基于MGWRK的土壤有机质制图及驱动因素研究

doi:10.3864/j.issn.0578-1752.2020.09.011

摘要/Abstract

摘要：

【目的】空间预测是一种获得有机质空间局部细节的重要方法,其准确性对于农田合理管理有着重要意义。本研究通过对比不同的土壤有机质空间制图方法以获得更优的预测精度,在预测的同时揭示环境协变量的空间非平稳性特征及不同环境协变量关系的空间尺度。【方法】选取晋中盆地的7个乡镇作为研究区,对比普通克里金（OK）、回归克里金（RK）、地理加权回归克里金（GWRK）和多重尺度地理加权回归克里金（MGWRK）4种不同方法对土壤有机质的预测能力和效果,并探究影响因子在空间分布中对有机质的影响效应变化和这种影响效应的空间尺度。MGWRK是一种多重尺度地理加权回归（MGWR）与普通克里金方法相结合的方法。【结果】选取坡向、坡度、年均降水量、年平均温度、海拔、植被年总初级生产力、年蒸散量、地形湿度指数、平面曲率、汇流动力指数、地形指数、地形粗糙指数、年平均NDVI为环境协变量参与建模,在多元线性回归建模中,模型统计学意义显著,这表明模型具备统计学意义。从Radius指数来看,各模型模拟效果由好到差依次为RK、OK、GWRK、MGWRK;从制图效果来看,MGWRK与GWRK制图效果相当,从有机质的空间预测图可以看出,土壤有机质在研究区呈现东西两侧偏低、中部偏高的空间格局,其中汾河以东、昌源河流经区域土壤有机质普遍偏高。坡向、年均降水量、年平均温度、海拔、地形指数、年平均NDVI对研究区东部有机质的影响强于西部,而坡度、植被年总初级生产力、年蒸散量、平面曲率、汇流动力指数、地形粗糙指数则表现出截然相反的空间非平稳性特征,地形湿度指数对有机质的影响则体现为北部强南部弱。【结论】MGWRK方法的空间预测精度分别达到了RK方法的69%、OK方法的71.74%、GWRK方法的71.17%。MGWRK在空间非平稳性特征的解释能力和空间可视化表现良好,为有机质的预测和空间非平稳性特征的描述提供方法借鉴。

关键词: 土壤有机质, 土壤制图, 多重尺度地理加权回归克里金, 地理加权回归克里金, 回归克里金, 空间非平稳性特征

Abstract:

【Objective】Spatial prediction is an important approach to obtain location-specific values of soil organic matter (SOM), which is an important figure of soil fertility and farmland management properly. This study was performed to compare different digital soil spatial mapping methods of SOM to get better prediction accuracy and to reveal the spatial non-stationarity characteristics of environmental covariates and the spatial scale of different environmental covariates at the same time. 【Method】In this study, the digital soil spatial mapping method was used, which was a combination of Multiscale Geographically Weighted Regression Model with simple Kriging of the residuals (MGWGK) for mapping SOM in seven towns from Jinzhong Basin. The performance of MGWRK with those of Ordinary Kriging (OK), Regression Kriging (RK), and Geographically Weighted Regression Kriging (GWRK) were compared to explore the relationship between influence factors and SOM on the influence degree and space scale. 【Result】Based on the stepwise regression method, 13 indexes were selected as environmental covariables in the modeling, including aspect, slope, height, annual average precipitation, annual average temperature, gross primary productivity (GPP), evapotranspiration (ET), topographic wetness index (TWI), plan curvature, stream power index (SPI), terrain position index (TPI), terrain ruggedness index (TRI), and the annual NDVI. In the multiple linear regression (MLR), the formula had statistical significance. In the Radius index, the performance of each model was in order from good to bad: RK, OK, GWRK, MGWRK. In the mapping performance, MGWRK was close to GWRK, and both of which were better than OK method and RK method. The SOM in the study area, showed a spatial pattern of higher in middle than east and west side, among which the SOM was high in the east of the Fenhe river and the Changyuan river. The influence of aspect, annual average precipitation, annual average temperature, height, TPI and the annual NDVI on SOM in the eastern of the study area was stronger than that in the western. Whiles slope, GPP, ET, plan curvature, SPI and TRI showed opposite influence in spatial. The influence of TWI on SOM was stronger in the northern than the southern. 【Conclusion】The spatial prediction accuracy of MGWRK was 69% of RK, 71.74% of OK, and 71.17% of GWRK. MGWRK performed well in the spatial non-stationary features and the spatial visualization, which provided a reference for prediction of SOM and description spatial non-stationarity characteristics.

Key words: soil organic matter (SOM), soil mapping, multiscale geographically weighted regression kriging (MGWRK), geographically weighted regression kriging (GWRK), regression kriging (RK), non-stationarity

乔磊,张吴平,黄明镜,王国芳,任健. 基于MGWRK的土壤有机质制图及驱动因素研究[J]. 中国农业科学, 2020, 53(9): 1830-1844.

Lei QIAO,WuPing ZHANG,MingJing HUANG,GuoFang WANG,Jian REN. Mapping of Soil Organic Matter and Its Driving Factors Study Based on MGWRK[J]. Scientia Agricultura Sinica, 2020, 53(9): 1830-1844.

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参考文献 39

[1]	黄昌勇, 徐建明 . 土壤学. 北京: 中国农业出版社, 2010.
	HUANG C Y, XU J M . Soil Science. Beijing: China Agriculture Press, 2010. (in Chinese)
[2]	银敏华, 李援农, 李昊, Yang Yang, 徐袁博, 张天乐 . 垄覆黑膜沟覆秸秆促进夏玉米生长及养分吸收. 农业工程学报, 2015,31(22):122-130.
	YIN M H, LI Y N, LI H, YANG Y, XU Y B, ZHANG T L . Ridge-furrow planting with black film mulching over ridge and corn straw mulching over furrow enhancing summer maize’s growth and nutrient absorption. Transactions of the Chinese Society of Agricultural Engineering, 2015,31(22):122-130. (in Chinese)
[3]	周伟, 王文杰, 何兴元, 张波, 肖路, 王琼, 吕海亮, 魏晨辉 . 哈尔滨城市绿地土壤肥力及其空间特征. 林业科学, 2018,54(9):9-17.
	ZHOU W, WANG W, HE X Y, ZHANG B, XIAO L, WANG Q, LV H L, WEI C H . Soil fertility and spatial variability of urban green land in Harbin. Scientia Silvae Sinicae, 2018,54(9):9-17. DOI: 10.11707/ j.1001-7488.20180902. (in Chinese)
[4]	庞蕊, 刘敏, 李美玲, 徐兴良 . 土壤碳排放组分区分的研究进展. 生态学杂志, 2017,36(8):2327-2335.
	PANG R, LIU M, LI M L, XU X L . Advances in research on the separation of soil carbon emissions. Chinese Journal of Ecology, 2017,36(8):2327-2335. DOI: 10.13292/j.1000-4890.201708.007. (in Chinese)
[5]	吴春生, 刘高焕, 刘庆生, 黄翀, 张韵婕, 管续栋 . 蒙古高原中北部土壤有机质空间分布研究. 资源科学, 2016,38(5):994-1002. doi: 10.18402/resci.2016.05.18
	WU C S, LIU G H, LIU Q S, HUANG C, ZHANG Y J, GUAN X D . The spatial distribution of soil organic matter on the north-central Mongolian Plateau. Resources Science, 2016,38(5):994-1002. DOI: 10.18402/resci.2016.05.18. (in Chinese) doi: 10.18402/resci.2016.05.18
[6]	陈琳, 任春颖, 王宗明, 张柏 . 基于克里金插值的耕地表层土壤有机质空间预测. 干旱区研究, 2017,34(4):798-805.
	CHEN L, REN C Y, WANG Z M, ZHANG B . Prediction of spatial distribution of topsoil organic matter content in cultivated land using Kriging methods. Arid Zone Research, 2017,34(4):798-805. DOI: 10.13866/j.azr.2017.04.12. (in Chinese)
[7]	ZENG C Y, YANG L, ZHU A X, DAVID G R, LIU J, LIU J Z, QIN C Z, WANG D S . Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method. Geoderma, 2016,281:69-82. doi: 10.1016/j.geoderma.2016.06.033
[8]	王合玲, 张辉国, 吕光辉 . 艾比湖流域土壤有机质与土壤因子响应关系的空间非平稳性分析. 水土保持研究, 2016,23(2):224-228,357,358.
	WANG H L, ZHANG H G, LV G H . Analysis of spatial non- stationary relationships between soil organic matter and soil factors in Ebinur Lake Basin. Researcher of Soil and Water Conservation, 2016,23(2):224-228,357,358. DOI: 10.13869/j.cnki. rswc.2016.02.042. (in Chinese)
[9]	马泉来, 高凤杰, 张志民, 单培明, 韩文文, 周军, 曲茉莉 . 我国东北黑土丘陵区小流域土壤有机质空间分布模拟. 环境科学研究, 2016,29(3):382-390.
	MA Q L, GAO F J, ZHANG Z M, SHAN P M, HAN W W, ZHOU J, QU M L . Simulation of spatial distribution of soil organic matter in a mollisol watershed in Northeastern China. Research of Environmental Sciences, 2016,29(3):382-390. DOI: 10.13198/j.issn.1001-6929.2016. 03.09. (in Chinese)
[10]	YE H C, HUANG W J, HUANG S Y, HUANG Y F, ZHANG S W, DONG Y Y, CHEN P F . Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon. Spatial Statistics, 2017,20:79-61.
[11]	石淑芹, 曹祺文, 李正国, 许恒周 . 气候与社会经济因素对土壤有机质影响的空间异质性分析——以黑龙江省中部地区为例. 中国生态农业学报, 2014,22(9):1102-1112.
	SHI S Q, CAO Q W, LI Z G, XU H Z . Influence of spatial heterogeneity of climatic and socio-economic factors on soil organic matter —A case study of the central Heilongjiang Province, China. Chinese Journal of Eco-Agriculture, 2014,22(9):1102-1112. DOI: 10.13930/j.cnki.cjea.140097. (in Chinese)
[12]	杜一尘, 李明泽, 范文义, 王斌 . 基于地理加权回归模型与林火遥感数据估算森林年龄. 林业科学, 2019,55(6):184-194.
	DU Y C, LI M Z, FAN W Y, WANG B . Estimation of forest stand age based on gwr model and forest fire remote sensing data. Scientia Silvae Sinicae, 2019,55(6):184-194. DOI: 10.11707/j.1001-7488. 20190622. (in Chinese)
[13]	王海宾, 侯瑞萍, 郑冬梅, 高秀会, 夏朝宗, 彭道黎 . 基于地理加权回归模型的亚热带地区乔木林生物量估算. 农业机械学报, 2018,49(6):184-190.
	WANG H B, HOU R P, ZHENG D M, GAO X H, XIA C Z, PENG D L . Biomass estimation of arbor forest in subtropical region based on geographically weighted regression model. Transactions of the Chinese Society of Agricultural Machinery, 2018,49(6):184-190. DOI: 10.6041/j.issn.1000-1298.201.06.021. (in Chinese)
[14]	亚森江·喀哈尔, 茹克亚·萨吾提, 尼加提·卡斯木, 尼格拉·塔什甫拉提, 张飞, 阿不都艾尼·阿不里, 师庆东 . 优化光谱指数的露天煤矿区土壤重金属含量估算. 光谱学与光谱分析, 2019,39(8):2486-2494.
	YASENJIANG K, RUKEYA S, NIJAT K, NIGARA T, ZHANG F, ABDUGHENI A, SHI Q D . Estimation of heavy metal contents in soil around open pit coal mine area based on optimized spectral index. Spectroscopy and Spectral Analysis, 2019,39(8):2486-2494. DOI: 10.3964/j.issn.1000-0593(2019)08-2486-09. (in Chinese)
[15]	许珊, 邹滨, 宫俊霞 . 2001~2015年中国城镇化与PM_(2.5)浓度时空关联特征. 中国环境科学, 2019,39(2):469-477.
	XU S, ZOU B, GONG J X . Analysis of the spatial-temporal association between urbanization and PM2.5 concentration during 2001~2015 period in Mainland China. China Environmental Science, 2019,39(2):469-477. DOI: 10.19674/j.cnki.issn1000-6923.2019.0057. (in Chinese)
[16]	XU C C, ZHAO J Y, LIU P . A geographically weighted regression approach to investigate the effects of traffic conditions and road characteristics on air pollutant emissions. Journal of Cleaner Production, 2019,239:118084. doi: 10.1016/j.jclepro.2019.118084
[17]	王海军, 张彬, 刘耀林, 刘艳芳, 徐姗, 邓羽, 赵雲泰, 陈宇琛, 洪松 . 基于重心-GTWR模型的京津冀城市群城镇扩展格局与驱动力多维解析. 地理学报, 2018,73(6):1076-1092. doi: 10.11821/dlxb201806007
	WANG H J, ZHANG B, LIU Y L, LIU Y F, XU S, DENG Y, ZHAO Y T, CHEN Y C, HONG S . Multi-dimensional analysis of urban expansion patterns and their driving forces based on the center of gravity-GTWR model: A case study of the Beijing-Tianjin-Hebei urban agglomeration. Acta Geographica Sinica, 2018,73(6):1076-1092. DOI: 10.11821/dlxb201806007. (in Chinese) doi: 10.11821/dlxb201806007
[18]	杨晴青, 刘倩, 尹莎, 张戬, 杨新军, 高岩辉 . 秦巴山区乡村交通环境脆弱性及影响因素——以陕西省洛南县为例. 地理学报, 2019,74(6):1236-1251. doi: 10.11821/dlxb201906012
	YANG Q Q, LIU Q, YIN S, ZHANG J, YANG X J, GAO Y H . Vulnerability and influencing factors of rural transportation environment in Qinling-Daba mountainous areas: A case study of Luonan county in Shaanxi province. Acta Geographica Sinica, 2019,74(6):1236-1251. DOI: 10.11821/dlxb201906012. (in Chinese) doi: 10.11821/dlxb201906012
[19]	鲍士旦 . 土壤农化分析. 3版. 北京: 中国农业出版社, 2015.
	BAO S D. Soil and Agrochemical Analysis. 3rd ed. Beijing: Chinese Agriculture Press, 2015. (in Chinese)
[20]	刘爱利, 王培法, 丁园圆 . 地统计学概论. 北京: 科学出版社, 2012.
	LIU A L, WANG P F, DING Y Y. Introduction to Geostatistics. Beijing: Science Press, 2012. (in Chinese)
[21]	WU Z H, WANG B Z, HUANG J L, AN Z H, JIANG P, CHEN Y Y, LIU Y F . Estimating soil organic carbon density in plains using landscape metric-based regression Kriging model. Soil & Tillage Research, 2019,195:104381.
[22]	HENGL T, HEUVELINK G B M, ROSSITER D G . About regression- kriging: From equations to case studies. Computers and Geosciences, 2007,33(10):1301-1315. doi: 10.1016/j.cageo.2007.05.001
[23]	SHEN Q S, WANG Y, WANG X R, LIU X, ZHANG X Y, ZHANG S L . Comparing interpolation methods to predict soil total phosphorus in the Mollisol area of Northeast China. Catena, 2019,174:59-72. doi: 10.1016/j.catena.2018.10.052
[24]	杨顺华, 张海涛, 郭龙, 任艳 . 基于回归和地理加权回归Kriging的土壤有机质空间插值. 应用生态学报, 2015,26(6):1649-1656. pmid: 26572015
	YANG S H, ZHANG H T, GUO L, REN Y . Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging. Chinese Journal of Applied Ecology, 2015,26(6):1649-1656. DOI: 10.13287/j.1001-9332.20150331.023. (in Chinese) pmid: 26572015
[25]	聂磊, 舒红, 刘艳 . 复杂地形地区月平均气温(混合)地理加权回归克里格插值. 武汉大学学报(信息科学版), 2018,43(10):1553-1559.
	NIE L, SHU H, LIU Y . Interpolation of monthly average temperature by using (mixed) Geographically Weighted Regression Kriging in the complex terrain region. Geomatics and Information Science of Wuhan University, 2018,43(10):1553-1559. DOI: 10.13203/j.whugis20160433. (in Chinese)
[26]	A. STEWART F, CHRIS B, MARTIN C . Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley, 2002.
[27]	A. STEWART F, YANG W B, KANG W . Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 2017,107(6):1247-1265. doi: 10.1080/24694452.2017.1352480
[28]	LI Z Q, TAYLOR O, STEWART F, KANG W, LEVI W, YU H C, LUO W . MGWR 1. 0 User Manual. Spatial Analysis Research Center (SPARC). [2019-11-1] https: //sgsup.asu.edu/sparc/mgwr.
[29]	YANG S H, LIU F, SONG X D, LU Y Y, LI D C, ZHAO Y G, ZHANG G L . Mapping topsoil electrical conductivity by a mixed geographically Weighted Regression Kriging: A case study in the Heihe River Basin, northwest China. Ecological Indicators, 2019,102:252-264.
[30]	邱孟龙, 曹小曙, 周建, 冯小龙, 高兴川 . 基于GWR模型的渭北黄土旱塬粮食单产空间分异及其影响因子分析——以陕西彬县为例. 中国农业科学, 2019,52(2):273-284.
	QIU M L, CAO X S, ZHOU J, FENG X L, GAO X C . Spatial differentiation and impact factors of grain yield per hectare in Weibei Plateau based on GWR Model: A case study of Binxian County, Shannxi. Scientia Agricultura Sinica, 2019,52(2):273-284. DOI: 10.3864/j.issn.0578-1752.2019.02.007. (in Chinese)
[31]	邹润彦, 周宏冀, 郭熙, 但承龙, 吕添贵, 李洪义 . 环鄱阳湖区农田土壤有机碳影响因素空间分布格局分析及制图研究. 长江流域资源与环境, 2019,28(5):1121-1131.
	ZOU R Y, ZHOU H J, GUO X, DAN C L, LV T G, LI H Y . Spatial distribution and mapping of influencing factors of farmland soil organic carbon in the Poyang Lake Region. Resources and Environment in the Yangtze Basin, 2019,28(5):1121-1131. DOI: 10.11870/ cjlyzyyhj201905012. (in Chinese)
[32]	杨帆, 曾维忠, 张维康, 庄天慧 . 林农森林碳汇项目持续参与意愿及其影响因素. 林业科学, 2016,52(7):138-147. doi: 10.11707/j.1001-7488.20160717
	YANG F, ZENG W Z, ZHANG W K, ZHUANG T H . Foresters’ constant participation willingness and affecting factors in forest carbon sequestration project. Scientia Silvae Sinicae, 2016,52(7):138-147. DOI: 10.11707/j.1001-7488.20160717. (in Chinese) doi: 10.11707/j.1001-7488.20160717
[33]	张吴平, 杨坚 . 食品试验设计与统计分析. 3版. 北京: 中国农业大学出版社, 2017.
	ZHANG W P, YANG J. . Food Experimental Design and Statistical Analysis. 3rd ed. Beijing: China Agricultural University Press, 2017. (in Chinese)
[34]	郑新奇, 吕利娜 . 地统计学（现代空间统计学）. 北京: 科学出版社, 2018.
	ZHENG X Q, LV L N. Geostatistics (Modern Spatial Statistics). Beijing: Science Press, 2018. (in Chinese)
[35]	李哈滨, 王政权, 王庆成 . 空间异质性定量研究理论与方法. 应用生态学报, 1998(6):93-99.
	LI H B, WANG Z Q, WANG Q C . Theory and methodology of spatial heterogeneity quantification. Chinese Journal of Applied Ecology, 1998(6):93-99. DOI: 10.13287/j.1001-9332.1998.0136. (in Chinese)
[36]	郑帅 . 温带森林土壤有机质分解对土壤含水量变化的响应[D]. 长春: 东北师范大学, 2019.
	ZHENG S . Response of soil organic matter decomposition to soil water content change in temperate forests[D]. Changchun: Northeast Normal University, 2019. (in Chinese)
[37]	肖璐, 郎艺超, 夏浪, 楼昭涵, 孙楠, 黄李童, George Christakos . 基于多源数据的PM(2.5)浓度时空分布预测与制图. 环境科学, 2017,38(12):4913-4923. doi: 10.13227/j.hjkx.201705122 pmid: 29964548
	XIAO L, LANG Y C, XIA L, LOU Z H, SUN N, HUANG L T, GEORGE C . Space-time estimations and mapping of PM2.5 fine particulates based on multi-source data. Environmental Science, 2017,38(12):4913-4923. DOI: 10.13227/j.hjkx.201705122. (in Chinese) doi: 10.13227/j.hjkx.201705122 pmid: 29964548
[38]	田瑞云, 王玉宽, 傅斌, 刘援 . 基于DEM的地形单元多样性指数及其算法. 地理科学进展, 2013,32(1):121-129. (in Chinese) doi: 10.11820/dlkxjz.2013.01.013
	TIAN R Y, WANG Y K, FU B, LIU Y . DEM-based topographic unit diversity index and its algorithm. Progress in Geography, 2013,32(1):121-129. DOI: 10.3724/SP.J.1033.2013.00121. (in Chinese) doi: 10.11820/dlkxjz.2013.01.013
[39]	RILEY S J, DEGLORIA S D, ELLIOT R . A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain Journal of Sciences, 1999,5:1-4.

数据类型 Data type	指标 Index
地形因子数据 Terrain data	坡向Aspect
	坡度 Slope
	海拔 Height
	地形湿度指数 Topographic wetness index, TWI
	平面曲率 Plan curvature, PC
	汇流动力指数 Stream power index, SPI
	地形指数 Terrain position index, TPI
	地形粗糙指数 Terrain ruggedness index, TRI
气象数据 Meteorological data	年平均降水Annual mean precipitation, PRE
气象数据 Meteorological data	年平均温度 Annual mean temperature, TEM
遥感数据 Remote sensing data	年植被总生产量GPP (From MOD17A2)
	年蒸散量 ET (From MOD16A2)
	年平均NDVI (来自MOD13A1,使用最大合成法求得) Annual mean NDVI (Data from MOD13A1, obtained by Maximum Value Composites method)

指标Index	容差 Tolerance	VIF
坡向Aspects (°)	0.993	1.007
坡度Slope (°)	0.548	1.824
年均降水量Annual mean precipitation (mm)	0.514	1.944
年平均温度Annual mean temperature (℃)	0.417	2.400
海拔Height (m)	0.670	1.493
植被年总初级生产力Gross primary productivity (kgC·m^-2)	0.216	4.636
年蒸散量Evapotranspiration (mm)	0.194	5.168
地形湿度指数 Topographic wetness index	0.652	1.534
平面曲率Plan curvature	0.928	1.078
汇流动力指数 Stream power index	0.980	1.021
地形指数 Terrain position index	0.730	1.369
地形粗糙指数 Terrain ruggedness index	0.548	1.824
年平均归一化植被指数 The annual NDVI	0.893	1.120

指标 Index	最小值 Minimum	最大值 Maximum	平均值 Mean	标准差 Standard deviation	方差 Variance	变异系数 Coefficient of variation (%)
有机质Soil organic matter (g·kg^-1)	2.10	33.00	13.91	5.01	25.09	36.00
坡向Aspects (°)	-1.00	359.12	180.32	106.64	11373.13	59.14
坡度Slope (°)	0.00	33.37	6.84	4.60	21.20	67.36
年均降水量 Annual mean precipitation (mm)	499.01	511.01	503.72	2.00	3.98	0.40
年平均温度 Annual mean temperature (℃)	12.25	15.20	13.37	0.73	0.53	5.45
海拔Height (m)	690.00	927.00	744.04	22.77	518.46	3.06
植被年总初级生产力 Gross primary productivity (kgC·m^-2)	270.28	817.96	557.30	68.57	4702.51	12.30
年蒸散量Evapotranspiration (mm)	248.43	461.20	358.95	37.47	1403.98	10.44
地形湿度指数 Topographic wetness index	0.00	19.39	9.61	3.14	9.86	32.67
平面曲率 Plan curvature	-0.01	0.01	0.00	0.00	0.00	1281.06
汇流动力指数 Stream power index	-167350.00	11730.90	-3405.69	18999.56	360983218.24	-557.88
地形指数 Terrain position index	-21.49	21.78	-0.47	4.88	23.78	-1040.39
地形粗糙指数 Terrain ruggedness index	0.00	18.72	3.81	2.25	5.05	58.96
年平均归一化植被指数 The annual NDVI	0.00	2.80	0.71	0.19	0.04	26.44

	拟合模型 Fitting model	块金值 Nugget	基台值 Sill	变程 Range (m)	块金系数 Nugget/Sill (%)
MLR残差 MLR residuals	E	10.59	21.20	2265	49.95
GWR残差 GWR residuals	E	0.38	3.72	636	10.22
MGWR残差MGWR residuals	E	0.81	2.29	657	35.37

模型 Model	RMSE	MAE	ME	R²	Radius
OK	2.8553	2.1009	0.0009	0.6984	107.4383
RK	2.7613	2.0396	-0.0001	0.7207	98.1195
MGWRK	3.5430	2.6292	0.0036	0.5010	172.8181
GWRK	3.0622	2.2402	0.0978	0.6492	133.6925