Journal
REMOTE SENSING
Volume 9, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/rs9040309
Keywords
LAI retrieval; hyperspectral remote sensing; sampling method; random forests; artificial neural networks; support vector machine
Categories
Funding
- Natural Science Foundation of China [61661136003, 41471285, 41471351]
- National Key Research and Development Program [2016YFD0300602]
- Special Funds for Technology innovation capacity building - the Beijing Academy of Agriculture and Forestry Sciences [KJCX20170423]
- UK Science and Technology Facilities Council through the PAFiC project [ST/N006801/1]
- Science and Technology Facilities Council [ST/N006801/1] Funding Source: researchfish
- STFC [ST/N006801/1] Funding Source: UKRI
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Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI inversion for the whole crop growth period and a single growth period. Random forest (RF), artificial neural network (ANN), and support vector machine (SVM) regression models were compared with a partial least-squares regression (PLS) model. The RF model yielded the highest precision, accuracy, and stability with V-R2, SDR 2, V-RMSE, and SDRMSE values of 0.741, 0.031, 0.106, and 0.005, respectively, over the whole growth period based on STR sampling. The ANN model had the highest precision, accuracy, and stability (0.452, 0.132, 0.086, and 0.009, respectively) over a single growth phase based on STR sampling. The precision, accuracy, and stability of the RF, ANN, and SVM models were improved by inclusion of STR sampling. The RF model is suitable for estimating LAI when sample plots and variation are relatively large (i.e., the whole growth period or more than one growth period). The ANN model is more appropriate for estimating LAI when sample plots and variation are relatively low (i.e., a single growth period).
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