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| DOI：10.13522/j.cnki.ggps.20180062 |
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| Estimating Soil Moisture Distribution in Winter Wheat Field Using SOC710VP Hyperspectral Imagery |
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LIU Xiaojing, CHEN Guoqing*, WANG Liang, CHEN Yujie, WANG Lan, LIU Xiaoyu, LI Xueguo
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State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, Agronomy College, Shandong Agricultural University, Tai’an 271018, China
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| Abstract: |
| 【Objective】Soil water controls crop growth and many soil physical and biochemical processes, and the purpose of this paper is to present how hyperspectral imagery can be used to estimate soil moisture distribution rapidly at large scale.【Method】 Based on hyperspectral data of the canopy of winter wheat, we calculated eight vegetation indices and then linked them to soil water content at different depths (0~20, 20~40, 40~60 cm) during key growth stages (jointing stage, heading stage, filling stage) of a winter wheat field.【Result】①The fitting between soil moisture and the vegetation indices varied with growth season. At jointing stage, the indices VOG1, mNDVI705 and VOG3 were superior, whereas at heading and filling stages, mNDVI705 and mSR705, and mNDVI705 and SARVI worked better, respectively. ②The fitting between soil water content and vegetation indices varied with soil depth as well. For the 0~20 cm soil, the model using VOG1 and mNDVI705 gave the best result with the coefficient of determination (R2) being 0.743, while for 20~40 cm soil, the model using mNDVI705 and SARVI was most accurate with R2 being 0.707. For the soil in 40~60 cm, the best vegetation indices for estimating the moisture was VOG3, mSR705 and SARVI, with R2 being 0.484. ③It was found that the fitting of the model for 0~20 cm soil was superior to that for 20~40 cm and 40~60 cm soil.【Conclusion】 Using VOG1 and mNDVI705 indices calculated from the hyperspectral imagery can estimate the moisture in 0~20 cm soil reasonably well, and can thus help improve irrigation design and water resource management at regional and catchment scales. |
| Key words: hyperspectral remote sensing; vegetation index; retrieval; winter wheat; soil water content |
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