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Shuguo Wang
School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China

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Journal article
Published: 28 October 2020 in Remote Sensing
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The leaf area index (LAI) is an essential indicator used in crop growth monitoring. In the study, a hybrid inversion method, which combined a physical model with a statistical method, was proposed to estimate the crop LAI. The simulated compact high-resolution imaging spectrometer (CHRIS) canopy spectral crop reflectance datasets were generated using the PROSAIL model (the coupling of PROSPECT leaf optical properties model and Scattering by Arbitrarily Inclined Leaves model) and the CHRIS band response function. Partial least squares (PLS) was then used to reduce the dimension of the simulated spectral data. Using the principal components (PCs) of PLS as the model inputs, the hybrid inversion models were built using various modeling algorithms, including the backpropagation artificial neural network (BP-ANN), least squares support vector regression (LS-SVR), and random forest regression (RFR). Finally, remote sensing mapping of the CHRIS data was achieved with the hybrid model to test the inversion accuracy of LAI estimates. The validation result yielded an accuracy of R2 = 0.939 and normalized root-mean-square error (NRMSE) = 6.474% for the PLS_RFR model, which indicated that the crops LAI could be estimated accurately by using spectral feature extraction and a hybrid inversion strategy. The results showed that the model based on principal components extracted by PLS had a good estimation accuracy and noise immunity and was the preferred method for LAI estimation. Furthermore, the comparative analysis results of various datasets showed that prior knowledge could improve the precision of the retrieved LAI, and using this information to constrain parameters (e.g., chlorophyll content or LAI), which make important contributions to the spectra, is the key to this improvement. In addition, among the PLS, BP-ANN, LS-SVR, and RFR methods, RFR was the optimal modeling algorithm in the paper, as indicated by the high R2 and low NRMSE in various datasets.

ACS Style

Liang Liang; Di Geng; Juan Yan; Siyi Qiu; Liping Di; Shuguo Wang; Lu Xu; Lijuan Wang; Jianrong Kang; Li Li. Estimating Crop LAI Using Spectral Feature Extraction and the Hybrid Inversion Method. Remote Sensing 2020, 12, 3534 .

AMA Style

Liang Liang, Di Geng, Juan Yan, Siyi Qiu, Liping Di, Shuguo Wang, Lu Xu, Lijuan Wang, Jianrong Kang, Li Li. Estimating Crop LAI Using Spectral Feature Extraction and the Hybrid Inversion Method. Remote Sensing. 2020; 12 (21):3534.

Chicago/Turabian Style

Liang Liang; Di Geng; Juan Yan; Siyi Qiu; Liping Di; Shuguo Wang; Lu Xu; Lijuan Wang; Jianrong Kang; Li Li. 2020. "Estimating Crop LAI Using Spectral Feature Extraction and the Hybrid Inversion Method." Remote Sensing 12, no. 21: 3534.

Journal article
Published: 26 November 2016 in Remote Sensing
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Validation is mandatory to quantify the reliability of remote sensing products (RSPs). However, this process is not straightforward and usually presents formidable challenges in terms of both theory and real-world operations. In this context, a dedicated validation initiative was launched in China, and we identified a validation strategy (VS). This overall VS focuses on validating regional-scale RSPs with a systematic site-to-network concept, consisting of four main components: (1) general guidelines and technical specifications to guide users in validating various land RSPs, particularly aiming to further develop in situ sampling schemes and scaling approaches to acquire ground truth at the pixel scale over heterogeneous surfaces; (2) sound site-based validation activities, conducted through multi-scale, multi-platform, and multi-source observations to experimentally examine and improve the first component; (3) a national validation network to allow for comprehensive assessment of RSPs from site or regional scales to the national scale across various zones; and (4) an operational RSP evaluation system to implement operational validation applications. Research progress on the development of these four components is described in this paper. Some representative research results, with respect to the development of sampling methods and site-based validation activities, are also highlighted. The development of this VS improves our understanding of validation issues, especially to facilitate validating RSPs over heterogeneous land surfaces both at the pixel scale level and the product level.

ACS Style

Shuguo Wang; Xin Li; Yong Ge; Rui Jin; Mingguo Ma; Qinhuo Liu; Jianguang Wen; Shaomin Liu. Validation of Regional-Scale Remote Sensing Products in China: From Site to Network. Remote Sensing 2016, 8, 980 .

AMA Style

Shuguo Wang, Xin Li, Yong Ge, Rui Jin, Mingguo Ma, Qinhuo Liu, Jianguang Wen, Shaomin Liu. Validation of Regional-Scale Remote Sensing Products in China: From Site to Network. Remote Sensing. 2016; 8 (12):980.

Chicago/Turabian Style

Shuguo Wang; Xin Li; Yong Ge; Rui Jin; Mingguo Ma; Qinhuo Liu; Jianguang Wen; Shaomin Liu. 2016. "Validation of Regional-Scale Remote Sensing Products in China: From Site to Network." Remote Sensing 8, no. 12: 980.