[1] 刘航, 吴文斌, 申格, 等. 1996—2016年松嫩平原传统大豆种植结构的时空演变. 应用生态学报, 2018, 29(10): 119-126 [Liu H, Wu W-B, Shen G, et al. Spatio-temporal evolution of traditional soybean planting structure in Songnen Plain, China in 1996-2016. Chinese Journal of Applied Ecology, 2018, 29(10): 119-126] [2] Peng J, Biswas A, Jiang QS. Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China. Geoderma, 2019, 337: 1309-1319 [3] 姚宇, 朱昌达. “渤海粮仓”典型区土壤养分时空变异. 中国人口·资源与环境, 2018, 28(suppl.1): 160-163 [Yao Y, Zhu C-D. Spatio-temporal variability of soil characteristics of the ‘Bohai Granary' typical area. China Population, Resources and Environment, 2018, 28(suppl.1): 160-163] [4] 孙宏勇, 刘小京, 张喜英. 盐碱地水盐调控研究. 中国生态农业学报, 2018, 26(10): 1528-1536 [Sun H-Y, Liu X-J, Zhang X-Y. Regulations of salt and water of saline-alkali soil: A review. Chinese Journal of Eco-Agriculture, 2018, 26(10): 1528-1536] [5] 姚海燕, 王纪忠, 付丽娜. 山东无棣县盐碱地综合治理、改良措施与成效. 农业工程技术, 2017, 37(8): 29-30 [Yao H-Y, Wang J-Z, Fu L-N. Comprehensive control, improvement measures and effects of saline and alkali land in Wudi County, Shandong Province. Agricultural Engineering Technology, 2017, 37(8): 29-30] [6] Schillaci C, Acutis M, Lombardo L, et al. Spatio-temporal topsoil organic carbon mapping of a semi-arid Medi-terranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modeling. Science of the Total Environment, 2017, 601-602: 821-832 [7] 张法升, 曲威, 尹光华, 等. 基于多光谱遥感影像的表层土壤有机质空间格局反演. 应用生态学报, 2010, 21(4): 883-888 [Zhang F-S, Qu W, Yin G-H, et al. Spatial pattern of surface soil organic matter based on remotely sensed multispectral image. Chinese Journal of Applied Ecology, 2010, 21(4): 883-888] [8] Wang SZ, Fan JW, Zhong HP, et al. A multi-factor weighted regression approach for estimating the spatial distribution of soil organic carbon in grassland. Catena, 2019, 174: 248-258 [9] 刘焕军, 潘越, 窦欣, 等. 黑土区田块尺度土壤有机质含量遥感反演模型. 农业工程学报, 2018, 34(1): 127-133 [Liu H-J, Pan Y, Dou X, et al. Soil organic matter content inversion model with remote sensing image in field scale of black soil area. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(1): 127-133] [10] 单海斌, 蒋平安, 颜安. 基于高光谱数据的北疆绿洲农田灰漠土有机质反演. 农业资源与环境学报, 2018, 35(3): 276-282 [Shan H-B, Jiang P-A, Yan A. Inversion of organic matter content in grey desert soil of Northern Xinjiang oasis farmland based on hyper-spectral data. Journal of Agricultural Resources and Environment, 2018, 35(3): 276-282] [11] 朱婉雪, 李仕冀, 张旭博, 等. 基于无人机遥感植被指数优选的田块尺度冬小麦估产. 农业工程学报, 2018, 34(11): 78-86 [Zhu W-X, Li S-J, Zhang X-B, et al. Estimation of winter wheat yield using optimal vegetation indices from unmanned aerial vehicle remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(11): 78-86] [12] Casa R, Castaldi F, Pascucci S, et al. A comparison of sensor resolution and calibration strategies for soil texture estimation from hyperspectral remote sensing. Geoderma, 2013, 197-198: 17-26 [13] Selige T, Jürgen B, Schmidhalter U. High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures. Geoderma, 2006, 136: 235-244 [14] 周在明, 杨燕明, 陈本清, 等. 基于无人机遥感监测滩涂湿地入侵种互花米草植被覆盖度. 应用生态学报, 2016, 27(12): 3920-3926 [Zhou Z-M, Yang Y-M, Chen B-Q, et al. Fractional vegetation cover of invasive Spartina alterniflora in coastal wetland using unmanned aerial vehicle (UAV) remote sensing. Chinese Journal of Applied Ecology, 2016, 27(12): 3920-3926] [15] 孙刚, 黄文江, 陈鹏飞, 等. 轻小型无人机多光谱遥感技术应用进展. 农业机械学报, 2018, 49(3): 1-17 [Sun G, Huang W-J, Chen P-F, et al. Advances in UAV-based multispectral remote sensing applications. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(3): 1-17] [16] 谭昌伟, 王纪华, 朱新开, 等. 基于Landsat TM影像的冬小麦拔节期主要长势参数遥感监测. 中国农业科学, 2011, 44(7): 1358-1366 [Tan C-W, Wang J-H, Zhu X-K, et al. Monitoring main growth status parameters at jointing stage in winter wheat based on Landsat TM images. Scientia Agricultura Sinica, 2011, 44(7): 1358-1366] [17] 张东辉, 赵英俊, 秦凯. 一种新的光谱参量预测黑土养分含量模型. 光谱学与光谱分析, 2018, 38(9): 2932-2936 [Zhang D-H, Zhao Y-J, Qin K. A new model for predicting black soil nutrient content by spectral parameters. Spectroscopy and Spectral Analysis, 2018, 38(9): 2932-2936] [18] 谭琨, 张倩倩, 曹茜, 等. 基于粒子群优化支持向量机的矿区土壤有机质含量高光谱反演. 地球科学(中国地质大学学报), 2015, 40(8): 1339-1345 [Tan K, Zhang Q-Q, Cao X, et al. Hyperspectral retrieval model of soil organic matter content based on particle swarm optimization-support vector machines. Earth Science-Journal of China University of Geosciences, 2015, 40(8): 1339-1345] [19] 郑春雅, 许中旗, 马长明, 等. 冀西北坝上地区退化防护林的土壤性质. 水土保持学报, 2016, 30(1): 203-207 [Zheng C-Y, Xu Z-Q, Ma C-M, et al. Soil properties of degraded shelter forests in Bashang Plateau of Northwestern Hebei Province. Journal of Soil and Water Conservation, 2016, 30(1): 203-207] [20] 吕真真, 刘广明, 杨劲松, 等. 黄河三角洲滨海盐渍土区土壤质量综合评价. 干旱地区农业研究, 2015, 33(6): 93-97 [Lyu Z-Z, Liu G-M, Yang J-S, et al. Synthetic evaluation of soil quality of the coastal saline soil in Yellow River Delta Area. Agricultural Research in the Arid Area, 2015, 33(6): 93-97] [21] 于淑会, 韩立朴, 高会, 等. 高水位区暗管埋设下土壤盐分适时立体调控的生态效应. 应用生态学报, 2016, 27(4): 1061-1068 [Yu S-H, Han L-P, Gao H, et al. Ecological effects of soil salinity regulation through saline water irrigation and subsurface drainage in high water table level area. Chinese Journal of Applied Ecology, 2016, 27(4): 1061-1068] [22] 杨顺华, 张海涛, 郭龙, 等. 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 [Yang S-H, Zhang H-T, Guo L, et al. Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging. Chinese Journal of Applied Ecology, 2015, 26(6): 1649-1656] [23] 谢文军, 张衍鹏, 张淼, 等. 滨海盐渍化土壤理化性质与小麦生产间的关系. 土壤学报, 2015, 52(2): 461-466 [Xie W-J, Zhang Y-P, Zhang M, et al. Relationships betwen soil physicochemical properties and wheat production in coastal saline soil. Acta Pedologica Sinica, 2015, 52(2): 461-466] [24] 刘云慧, 宇振荣, 张风荣, 等. 县域土壤有机质动态变化及其影响因素分析. 植物营养与肥料学报, 2005, 11(3): 294-301 [Liu Y-H, Yu Z-R, Zhang F-R, et al. Dynamic change of soil organic matter and its affecting factors at county level. Plant Nutrition and Fertilizer Science, 2005, 11(3): 294-301] [25] 陈小红, 段争虎. 土壤碳素固定及其稳定性对土壤生产力和气候变化的影响研究. 土壤通报, 2007, 38(4): 765-772 [Chen X-H, Duan Z-H. Impacts of soil carbon sequestration and stabilization on soil productivity and climate change: A review. Chinese Journal of Soil Science, 2007, 38(4): 765-772] [26] 陈思明, 毛艳玲, 邹小兴, 等. 基于不同建模方法的湿地土壤有机质含量多光谱反演. 土壤通报, 2018, 49(1): 16-22 [Chen S-M, Mao Y-L, Zou X-X, et al. Comparative assessment of different methods for estimating soil organic matter content with multispectral data in wetland. Chinese Journal of Soil Science, 2018, 49(1): 16-22] [27] Liu JB, Han JC, Zhang Y, et al. Prediction of soil organic carbon with different parent materials development using visible-near infrared spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, 204: 33-39] [28] 张新乐, 窦欣, 谢雅慧, 等. 引入时相信息的耕地土壤有机质遥感反演模型. 农业工程学报, 2018, 34(4): 143-150 [Zhang X-L, Dou X, Xie Y-H, et al. Remote sensing inversion model of soil organic matter in farmland by introducing temporal information. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(4): 143-150] [29] 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. 干旱区研究, 2017, 34(4): 798-805 [Chen L, Ren C-Y, Wang Z-M, et al. Prediction of spatial distribution of topsoil organic matter content in cultivated land using Kriging methods. Arid Zone Research, 2017, 34(4): 798-805] [30] 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. 光谱学与光谱分析, 2017, 37(2): 566-570 [Liu H-J, Ning D-H, Kang R, et al. A study on predicting model of organic matter content incorporating soil moisture variation. Spectroscopy and Spectral Analysis, 2017, 37(2): 566-570] |