光谱学与光谱分析, 2016, 36 (5): 1378, 网络出版: 2016-12-20  

ADI土壤水分反演方法

Soil Moisture Monitoring Based on Angle Dryness Index
作者单位
1 北京大学遥感与地理信息系统研究所, 北京 100871
2 中国交通通信信息中心, 北京 100011
3 地理信息基础软件与应用国家测绘信息局工程技术研究中心, 北京 100871
摘要
土壤水分是影响植被、 土壤和大气之间能量和水分循环的重要因素, 及时准确获取土壤湿度信息有利于提高作物估产精度和改善田间管理措施。本文基于红光与近红外光谱特征空间(NIR-RED)发展了一种新型土壤水分遥感监测模型ADI(angle dryness index), 提高了可见光与近红外波段监测土壤水分的精度。经过研究表明, 在红光与近红外(NIR-RED)特征空间中, 存在一个中间角度变量θ, 利用光谱反射率与土壤水分之间的经验关系式模型以及混合像元分解公式证明该变量能够表征土壤湿度情况, 而不受植被覆盖度的影响, 因此利用该原理构建了ADI方法。最后利用两组遥感数据(分别为TM5与MODIS产品数据)以及对应的地面观测数据进行验证, 结果表明计算值与实测值均具有较高的一致性, R2分别达到0.74与0.64。同时, 将MPDI的计算结果与实测值进行了比较, 两组数据的R2均小于0.60, 表明ADI方法的计算精度高于MPDI。在MPDI的计算过程中用到了植被覆盖度, 这可能是引起计算结果误差的主要因素。此外, MPDI的计算结果表征土壤湿度的相对值, 而ADI则能定量的获取土壤水分含量。MODIS像元除了具有植被与土壤两个端元, 还有其他类型端元的概率高于TM数据, 因而MODIS数据的计算精度低于TM。因此, ADI是一种简单可行且具有较大应用前景的土壤水分反演方法, 适合于推广应用。
Abstract
Soil moisture content (SMC) is one of the most important indicators influencing the exchange of energy and water among vegetation, soil, and the atmosphere. Accurate detection of soil moisture content is beneficial to improving the precision of crop yield evaluating and field management measures. In this paper, a novel method ADI (Angle Dryness Index) based on NIR-RED spectral feature space used for calculating SMC was proposed, which improved the accuracy of calculating SMC with red and near infrared band reflectance. It was found that an intermediate parameter θ in NIR-RED feature space was significantly related to SMC, and independent of vegetation coverage according to the linear decomposition of mixed pixel and the empirical correlation between SMC and red/NIR band reflectance which were achieved by previous researches. Then, ADI was proposed with the feature discovered in the paper. The mathematical expression on SMC is nonlinear, and the newton iterative method is applied to ADI for calculation SMC. Then, the newly proposed method was validated with two kinds of remote sensing imagery data (Thematic Mapper (TM) and moderate resolution imaging spectrometer (MODIS)) and the synchronous observed data in the field. Validation results revealed that the ADI-derived SMC was highly accordant with the in-situ results with high correlation (R2=0.74 with TM and R2=0.64 with MODIS data). We also calculated MPDI (Modified Perpendicular Drought Index) developed by Ghulam, which is also proposed with the red and near infrared reflectance. The result showed that the accuracy of MPDI was lower than that of ADI. The most likely reason was that ADI was insensitive to fv, but the calculation errors of fv would reduce the accuracy of SMC estimation. MODIS had a low spatial resolution, thus there may be more than two end members in a mixed pixel. In this case, the linear decomposition of mixed pixel was not applicable and the errors would finally be enlarged. ADI achieved good results in monitoring SMC in vegetated area because it was less influenced by vegetation coverage than other similar approaches. ADI only requires the satellite image data including the red and near infrared band which are available from most of the optical sensors. Therefore, it is an effective and promising method for monitoring SMC in vegetated area, and would be widely used in agriculture, meteorology, and hydrology.

高中灵, 王建华, 郑小坡, 孙越君, 秦其明. ADI土壤水分反演方法[J]. 光谱学与光谱分析, 2016, 36(5): 1378. GAO Zhong-ling, WANG Jian-hua, ZHENG Xiao-po, SUN Yue-jun, QIN Qi-ming. Soil Moisture Monitoring Based on Angle Dryness Index[J]. Spectroscopy and Spectral Analysis, 2016, 36(5): 1378.

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