| 侯学会，王 猛，高 帅，隋学艳，梁守真.综合近红外-红波段-短波红外三波段光谱特征空间的小麦冠层含水量反演[J].麦类作物学报,2020,(7):866 |
| 综合近红外-红波段-短波红外三波段光谱特征空间的小麦冠层含水量反演 |
| A New Method Monitoring Water Content in Wheat Canopy Based on Nir-Red-Swir Spectral Space from Landsat 8 OLI Data |
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| DOI：10.7606/j.issn.1009-1041.2020.07.12 |
| 中文关键词: Nir-Red-Swir光谱特征空间 三波段垂直植被水分指数 小麦冠层含水量 植被覆盖度 Landsat 8 OLI |
| 英文关键词:Nir-Red-Swir spectral space Three-band perpendicular vegetation water index Wheat canopy water content Coverage Landsat 8 OLI |
| 基金项目:山东省农业科学院创新工程项目（CXGC2018E12）；国家重大研发计划项目（2017YFA0603004） |
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| 中文摘要: |
| 为提高返青期-拔节期-开花期-灌浆期不同覆盖条件下小麦冠层含水量的遥感反演精度，综合分析基于Nir-Red和Nir-Swir光谱特征空间开展作物含水量监测的优势与局限，利用垂直干旱指数（perpendicular drought index，PDI）和短波红外垂直失水指数（shortwave infrared perpendicular water stress index，SPSI）的比值形式，构建了一种基于近红外-红波段-短波红外（Nir-Red-Swir）三波段光谱特征空间的垂直植被水分指数（three-band perpendicular vegetation water index，TPVWI）。结果表明，在不同生育时期，TPVWI与小麦冠层含水量（vegetation water content，VWC)均具有显著相关关系（P<0.01），且对植被含水量的敏感性优于PDI、作物水分监测指数（plant water index，PWI）、SPSI和NDVI 4种植被指数，且在反映小区域内小麦冠层含水量的时空趋势上有较好的表征能力。对比地面实测数据，利用TPVWI建立的作物含水量估测模型的预测精度较高，r与RMSE分别为0.763和2.296%，说明利用综合Nir-Red-Swir三波段光谱空间特征的植被水分指数在监测不同覆盖条件下的作物含水量具有一定的可行性，可丰富当前作物冠层含水量遥感监测的理论方法。 |
| 英文摘要: |
| In order to inverse the wheat canopy water content (VWC) under different covering conditions using remote sensing data at different growth stages, such as returning, jointing, flowering, andgrain filling stage, a new index termed three-band perpendicular vegetation water index (TPVWI) was found to estimate vegetation water content of winter wheat using the ratio of perpendicular drought index (PDI) and short wave infrared perpendicular water stress index (SPSI), according to the advantages and limitations of Nir-Red and Nir-Swir spectral space in monitoring crop water content. Results showed that TPVWI had a significantly negative correlation (P<0.01) with VWC at all growth stages, and its relationship with VWC was better than that of PDI, plant water index (PWI), SPSI and normalized difference vegetation index (NDVI). The TPVWI also has a good quality in analysing the spatial-temporal pattern of the VWC of wheat in a small area. Verification with ground measured data, vegetation water model established based on TPVWI has higher accuracy, and r and RMSE were 0.763 and 2.296%, respectively. The results indicated that TPVWI is certainly feasible to estimate crop water content under different fraction covering conditions of wheat and it provides a new method for monitoring crop water content using remote sensing data. |
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