枯落层盖度的定量估算对于植被防蚀功效的评价具有重要作用。 在陕北黄土丘陵沟壑区选取典型落叶林和退耕草地作为光谱测试样地, 通过室内和野外实验分析枯落层—土壤混合场景的光谱变化规律, 研究高光谱指数NDLI(normalized difference lignin index)和CAI(cellulose absorption index)估算枯落层盖度的有效性。 结果表明, 受颜色及含水量等因素的影响, 不同盖度枯落层—土壤混合场景的光谱反射率呈现不同的变化规律。 干和湿状态下, 随着枯落层盖度的增加混合场景的NDLI、 CAI指数值均呈现增大的趋势, CAI与枯落层盖度的相关性高于NDLI, R2最高为0.98(阔叶林和退耕草地干的混合场景), CAI能够更有效地估算枯落层盖度。 野外实测验证了室内实验结果, 退耕草地混合场景的CAI值与枯落层盖度相关性最高(R2=0.90), 野外条件下两种高光谱指数估算枯落层盖度的有效性均有一定程度的降低。 该研究为枯落层盖度的遥感定量反演提供了基础与依据。

Quantifying plant litter cover is important to evaluate the effectiveness of vegetation to protect soil against soil erosion. Field campaign was carried out in the Loess Hilly-gully Region of Northern Shaanxi to acquire the spectral reflectance data of plant-soil mixed scenes for two broadleaf forests and rehabilitated grassland. Spectral behavior of plant-soil mixed scenes was analyzed and the effectiveness of hyperspectral indexes NDLI (normalized difference lignin index) and CAI (cellulose absorption index) for quantifying plant litter cover was evaluated based on lab and field experiments. The results showed spectra reflectance of plant-soil mixed scenes with different proportions of litter and soil varied greatly by the influence of color and water content. The NDLI and CAI values increased with the rise of proportion of litter within the mixed scene under dry and wet status, regression analysis between the two indexes and plant litter cover of mixed scenes showed that the obviously better result for CAI (R2=0.98, rehabilitated grassland and broadleaf forests under dry status). The discrimination between mixed scenes and soil using CAI significantly outperformed NDLI under dry status; the estimation of plant litter cover by CAI is more effective compared to NDLI. Lab analysis was validated by field measuring: R2=0.90 showed highest correlation between CAI and plant litter cover for rehabilitated grassland. The validity of estimation of plant litter cover by both two indexes reduced to some degree in the field. The research enhanced the scientific basis for remote sensing estimation of plant litter cover.