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• W3048588337 startingPage "2564" @default.
• W3048588337 abstract "Crop biomass is a critical variable to make sound decisions about field crop monitoring activities (fertilizers and irrigation) and crop productivity forecasts. More importantly, crop biomass estimations by components are essential for crop growth monitoring as the yield formation of crops results from the accumulation and transportation of substances between different organs. Retrieval of crop biomass from synthetic aperture radar SAR or optical imagery is of paramount importance for in-season monitoring of crop growth. A combination of optical and SAR imagery can compensate for their limitations and has exhibited comparative advantages in biomass estimation. Notably, the joint estimations of biophysical parameters might be more accurate than that of an individual parameter. Previous studies have attempted to use satellite imagery to estimate aboveground biomass, but the estimation of biomass for individual organs remains a challenge. Multi-target Gaussian process regressor stacking (MGPRS), as a new machine learning method, can be suitably utilized to estimate biomass components jointly from satellite imagery data, as the model does not require a large amount of data for training and can be adjusted to the required degrees of relationship exhibited by the given data. Thus, the aim of this study was to estimate the biomass of individual organs by using MGPRS in conjunction with optical (Sentinel-2A) and SAR (Sentinel-1A) imagery. Two hybrid indices, SAR and optical multiplication vegetation index (SOMVI) and SAR and optical difference vegetation index (SODVI), have been constructed to examine their estimation performance. The hybrid vegetation indices were used as input for the MGPRS and single-target Gaussian process regression (SGPR). The accuracy of the estimation methods was analyzed by in situ measurements of aboveground biomass (AGB) and organ biomass conducted in 2018 and 2019 over the paddy rice fields of Xinghua in Jiangsu Province, China. The results showed that the combined indices (SOMVI and SODVI) performed better than those derived from either the optical or SAR data only. The best predictive accuracy was achieved by the MGPRS using SODVI as input (r2 = 0.84, RMSE = 0.4 kg/m2 for stem biomass; r2 = 0.87, RMSE = 0.16 kg/m2 for AGB). This was higher than using SOMVI as input for the MGPRS (r2 = 0.71, RMSE = 1.12 kg/m2 for stem biomass; r2 = 0.71, RMSE = 0.56 kg/m2 for AGB) or SGPR (r2 = 0.63, RMSE = 1.08 kg/m2 for stem biomass; r2 = 0.67, RMSE = 1.08 kg/m2 for AGB). Relatively, higher accuracy for leaf biomass was achieved using SOMVI (r2 = 0.83) than using SODVI (r2 = 0.73) as input for MGPRS. Our results demonstrate that the combined indices are effective by integrating SAR and optical imagery and MGPRS outperformed SGPR with the same input variable for estimating rice crop biomass. The presented workflow will improve the estimation of crops biomass components from satellite data for effective crop growth monitoring." @default.
• W3048588337 created "2020-08-18" @default.
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• W3048588337 date "2020-08-09" @default.
• W3048588337 modified "2023-10-18" @default.
• W3048588337 title "Estimation of Canopy Biomass Components in Paddy Rice from Combined Optical and SAR Data Using Multi-Target Gaussian Regressor Stacking" @default.
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• W3048588337 cites W1964075343 @default.
• W3048588337 cites W1967135612 @default.
• W3048588337 cites W1976505356 @default.
• W3048588337 cites W1985555755 @default.
• W3048588337 cites W1986667913 @default.
• W3048588337 cites W1986812364 @default.
• W3048588337 cites W1987997875 @default.
• W3048588337 cites W1996091271 @default.
• W3048588337 cites W2000860904 @default.
• W3048588337 cites W2001467506 @default.
• W3048588337 cites W2003337584 @default.
• W3048588337 cites W2016571835 @default.
• W3048588337 cites W2021711669 @default.
• W3048588337 cites W2029738218 @default.
• W3048588337 cites W2032963712 @default.
• W3048588337 cites W2035021121 @default.
• W3048588337 cites W2036003376 @default.
• W3048588337 cites W2041139590 @default.
• W3048588337 cites W2058848462 @default.
• W3048588337 cites W2060426168 @default.
• W3048588337 cites W2062781596 @default.
• W3048588337 cites W2062979841 @default.
• W3048588337 cites W2067703153 @default.
• W3048588337 cites W2077707413 @default.
• W3048588337 cites W2087070363 @default.
• W3048588337 cites W2096670339 @default.
• W3048588337 cites W2097993678 @default.
• W3048588337 cites W2119582019 @default.
• W3048588337 cites W2122347864 @default.
• W3048588337 cites W2130670721 @default.
• W3048588337 cites W2139712007 @default.
• W3048588337 cites W2141135204 @default.
• W3048588337 cites W2142213787 @default.
• W3048588337 cites W2145539952 @default.
• W3048588337 cites W2152863443 @default.
• W3048588337 cites W2157005989 @default.
• W3048588337 cites W2167787089 @default.
• W3048588337 cites W2167881994 @default.
• W3048588337 cites W2171369471 @default.
• W3048588337 cites W2313063557 @default.
• W3048588337 cites W2400974733 @default.
• W3048588337 cites W2487649765 @default.
• W3048588337 cites W2566965661 @default.
• W3048588337 cites W2598497520 @default.
• W3048588337 cites W2768035654 @default.
• W3048588337 cites W2771011869 @default.
• W3048588337 cites W2795780933 @default.
• W3048588337 cites W2810444994 @default.
• W3048588337 cites W2883857478 @default.
• W3048588337 cites W2887303328 @default.
• W3048588337 cites W2890611227 @default.
• W3048588337 cites W2900971935 @default.
• W3048588337 cites W2903885536 @default.
• W3048588337 cites W2914993640 @default.
• W3048588337 cites W2922011595 @default.
• W3048588337 cites W2928790886 @default.
• W3048588337 cites W2943149585 @default.
• W3048588337 cites W2947850171 @default.
• W3048588337 cites W2950674916 @default.
• W3048588337 cites W2959046283 @default.
• W3048588337 cites W2964553263 @default.
• W3048588337 cites W2974230642 @default.
• W3048588337 cites W2978215673 @default.
• W3048588337 cites W2984811207 @default.
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• W3048588337 doi "https://doi.org/10.3390/rs12162564" @default.
• W3048588337 hasPublicationYear "2020" @default.
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