|
|
|
| Inversion Models of Soil Organic Matter in Oasis-Desert Ecotone Based on TM Image Reflectance |
| Luan Fuming1,2, Zhang Xiaolei1, Xiong Heigang3, Wang Fang1,2, Zhang Fang4 |
1. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. College of Applied Arts and Science of Beijing Union University, Beijing 100083, China;
4. College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China |
|
|
|
|
Abstract The image spectrum reflectance (R), spectrum reciprocal (1/R), the logarithm of reciprocal spectrum lg(1/R), first derivative reflectance spectrum (FDR) and spectrum band depth (D) of five spectral index were selected to establish soil organic matter (SOM) inversion models, and the response characteristics of TM image of Qitai county are analyzed, the F test was used to validate the model significance. It turns out that: The accuracy value of the inversion models established by the five indicators on the soil layers and different depths SOM content are various, and the order from low to high is: lg(1/R)< R< 1/R< FDR< D. The model accuracy of D is the best for inversion of SOM content, and achieves a good standard for the monitoring of SOM content (10-20 cm). The model accuracy of FDR is good, and 1/R，R are not good. The model accuracy of lg(1/R) is the lowest in predicting SOM content. The precision order of the soil layers models is: 50-60 cm< 40-50 cm< 30-40 cm< 20-30 cm< 0-10 cm< 10-20 cm, and for the different soil depths models is: 0-60 cm< 0-50 cm< 0-40 cm< 0-30 cm< 0-10 cm< 0-20 cm.
|
|
Received: 25 April 2013
Published: 20 July 2014
|
|
|
|
Corresponding Authors:
张小雷（Email：zhangxl@ms.xjb.ac.cn）
E-mail: zhangxl@ms.xjb.ac.cn
|
[1] Alabbas A H,Swain P H,Baumgardner M F.Relating organic matter and clay content to the multispectral radiance of soils[J].Soil Science,1972,114(6):477.
[2] Li M Z,Shibusawa S,Sasao A.Spectroscopic approach to soil parameters sensing Nir:99[C]//Zeng D C,Wang M H.Innovation of Aricultural Engineering Technology for the 21st Century-Proceedings of International Conference on Agricultural Engineering,1999:98-103.
[3] 孙珂,陈圣波.基于遗传算法综合Terra/Aqua MODIS热红外数据反演地表组分温度[J].红外与毫米波学报,2012,35(5):462-468.
[4] 卢艳丽,白由路,杨俐苹.基于主成分回归分析的土壤有机质高光谱预测与模型验证[J].植物营养与肥料学报,2011,17(2):456-463.
[5] 刘树林,王涛,屈建军.浑善达克沙地土地沙漠化过程中土壤粒度与养分变化研究[J].中国沙漠,2008,28(4):611-616.
[6] 王丽霞,段文标,陈立新,等.红松阔叶混交林林隙大小对土壤水分空间异质性的影响[J].应用生态学报,2013,24(1):17-24.
[7] 程朋根,吴剑,李大军,等.土壤有机质高光谱遥感和地统计定量预测[J].农业工程学报,2009,25(3):142-147.
[8] 王静,何挺,李玉环,等.基于高光谱遥感技术的土地质量信息挖掘研究[J].遥感学报,2005,9(4):438-445.
[9] 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[J].应用生态学报,2010,21(4):883-888.
[10] 张文娟,王绍强,常华,等.遥感在土壤碳储量估算中的应用[J].地理科学进展,2005,24(3):118-126.
[11] 周萍,王润生,阎柏琨,等.高光谱遥感土壤有机质信息提取研究[J].地理科学进展,2008,27(5):27-34.
[12] 刘焕军,张柏,刘殿伟,等.松嫩平原典型土壤高光谱定量遥感研究[J].遥感学报,2008,12(4):647-654.
[13] 徐永明,蔺启忠,王璐,等.基于高分辨率反射光谱的土壤营养元素估算模型[J].土壤学报,2006,43(5):709-716.
[14] 熊小刚,韩兴国.内蒙古半干旱草原灌丛化过程中小叶锦鸡儿引起的土壤碳、氮资源空间异质性分布[J].生态学报,2005,25(7):1673-1683.
[15] 崔夺,李玉霖,王新源,等.北方荒漠及荒漠化地区草地地上生物量空间分布特征[J].中国沙漠,2011,31(4):865-811.
[16] 胡忠良,潘根兴,李恋卿,等.贵州喀斯特山区不同植被下土壤C、N、P含量和空间异质性[J].生态学报,2009,29(8):4187-4195.
[17] 穆叶赛尔·吐地,吉力力·阿不都外力,姜逢清.天山北坡东西段林沿土壤有机质含量特征对比分析[J].水土保持研究,2013,20(1):70-75.
[18] 王晶,吕昭智,钱翌,等.新疆准噶尔盆地典型荒漠区不同景观植被对土壤养分的影响[J].中国沙漠,2010,30(6):1367-1374.
[19] 王合玲,张辉国,秦璐,等.新疆艾比湖流域土壤有机质的空间分布特征及其影响因素[J].生态学报,2012,32(16):4969-4980.
[20] 栾福明,张小雷,熊黑钢,等.基于不同模型的土壤有机质含量高光谱反演比较分析[J].光谱学与光谱分析,2013,33(1):196-200.
[21] 沙晋明,陈鹏程,陈松林.土壤有机质光谱响应特性研究[J].水土保持研究,2003,10(2):21-25. |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
2014, Vol. 34 
