摘要: |
【目的】面向现代农业生产和管理的数据需求,基于ACRM冠层反射率模型,探索适于冬小麦叶面积指数(LAI)和叶片叶绿素含量(LCC)反演的波段选择方案。【方法】首先,考虑高光谱数据降维和CR模型模拟误差,选出覆盖蓝、绿、红与近红外的5个波段(波段选择方案B1),开展LAI与LCC同步反演。进而,分别选择LAI和LCC的敏感波段,开展了对应参数的反演试验。【结果】1)基于B1,能够在多数田块实现较为准确的LAI与LCC同步反演(LAI反演值与实测值间决定系数(R2)为0.8604,均方根误差(RMSE)为0.963;LCC反演的R2为0.8141,RMSE为0.0689)。2)仅利用LAI或LCC敏感波段的反演结果与基于B1的反演结果无明显差异。【结论】对比本研究与利用相同数据的前期研究可见:旨在高光谱数据降维与限制CR模型模拟误差的波段选择对LAI反演精度改进作用较为显著。相较而言,仅选用单一目标参数(LAI或LCC)的敏感波段,对反演精度改进并不明显。由此,一方面证实了常规反演方法与面向对象反演法不强调选用单一目标参数敏感波段的合理性;另一方面,并不否定MSDT反演法以及一些相关研究提出的,仅采用单一目标参数敏感波段来开展反演的合理性。 |
关键词: 叶面积指数 叶片叶绿素含量 冠层反射率模型 遥感反演 波段选择 |
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基金项目:四川省应用基础研究项目“基于互联网+多阶段遥感反演的区域水稻参数逐田块监测技术研究”(2017JY0284);四川省省院省校合作项目“基于大数据机器学习与冠层反射率模型结合的水稻叶面积指数提取技术”(2018JZ0054);成都市重点研发支撑计划项目“互联网+机器学习下的农情遥感监测方法与大数据平台”(2019-YF05-01368-SN);四川省应用基础研究项目“星机地协同的若尔盖草地鼠害遥感监测研究”(2017JY0155);四川省财政创新能力提升工程项目“基于冠层反射率模型多阶段反演的逐地块水稻参数采集技术研究”(2017QNJJ-023) |
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Retrieving crop LAI and LCC based on their sensitive bands using the ACRM model |
Liu Ke1, Liu Yongling1, Zhang Min1, Liu Shichuan1, Ren Guoye1, Wu Wenbin2, Li Yuanhong1
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1.Institute of Remote Sensing Application, Sichuan Academy of Agricultural Sciences /Chengdu Branch of Remote Sensing Application Center, Ministry of Agriculture and Rural Affairs;2.Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences
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Abstract: |
[Purpose] Leaf area index (LAI) and leaf chlorophyll content (LCC) are promising variables for decision making in modern agriculture. Using remote sensing data, LAI and LCC can be retrieved simultaneously by inversing canopy reflectance (CR) models. Such methodology is known for its better universality and less dependence on in-situ measurement. It has been stated by many studies that band selection is one of the key issues for retrieving crop variables based on a CR model. Therefore, aimed at monitoring LAI and LCC accurately for modern agriculture, we investigated the schemes of band selection for CR model inversion in this study, with particular attention on constraining the inversion by applying only the sensitive bands of LAI or LCC (i.e. the spectral regions where LAI or LCC dominates the reflectance). [Method] 1) a preliminary band selection was conducted for dimension reduction of hyperspectral data, and for eliminating the bands with significant discrepancies between the simulated and the remotely sensed spectra. This is realized by firstly assuming a combination of 5 bands, covering the spectral regions of blue, green, red and near-inferred. However, the exact bands were undetermined. Secondly, the bands in each spectral region, which achieved the optimum goodness of fitting between the simulated and the observed spectra, were selected. This scheme of band selection is denoted as B1. It was then tested for the simultaneous retrieval of LAI and LCC. 2) Based on B1, relevant studies, and a sensitivity evaluation on ACRM parameters using EFAST (extended Fourier amplitude sensitivity test), the sensitive bands of LAI or LCC were selected respectively, denoted as B2-B5. And then, LAI or LCC was retrieved using their sensitive bands only. [Result] Result shows that, 1) with B1, the LAI and LCC values in most (4 out of 5) fields can be retrieved simultaneously in reasonable accuracies (R2 = 0.8604 and root-mean-square error (RMSE) = 0.963 for LAI, and R2 = 0.8141 and RMSE = 0.0689 for LCC). 2) The accuracies based on the sensitive bands of LAI or LCC showed no significant differences compared with the aforementioned results based on B1. [Conclusion] Comparing this study to former studies using the same dataset, it can be found that band selection, which considering dimension reduction of hyperspectral data and avoiding errors of CR models, brings relevantly significant improvement on the retrieval accuracy of LAI. However, comparatively, it is not so effective to constrain the inversion by using only the sensitive bands of a target variable. This study proved the rationality of conventional and object-based inversion approaches, in which constraining the inversion with only the sensitive bands of a target variable was not emphasized. Nevertheless, the potential of such constrain can neither be negated, according to the result of this study. |
Key words: leaf area index leaf chlorophyll content canopy reflectance model inversion band selection |