18.2
50~60	226	138	18.9
60~70	209	137	18.9
≥70	210	137	18.9

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