Estimating the Leaf Area Index, height and biomass of maize using HJ-1 and RADARSAT-2

Gao, Shuai; Niu, Zheng; Huang, Ni; Hou, Xiudan

doi:10.1016/j.jag.2013.02.002

Cited by 125 publications

(99 citation statements)

References 29 publications

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“…The results of OSVIs showed that the least-correlated with LAI and biomass was RVI1. Our results were consistent with the study of Gao et al [24]. In contrast, OSAVI, SAVI, and NDVI were very sensitive to low LAI (LAI < 3) and were saturated at medium to high LAI values (LAI > 3) [16,[50][51][52].…”

Section: Discussionsupporting

confidence: 92%

“…The combination of optical and SAR image data has been used for the estimation of the LAI and biomass of crops and forests, and the results have shown that the estimated values agree well with the actual values [27,[35][36][37]. Gao et al estimated the LAI, height, and biomass of maize using single-temporal environment and disaster monitoring satellite constellation (Huanjing (HJ)-1A/B) and RADARSAT-2; the results showed that this integrated method of determining VIs were well correlated with the LAI, height, and biomass near the maize heading stage [24]. However, few studies have combined the optical and SAR data based on multi-temporal images for estimating the LAI and biomass of winter wheat.…”

Section: Introductionmentioning

confidence: 78%

“…However, SAR images are limited by the technique's imaging geometry and radiation mechanism [24]. Several SARs have been launched, such as ALOS-PALSAR (Japan), TerraSAR-X (Germany), Sentinel 1 (European Space Agency), and Radarsat1 and 2 (Canada).…”

Section: Introductionmentioning

confidence: 99%

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Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

et al. 2015

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Leaf area index (LAI) and biomass are frequently used target variables for agricultural and ecological remote sensing applications. Ground measurements of winter wheat LAI and biomass were made from March to May 2014 in the Yangling district, Shaanxi, Northwest China. The corresponding remotely sensed data were obtained from the earth-observation satellites Huanjing (HJ) and RADARSAT-2. The objectives of this study were (1) to investigate the relationships of LAI and biomass with several optical spectral vegetation indices (OSVIs) and radar polarimetric parameters (RPPs), (2) , p < 0.01) were highly correlated with biomass. The estimation accuracy of LAI and biomass was better using the COSVI-RPPs than using the OSVIs and RPPs alone. The results revealed that the PLSR regression equation better estimated LAI and biomass than the MSR regression equation based on all the COSVI-RPPs, OSVIs, and RPPs. Our results indicated that the COSVI-RPPs can be used to robustly estimate LAI and biomass. This study may provide a guideline for improving the estimations of LAI and biomass of winter wheat using multisource remote sensing data.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 78%

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Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

et al. 2015

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“…For C-band, canopy volume scattering dominates in the backward scattering. The crosspolarization data is directly related to volume scattering of rice canopy while the co-polarization one is related to doublebounce and scattering from ground of rice plants, so HV data was more highly correlated to rice structure parameters than HH data [10] . had demonstrated that HV or VH was more suitable than the HH and VV data for retrieving rice parameters, such as rice biomass or height.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, the input vector contains the backscattering coefficients of rice, and the output vector contains the rice height, moisture content and biomass measured in rice field. Recent studies have shown that the backscattering coefficient is sensitive to crop biomass and dielectric property [8][9][10][11] . In general, the rice biomass is positively related to canopy height, and training sample set is produced based on the positive correlation between the two.…”

Section: Study Area and Datamentioning

confidence: 99%

Estimating Paddy Rice Biomass Using Radarsat-2 Data Based on Artificial Neural Network

Jing¹,

Zhang²,

Wang³

2013

Proceedings of the 2013 International Conference on Remote Sensing,Environment and Transportation Engineering

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Abstract-Accurate estimation of rice biomass plays a role in crop growth monitoring and agricultural carbon cycle. Dualpolarization Radarsat-2 data were used in this study for rice biomass inversion. Artificial neural network (ANN) method has been applied to simulate the relation between rice canopy parameters (height, moisture content and biomass etc.) and radar backscattering coefficients. By comparing the network training performance and RMSE of different polarization combinations, HV data had better accuracy which was selected as input of the network for biomass inversion. The distribution of rice biomass was also mapped at regional scale. This study indicated that Radarsat-2 SAR data could be used to estimate rice biomass with the root mean square error (RMSE) of <108 g/m 2 .

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Relationship between aerodynamic roughness length and bulk sedge leaf area index in a mixed‐species boreal mire complex

Alekseychik

Korrensalo

Mammarella

et al. 2017

Geophysical Research Letters

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Leaf area index (LAI) is an important parameter in natural ecosystems, representing the seasonal development of vegetation and photosynthetic potential. However, direct measurement techniques require labor‐intensive field campaigns that are usually limited in time, while remote sensing approaches often do not yield reliable estimates. Here we propose that the bulk LAI of sedges (LAIs) can be estimated alternatively from a micrometeorological parameter, the aerodynamic roughness length for momentum (z0). z0 can be readily calculated from high‐response turbulence and other meteorological data, typically measured continuously and routinely available at ecosystem research sites. The regressions of LAI versus z0 were obtained using the data from two Finnish natural sites representative of boreal fen and bog ecosystems. LAIs was found to be well correlated with z0 and sedge canopy height. Superior method performance was demonstrated in the fen ecosystem where the sedges make a bigger contribution to overall surface roughness than in bogs.

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Estimating the Leaf Area Index, height and biomass of maize using HJ-1 and RADARSAT-2

Cited by 125 publications

References 29 publications

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

Estimating Paddy Rice Biomass Using Radarsat-2 Data Based on Artificial Neural Network

Relationship between aerodynamic roughness length and bulk sedge leaf area index in a mixed‐species boreal mire complex

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