黑土区田块土壤有机质空间分异及分布研究
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摘要
本文选取东北典型黑土区田块为研究区,实测获取土壤样点数据,基于遥感影像和地形数据,分别利用单一地统计、混合地统计和遥感反演方法预测土壤有机质(SOM)空间分布。结果表明:研究区SOM含量变异系数为31.897%呈中等程度变异,且存在强烈空间自相关性;对光谱反射率进行数学运算得到的光谱指数"Tan345"(Landsat8影像345波段夹角正切值)与SOM极显著相关,相关系数最高达0.570;以光谱指数"Tan345"与地形因子"G"(高程)为输入量、利用回归克里格法预测的SOM精度最高;研究区SOM含量西高东低,沟底和平缓的坡顶含量较高。研究结果对于促进精准农业、估算土壤碳库有着重要的意义,并为小尺度SOM预测提供借鉴。
Soil sample data were measured in the field of typical Black Soil area of Northeast China. Based on remote sensing images and measured digital elevation models, the spatial distribution of soil organic matter(SOM) was predicted by using the methods of single geostatistics, mixed geostatistics and remote sensing inversion. The coefficient of variation of SOM content in the study area was 31.897%, with moderate variation and strong spatial autocorrelation.Spectral index obtained by mathematical calculation of spectral reflectivity was significantly correlated with SOM, and their correlation coefficient was up to 0.570. When the spectral index "tangent of the angle formed by band 3, band 4and band 5 of Landsat 8 image" and the topographic index "elevation" were taken as inputs, the content of SOM predicted by regression kriging method was the highest. Generally, SOM content in the west was higher than that in the east, and was higher in gully bottom and gentle slope. These results could be used to predict the SOM content at field scale, and provide reference for precision agriculture and the estimation of soil carbon pool.
Soil sample data were measured in the field of typical Black Soil area of Northeast China. Based on remote sensing images and measured digital elevation models, the spatial distribution of soil organic matter(SOM) was predicted by using the methods of single geostatistics, mixed geostatistics and remote sensing inversion. The coefficient of variation of SOM content in the study area was 31.897%, with moderate variation and strong spatial autocorrelation.Spectral index obtained by mathematical calculation of spectral reflectivity was significantly correlated with SOM, and their correlation coefficient was up to 0.570. When the spectral index "tangent of the angle formed by band 3, band 4and band 5 of Landsat 8 image" and the topographic index "elevation" were taken as inputs, the content of SOM predicted by regression kriging method was the highest. Generally, SOM content in the west was higher than that in the east, and was higher in gully bottom and gentle slope. These results could be used to predict the SOM content at field scale, and provide reference for precision agriculture and the estimation of soil carbon pool.
引文
[1]唐梦迎,丁建丽,夏楠,等.干旱区典型绿洲土壤有机质含量分布特征及其影响因素[J].土壤学报, 2017, 54(03):759-766.
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[14]刘玲,王海燕,戴伟,等.长白山低山区森林土壤有机碳及养分空间异质性[J].应用生态学报, 2014, 25(09):2460-2468.
[15]刘焕军,赵春江,王纪华,等.黑土典型区土壤有机质遥感反演[J].农业工程学报, 2011, 27(08):211-215.
[16] FENG C, KISSEL D E, WEST L T, et al. Field-scale mapping of surface soil organic carbon using remotely sensed imagery.[J]. Soil Science Society of America Journal, 2000, 64(2):746-753.
[17]陈琳,任春颖,王宗明,等.基于克里金插值的耕地表层土壤有机质空间预测[J].干旱区研究, 2017, 34(04):798-805.
[18]刘焕军,宁东浩,康苒,等.考虑含水量变化信息的土壤有机质光谱预测模型[J].光谱学与光谱分析, 2017, 37(02):566-570.
[19] ZHANG L, REN R, CHEN S, et al. Spatial variability of surface sediment basis on geostatistical analysis in the littoral area of Yellow River delta, China[J]. Indian Journal of Geo-Marine Sciences, 2014,43(4):463-472.
[20]施周,闫杭召,毕晨,等.基于地统计学——克里格插值法的村镇地表水体水质监测[J].环境工程学报, 2017, 11(04):2607-2613.
[21]张素梅,王宗明,张柏,等.利用地形和遥感数据预测土壤养分空间分布[J].农业工程学报, 2010, 26(05):188-194.
[22] ZHANG S, HUANG Y, SHEN C, et al. Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information[J]. Geoderma, 2012, 171-172(2):35-43.
[23]宋丰骥,常庆瑞,钟德燕.黄土高原沟壑区土壤养分空间变异及其与地形因子的相关性[J].西北农林科技大学学报(自然科学版),2011, 39(12):166-172+180.
[24]杨谦,王晓晴,孙孝林,等.基于REML的普通克里格和回归克里格在土壤属性空间预测中的比较.土壤通报, 2018, 49(2):154-163.
[25] KESKIN H, GRUNWALD S. Regression kriging as a workhorse in the digital soil mapper's toolbox☆[J]. Geoderma, 2018, 326:22-41.
[26]李欣宇,宇万太,李秀珍.遥感与地统计方法在表层土壤有机碳空间格局研究中的应用比较[J].农业工程学报,2009,25(03):148-152.
[27] ZHU A X. Measuring uncertainty in class assignment for natural resource maps under fuzzy logic[J]. Photogrammetric Engineering&Remote Sensing, 1997, 63(10):1195-1202.
[28]陈锋锐,秦奋,李熙,等.基于多元地统计的土壤有机质含量空间格局反演[J].农业工程学报, 2012, 28(20):188-194, 297.
[2] MAO Y, SANG S, LIU S, et al. Spatial distribution of pH and organic matter in urban soils and its implications on site-specific land uses in Xuzhou, China[J]. Comptes Rendus Biologies, 2014, 337(5):332-337.
[3]王苗苗,陈洪松,付同刚,等.典型喀斯特小流域不同植被类型间土壤养分的差异性及其空间预测方法[J].应用生态学报, 2016, 27(06):1759-1766.
[4] PICCINI C, MARCHETTI A, FRANCAVIGLIA R. Estimation of soil organic matter by geostatistical methods:Use of auxiliary information in agricultural and environmental assessment[J]. Ecological Indicators,2014, 36(36):301-314.
[5] ELBASIOUNY H, ABOWALY M, ABU_ALKHEIR A, et al. Spatial variation of soil carbon and nitrogen pools by using ordinary Kriging method in an area of north Nile Delta, Egypt[J]. Catena, 2014, 113(1):70-78.
[6] LIU S, AN N, YANG J, et al. Prediction of soil organic matter variability associated with different land use types in mountainous landscape in southwestern Yunnan province, China[J]. Catena, 2015, 133:137-144.
[7] LYDIA, MUMBI, CHABALA, et al. Application of ordinary Kriging in mapping soil organic carbon in Zambia[J]. Pedosphere, 2017, 27(2):338-343.
[8] XIE X L, LI A B. Improving spatial estimation of soil organic matter in a subtropical hilly area using covariate derived from vis-NIR spectroscopy[J]. Biosystems Engineering, 2016, 152:126-137.
[9]王平,李浩,陈帅,等.基于坡度的黑土区切沟密度协同克里格插值方法研究[J].水土保持研究, 2014, 21(06):312-317.
[10]吴才武,夏建新,段峥嵘.土壤有机质预测性制图方法研究进展[J].土壤通报, 2015, 46(01):239-247.
[11] XU Y, SMITH S E, GRUNWALD S, et al. Estimating soil total nitrogen in smallholder farm settings using remote sensing spectral indices and regression Kriging[J]. CATENA, 2017, 163:111-122.
[12] ZHZO M S, ROSSITE D G, LI D C, et al. Mapping soil organic matter in low-relief areas based on land surface diurnal temperature difference and a vegetation index[J]. Ecological Indicators, 2014, 39:120-133.
[13]代富强,周启刚,刘刚才.基于回归克里格和遥感的紫色土区土壤有机质含量空间预测[J].土壤通报, 2014, 45(03):562-567.
[14]刘玲,王海燕,戴伟,等.长白山低山区森林土壤有机碳及养分空间异质性[J].应用生态学报, 2014, 25(09):2460-2468.
[15]刘焕军,赵春江,王纪华,等.黑土典型区土壤有机质遥感反演[J].农业工程学报, 2011, 27(08):211-215.
[16] FENG C, KISSEL D E, WEST L T, et al. Field-scale mapping of surface soil organic carbon using remotely sensed imagery.[J]. Soil Science Society of America Journal, 2000, 64(2):746-753.
[17]陈琳,任春颖,王宗明,等.基于克里金插值的耕地表层土壤有机质空间预测[J].干旱区研究, 2017, 34(04):798-805.
[18]刘焕军,宁东浩,康苒,等.考虑含水量变化信息的土壤有机质光谱预测模型[J].光谱学与光谱分析, 2017, 37(02):566-570.
[19] ZHANG L, REN R, CHEN S, et al. Spatial variability of surface sediment basis on geostatistical analysis in the littoral area of Yellow River delta, China[J]. Indian Journal of Geo-Marine Sciences, 2014,43(4):463-472.
[20]施周,闫杭召,毕晨,等.基于地统计学——克里格插值法的村镇地表水体水质监测[J].环境工程学报, 2017, 11(04):2607-2613.
[21]张素梅,王宗明,张柏,等.利用地形和遥感数据预测土壤养分空间分布[J].农业工程学报, 2010, 26(05):188-194.
[22] ZHANG S, HUANG Y, SHEN C, et al. Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information[J]. Geoderma, 2012, 171-172(2):35-43.
[23]宋丰骥,常庆瑞,钟德燕.黄土高原沟壑区土壤养分空间变异及其与地形因子的相关性[J].西北农林科技大学学报(自然科学版),2011, 39(12):166-172+180.
[24]杨谦,王晓晴,孙孝林,等.基于REML的普通克里格和回归克里格在土壤属性空间预测中的比较.土壤通报, 2018, 49(2):154-163.
[25] KESKIN H, GRUNWALD S. Regression kriging as a workhorse in the digital soil mapper's toolbox☆[J]. Geoderma, 2018, 326:22-41.
[26]李欣宇,宇万太,李秀珍.遥感与地统计方法在表层土壤有机碳空间格局研究中的应用比较[J].农业工程学报,2009,25(03):148-152.
[27] ZHU A X. Measuring uncertainty in class assignment for natural resource maps under fuzzy logic[J]. Photogrammetric Engineering&Remote Sensing, 1997, 63(10):1195-1202.
[28]陈锋锐,秦奋,李熙,等.基于多元地统计的土壤有机质含量空间格局反演[J].农业工程学报, 2012, 28(20):188-194, 297.