高光谱遥感土壤湿度信息提取研究

EXTRACTION OF SOIL MOISTURE INFORMATION BY HYPERSPECTRAL REMOTE SENSING

精准农作管理中土壤水分、土壤养分等的空间信息分布 ,可通过高光谱遥感传感器获得。本文通过对土壤的光谱反射率与土壤的表面湿度进行分析 ,比较 5种方法在反演土壤表面湿度的能力 ,并对小汤山精准农业试验区的土壤表面湿度进行高光谱填图 ,建立了较为精细的土壤水分空间分布图 ,对高光谱遥感在精准农业中深入应用进行了有效探索。

Development of precision farming calls urgently for remote sensing techniques capable of providing timely accurate ground information. Estimation of soil moisture from reflectance measurements in the solar spectral domain (400～2 500 nm) was investigated. For this purpose, 18 soils representing a large range of permanent characteristics were gathered for the test. Reflectance data were measured in the laboratory during the soil drying process with a high spectral resolution spectroradiometer. Five approaches were compared. The first one was based on single-band reflectance and on calibration of the reflectance data by the reflectance of the corresponding soil under dry conditions, the second and the third approaches on either reflectance derivatives or absorbance derivatives and the fourth and fifth approaches on differences between reflectance and absorbance observed in two non-consecutive bands.In the first step, the relationships were calibrated over half the dataset (nine soils) with emphasis on selection of the most pertinent spectral bands. Results showed that, for the first approach, the bands corresponding to the highest water absorption capacities (1 944 nm) yielded the best soil moisture retrieval performance. For the second and third approaches, the bands corresponding to sharp edges of the water absorption features performed better (1 834 nm for the reflectance derivatives and 1 622 nm for the absorbance derivatives). The fourth and fifth approaches could be considered as a generalization of the derivative approach when bands were no longer consecutive. The best performance was achieved when the bands were not too far apart. The best overall retrieval performances were achieved with the absorbance derivatives and the absorbance difference, confirming the non-linear character of the relationship between soil moisture and reflectance.The previously calibrated relations were tested against the evaluation dataset obtained from the nine remaining soils. The results showed additionally that calibration of the reflectance values by that observed under dry conditions was only partly minimizing impact of soil type. The best performances for the lowest soil moisture values (< 0.20 g cm -3) were obtained with the reflectance derivatives. However, because of the non-linear behaviour for the highest soil moisture values, the absorbance derivatives and absorbance difference provided the best estimation of these moisture levels. With the relations set between reflectance and moisture, soil moisture was conversed from airborne remote sensing images.