地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (7): 983-993.doi: 10.3724/SP.J.1047.2017.00983
姜红1(), 玉素甫江·如素力1,2,*(
), 热伊莱·卡得尔1, 阿迪来·乌甫1
收稿日期:
2017-01-03
修回日期:
2017-02-23
出版日期:
2017-07-10
发布日期:
2017-07-10
作者简介:
作者简介:姜 红(1991-),男,重庆人,硕士生,主要从事资源环境遥感研究。E-mail:
基金资助:
JIANG Hong1(), YUSUFUJIANG Rusuli1,2,*(
), REYILAI Kadeer1, ADILAI Wufu1
Received:
2017-01-03
Revised:
2017-02-23
Online:
2017-07-10
Published:
2017-07-10
Contact:
YUSUFUJIANG Rusuli
摘要:
土壤盐渍化严重制约了农业可持续发展和生态安全,土壤盐渍化的精确评价分析,对土壤盐渍化的改善和治理具有重要的意义。本文以新疆焉耆盆地为研究对象,Landsat8 OLI遥感影像和实测采样数据相结合,提取地下水埋深(GD)、盐分指数(SI)、地表蒸散量(SET)和改进型温度植被干旱指数(MTVDI)建立了土壤盐渍化评价模型。结果表明:①结合野外实测土壤盐分数据,对BP神经网络模型进行训练。最终以最优的4-4-1结构的3层BP神经网模型对研究区土壤盐渍化进行了预测(R2=0.864,RMSE=0.569)。相比传统多元线性回归模型(R2=0.741,RMSE=0.767),神经网络模型对土壤盐渍化的预测精度更高;②土壤盐渍化分布与GD、SI、SET和MTVDI等存在较强的关联性,不同等级的土壤盐渍化是不同影响因素不同程度上组合而引起的结果,盐渍化土地主要分布在地下水位较低以及土地开垦之后没有利用的荒地区域;③整个研究区大部分区域受到不同程度的盐渍化影响,耕地退化为盐渍地导致该区域土壤盐渍化以及土壤次生盐渍化进一步加剧。
姜红, 玉素甫江·如素力, 热伊莱·卡得尔, 阿迪来·乌甫. 基于神经网络模型的干旱区绿洲土壤盐渍化评价分析[J]. 地球信息科学学报, 2017, 19(7): 983-993.DOI:10.3724/SP.J.1047.2017.00983
JIANG Hong,YUSUFUJIANG Rusuli,REYILAI Kadeer,ADILAI Wufu. Evaluation and Analysis of Soil Salinization in the Arid Zones based on Neural Network Model[J]. Journal of Geo-information Science, 2017, 19(7): 983-993.DOI:10.3724/SP.J.1047.2017.00983
表1
训练样本
MTVDI | SI | GD/m | SET/mm | MTVDI | SI | GD/m | SET/mm |
---|---|---|---|---|---|---|---|
0.564 | 0.829 | 7.268 | 3.340 | 0.455 | 1.061 | 5.779 | 3.777 |
0.499 | 1.011 | 6.424 | 4.346 | 0.125 | 1.091 | 4.781 | 2.955 |
0.545 | 0.940 | 7.650 | 3.789 | 0.652 | 0.908 | 8.194 | 4.562 |
0.690 | 0.991 | 8.521 | 6.529 | 0.551 | 0.975 | 7.101 | 3.343 |
0.446 | 1.191 | 5.647 | 5.479 | 0.561 | 0.942 | 7.223 | 3.761 |
0.570 | 0.893 | 7.331 | 3.682 | 0.699 | 0.888 | 8.592 | 5.433 |
0.387 | 1.066 | 4.665 | 2.433 | 0.396 | 1.036 | 4.827 | 3.527 |
0.481 | 1.063 | 6.167 | 3.093 | 0.556 | 0.835 | 7.162 | 4.371 |
0.473 | 1.112 | 6.046 | 4.002 | 0.364 | 1.128 | 4.256 | 2.965 |
0.425 | 1.043 | 5.315 | 4.088 | 0.244 | 1.114 | 7.024 | 3.072 |
0.252 | 0.957 | 2.034 | 2.358 | 0.657 | 0.852 | 8.240 | 4.194 |
0.603 | 0.895 | 7.705 | 6.585 | 0.341 | 1.183 | 3.841 | 4.362 |
0.287 | 1.019 | 5.499 | 3.811 | 0.507 | 1.048 | 6.540 | 4.324 |
0.551 | 0.908 | 7.102 | 4.387 | 0.523 | 0.852 | 6.755 | 3.571 |
0.707 | 0.906 | 8.651 | 3.386 | 0.602 | 1.029 | 7.694 | 4.335 |
0.535 | 0.929 | 6.907 | 3.756 | 0.253 | 0.995 | 2.065 | 2.320 |
0.467 | 0.919 | 5.970 | 3.339 | 0.452 | 0.870 | 5.731 | 4.230 |
0.366 | 0.849 | 4.301 | 2.508 | 0.575 | 0.877 | 7.395 | 4.269 |
0.534 | 0.963 | 6.896 | 3.361 | 0.076 | 1.362 | 0.914 | 2.681 |
0.463 | 1.124 | 5.903 | 5.865 | 0.490 | 1.024 | 6.298 | 3.351 |
0.466 | 1.187 | 5.950 | 4.244 | 0.394 | 0.806 | 4.784 | 3.534 |
0.526 | 1.132 | 6.792 | 4.448 | 0.663 | 0.887 | 8.292 | 3.561 |
0.653 | 0.874 | 8.206 | 3.714 | 0.689 | 0.966 | 8.509 | 3.247 |
0.360 | 1.124 | 7.220 | 2.443 | 0.438 | 0.822 | 5.515 | 4.361 |
0.406 | 1.010 | 4.994 | 5.576 | 0.585 | 0.985 | 7.506 | 4.307 |
表2
测试样本
MTVDI | SI | GD/m | SET/mm | 盐分实测 值/(g/kg) | 盐分预测 值/(g/kg) |
---|---|---|---|---|---|
0.381 | 1.266 | 4.567 | 3.546 | 26.777 | 16.188 |
0.452 | 0.870 | 5.731 | 2.229 | 22.459 | 7.294 |
0.295 | 1.129 | 6.362 | 3.122 | 51.954 | 32.565 |
0.446 | 0.884 | 5.640 | 4.032 | 2.247 | 3.769 |
0.531 | 0.831 | 6.848 | 3.190 | 1.249 | 2.100 |
0.597 | 0.935 | 7.642 | 3.648 | 1.454 | 1.185 |
0.659 | 1.057 | 8.260 | 4.003 | 2.543 | 0.824 |
0.508 | 0.824 | 6.546 | 3.785 | 0.862 | 2.049 |
0.313 | 0.807 | 3.293 | 3.506 | 6.360 | 14.040 |
0.566 | 0.922 | 7.293 | 4.687 | 1.807 | 1.080 |
0.466 | 0.822 | 5.942 | 3.991 | 2.749 | 2.819 |
0.465 | 1.133 | 5.939 | 2.205 | 17.086 | 10.271 |
0.765 | 1.147 | 9.034 | 4.056 | 1.308 | 0.575 |
0.448 | 0.987 | 5.670 | 3.843 | 4.217 | 4.834 |
0.229 | 1.046 | 6.834 | 3.026 | 46.748 | 40.194 |
0.423 | 1.163 | 5.271 | 4.599 | 1.366 | 6.283 |
0.485 | 0.986 | 6.229 | 5.950 | 0.749 | 1.490 |
0.638 | 0.859 | 8.057 | 3.842 | 0.720 | 0.797 |
0.575 | 0.999 | 7.399 | 3.605 | 4.127 | 1.575 |
0.515 | 1.126 | 6.638 | 5.632 | 1.014 | 1.606 |
0.639 | 0.927 | 8.075 | 3.562 | 3.952 | 0.897 |
0.558 | 1.029 | 7.195 | 3.648 | 0.661 | 1.900 |
0.516 | 1.042 | 8.253 | 4.311 | 2.424 | 2.697 |
0.666 | 1.004 | 5.064 | 4.367 | 1.601 | 0.620 |
0.466 | 1.161 | 5.954 | 4.044 | 1.455 | 5.087 |
0.610 | 0.884 | 7.784 | 3.167 | 5.714 | 1.156 |
0.514 | 0.993 | 6.627 | 3.695 | 1.131 | 2.687 |
0.755 | 0.935 | 8.978 | 4.472 | 1.366 | 0.531 |
0.422 | 1.166 | 5.259 | 4.062 | 4.555 | 7.920 |
0.507 | 1.048 | 6.540 | 4.219 | 0.984 | 2.558 |
[1] |
孙倩,塔西甫拉提·特依拜,丁建丽,等.干旱区典型绿洲土地利用/覆被变化及其对土壤盐渍化的效应研究——以新疆沙雅县为例[J].地理科学进展,2012,31(9):1212-1223.
doi: 10.11820/dlkxjz.2012.09.013 |
[ Sun Q, Tashpolat·Tiyip, Ding J L, et al. Study on land use/cover changes and soil salinization in dry areas: a case study of Shaya county in Xinjiang[J]. Progress in Geography, 2012,31(9):1212-1223. ]
doi: 10.11820/dlkxjz.2012.09.013 |
|
[2] | 翁永玲,宫鹏.土壤盐渍化遥感应用研究进展[J].地理科学,2006,26(3):369-375. |
[ Weng Y L, Gong P.A review on remote sensing technique for salt-affected soils[J]. Scientia Geographica Sinica, 2006,26(3):369-375. ] | |
[3] | 王遵亲,祝寿泉,俞仁培,等.中国盐渍土[M].北京:科学出版社,1993:7-78. |
[ Wang Z Q, Zhu S Q, Yu R P, et al.Chinese Saline soil[M]. Beijing: Science Press, 1993:7-78. ] | |
[4] |
张荣群,乔月霞,薛佳妮.银川平原土壤盐渍化与土地利用强度的空间关联分析[J].地球信息科学学报,2015,17(5):598-606.
doi: 10.3724/SP.J.1047.2015.00598 |
[ Zhang R Q, Qiao Y X, Xue J N.Spatial relationship analysis between the soil salinization and land use intensity in Yinchuan Plain[J]. Journal of Geo-information Science, 2015,17(5):598-606. ]
doi: 10.3724/SP.J.1047.2015.00598 |
|
[5] |
丁建丽,陈文倩,陈芸.干旱区土壤盐渍化灾害预警——以渭——库绿洲为例[J].中国沙漠,2016,36(4):1079-1086.
doi: 10.7522/j.issn.1000-694X.2015.00067 |
[ Ding J L, Chen W Q, Chen Y.Soil salinization disaster warning in arid zones: A case study in the Ugan-Kuqa oasis[J]. Journal of Desert Research, 2016,36(4):1079-1086. ]
doi: 10.7522/j.issn.1000-694X.2015.00067 |
|
[6] |
王静,刘湘南,黄方,等.基于ANN技术和高光谱遥感的盐渍土盐分预测[J].农业工程学报,2009,25(12):161-166.
doi: 10.3969/j.issn.1002-6819.2009.12.029 |
[ Wang J, Liu X N, Huang F, et al. Salinity forecasting saline soil based on ANN and hyperspectral remote sensing[J].Transactions of the CSAE, 2009,25(12):161-166. ]
doi: 10.3969/j.issn.1002-6819.2009.12.029 |
|
[7] |
史晓霞,王静,任春颖,等.基于GIS与Geo-CA模型的半干旱区土壤盐碱化动态模拟研究[J].东北师范大学学报(自然科学版),2004,36(2):88-94.
doi: 10.3321/j.issn:1000-1832.2004.02.016 |
[ Shi X X, Wang J, Ren C Y, et al.Study on the modeling of soil salinization in semi-arid area based on GIS and Geo-CA model[J]. Journal of Northeast Normal University, 2004,36(2):88-94. ]
doi: 10.3321/j.issn:1000-1832.2004.02.016 |
|
[8] |
陈实,高超,徐斌,等.新疆石河子农区土壤含盐量定量反演及其空间格局分析[J].地理研究,2014,33(11):2135-2144.
doi: 10.11821/dlyj201411013 |
[ Chen S, Gao C, Xu B, et al.Quantitative inversion of soil salinity and analysis of its spatial pattern in agricultural area in Shihezi of Xinjiang[J]. Geographical Research, 2014,33(11):2135-2144. ]
doi: 10.11821/dlyj201411013 |
|
[9] |
谢姆斯叶·艾尼瓦尔,塔西甫拉提·特依拜,王宏卫,等.人工智能计算技术在新疆干旱区典型绿洲土壤盐分预测中的应用[J].中国沙漠,2014,34(1):153-161.
doi: 10.7522/j.issn.1000-694X.2013.00294 |
[ Shamsiya·Anwar, Tashpolat·Tiyip, Wang H W, et al. Application of artificial intelligent technique for predicting soil salinity in a Typical oasis of arid area in Xinjiang, China[J]. Journal of Desert Research, 2014,34(1):153-161. ]
doi: 10.7522/j.issn.1000-694X.2013.00294 |
|
[10] | 买买提·沙吾提,塔西甫拉提·特依拜,丁建丽,等.基于GIS的干旱区土壤盐渍化敏感性评价——以渭干河—库车河三角洲绿洲为例[J].资源科学,2012,34(2):353-358. |
[ Mamatsawut, Tashpolat·Tiyip, Ding J L, et al. A GIS-based assessment on sensitivity of soil salinization in arid areas: a case study of the Ugan-Kuqa river delta[J]. Resources Science, 2012,34(2):353-358. ] | |
[11] | 玉素甫江·如素力.新疆焉耆盆地二元水循环过程模拟研究[D].北京:中国科学院大学,2015:27-28. |
[ Yusufujiang R.Research on dual water cycling simulation in Yanqi Basin,Xinjiang[D].Beijing: University of Chinese Academy of Sciences, 2015:27-28. ] | |
[12] | 蔡阿兴,陈章英,蒋正琦,等.我国不同盐渍地区盐分含量与电导率的关系[J].土壤,1997,29(1):54-57. |
[ Cai A X, Chen Z Y, Jiang Z Q, et al.Relationship between salt content and conductivity in different saline areas in China[J].Soils, 1997,29(1):54-57. ] | |
[13] | 韩桂红,塔西甫拉提·特依拜,买买提·沙吾提,等.渭-库绿洲地下水对土壤盐渍化和其逆向演替过程的影响[J].地理科学,2012,32(3):362-367. |
[ Han G H, Tashpolat·Tiyip, Mamatsawt, et al. Influence of groundwater on soil salinization and its reversal evolvement in Wei-ku oasis[J].Scientia Geographica Sinica, 2012,32(3):362-367. ] | |
[14] | 王水献,周金龙,董新光.地下水浅埋区土壤水盐试验分析[J].新疆农业大学学报,2004,27(3):52-56. |
[ Wang S X, Zhou J L, Dong X G.Experimental analysis on the soil water and salt dynamic variation in shallow groundwater areas[J]. Journal of Xinjiang Agricultural University, 2004,27(3):52-56. ] | |
[15] |
塔西甫拉提·特依拜,阿布都瓦斯提·吾拉木.绿洲——荒漠交错带地下水位分布的遥感模型研究[J].遥感学报,2002,6(4):299-306.
doi: 10.3321/j.issn:1007-4619.2002.04.011 |
[ Tashpolat·Tiyip, Abduwasit·Ghulam. Research on model of groundwater level distribution in the oasis and desert ecotone using remote sensing[J]. Journal of Remote Sensing, 2002,6(4):299-306. ]
doi: 10.3321/j.issn:1007-4619.2002.04.011 |
|
[16] |
哈学萍,丁建丽,塔西甫拉提·特依拜,等.基于SI-Albedo特征空间的干旱区盐渍化土壤信息提取研究——以克里雅河流域绿洲为例[J].土壤学报,2009,46(3):381-390.
doi: 10.3321/j.issn:0564-3929.2009.03.002 |
[ Ha X P, Ding J L, Tashpolat·Tiyip, et al. SI-albedo space-based extraction of salinization information in arid area[J]. Acta Pedologica Sinica, 2009,46(3):381-390. ]
doi: 10.3321/j.issn:0564-3929.2009.03.002 |
|
[17] | 郭亮,沈志,申旭辉,等.干旱区盐渍地土壤含水量分布及其光谱特征研究[J].安徽农业科学,2015,43(15):102-106. |
[ Guo L, Shen Z, Shen X H, et al.Study on soil moisture distribution and spectral characteristics of soil in arid region[J]. Journal of Anhui Agricature, 2015,43(15):102-106. ] | |
[18] | 姜红,玉素甫江·如素力,热伊莱·卡得尔,等.不同空间插值方法对博斯腾湖水体矿化度的适应性评价研究[J].新疆师范大学学报(自然科学版),2016,35(4):7-14. |
[ Jiang H, Yusufujiang·Rusuli, Reyilai·Kadeer, et al. Study on suitability of different interpolation methods for evaluation of water salinity in Bosten Lake[J]. Journal of Xinjiang Normal University(Natural Sciences Edition), 2016,35(4):7-14. ] | |
[19] |
Sandholt I, Rasmussen K, Andersen J.A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J]. Remote Sensing of Environment, 2002,79(2):213-224.
doi: 10.1016/S0034-4257(01)00274-7 |
[20] |
陈艳华,张万昌.植被类型对温度植被干旱指数(TVDI)的影响研究——以黑河绿洲区为例[J].遥感技术与应用,2007,22(6):700-706.
doi: 10.3969/j.issn.1004-0323.2007.06.006 |
[ Chen Y H, Zhang W C.Evaluating effects of vegetationtypes on temperature vegetation drought index(TVDI) in the Heihe oasis region[J]. Remote Sensing Technology and Application, 2007,22(6):700-706. ]
doi: 10.3969/j.issn.1004-0323.2007.06.006 |
|
[21] |
Qi J, Chehbouni A, Huete A R, et al.A modified soil adjusted vegetation index[J]. Remote Sensing of Environment, 1994,48(2):119-126.
doi: 10.1016/0034-4257(94)90134-1 |
[22] | 文军,王介民.一种由卫星遥感资料获得的修正的土壤调整植被指数[J].气候与环境研究,1997,2(3):302-309. |
[ Wen J, Wang J M. A modified soil-adjusted vegetation index obtained from satellite remote sensing data[J]. Climatic and Environmental Ressarch, 1997,2(3):302-309. ] | |
[23] | 覃志豪,Zhang M H, Amon K,等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报,2001,56(4):456-466. |
[ Qin Z H, Zhang M H, Amon K, et al.Mono-window algorithm for retrieving land surface temperature from landsat TM6 data[J]. Acta Geographica Sinica, 2001,56(4):456-466. ] | |
[24] |
楼琇林,黄韦艮.基于人工神经网络的赤潮卫星遥感方法研究[J].遥感学报,2003,7(2):125-130.
doi: 10.3321/j.issn:1007-4619.2003.02.008 |
[ Lou X L, Huang W G.An artificial neural network method for detecting red tides with NOAA AVHRR imagery[J]. Journal of Remote Sensing, 2003,7(2):125-130. ]
doi: 10.3321/j.issn:1007-4619.2003.02.008 |
|
[25] |
Dreyer P.Classification of land cover using optimized neural nets on SPOT data[J]. Photogrammetric Engineering & Remote Sensing, 1993,59(5):617-621.
doi: 10.1016/0031-0182(93)90010-G |
[26] |
Zhang Y Z, Pullianinen J, Koponen S, et al.Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data[J]. Remote Sensing of Environment, 2002,81(2/3):327-336.
doi: 10.1016/S0034-4257(02)00009-3 |
[27] | 新疆农业厅,新疆普查办公室.新疆土壤[M].北京:科学出版社,1996:151-521. |
[ Xinjiang department of agricultural, Xinjiang soil survey office eds. The soil of Xinjiang. Beijing: Science Press, 1996:151-521. ] |
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