Rape (Brassica napus L.) Growth Monitoring and Mapping Based on Radarsat-2 Time-Series Data

Zhang, Wangfei; Chen, Erxue; Li, Zengyuan; Zhao, Lei; Ji, Yongjie; Zhang, Yahong; Liu, Zhiqin

doi:10.3390/rs10020206

Cited by 14 publications

(7 citation statements)

References 32 publications

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“…Rate of emergence and spring survival were also estimated in wheat through UAV-based multispectral imagery [25]. Moreover, Satellite-based vegetation data have also been used for crop growth monitoring [26][27][28]. Recently, UAV-based vegetation indices such as the enhanced vegetation index (EVI) and the normalized difference red edge (NDRE) were estimated to evaluate physiological status of wheat and sorghum crops during maturation under drought conditions [8,26].…”

Section: Introductionmentioning

confidence: 99%

Time-Series Multispectral Indices from Unmanned Aerial Vehicle Imagery Reveal Senescence Rate in Bread Wheat

et al. 2018

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Detection of senescence's dynamics in crop breeding is time consuming and needs considerable details regarding its rate of progression and intensity. Normalized difference red-edge index (NDREI) along with four other spectral vegetative indices (SVIs) derived from unmanned aerial vehicle (UAV) based spatial imagery, were evaluated for rapid and accurate prediction of senescence. For this, 32 selected winter wheat genotypes were planted under full and limited irrigation treatments. Significant variations for all five SVIs: green normalize difference vegetation index (GNDVI), simple ratio (SR), green chlorophyll index (GCI), red-edge chlorophyll index (RECI), and normalized difference red-edge index (NDREI) among genotypes and between treatments, were observed from heading to late grain filling stages. The SVIs showed strong relationship (R 2 = 0.69 to 0.78) with handheld measurements of chlorophyll and leaf area index (LAI), while negatively correlated (R 2 = 0.75 to 0.77) with canopy temperature (CT) across the treatments. NDREI as a new SVI showed higher correlations with ground data under both treatments, similarly as exhibited by other four SVIs. There were medium to strong correlations (r = 0.23-0.63) among SVIs, thousand grain weight (TGW) and grain yield (GY) under both treatments. Senescence rate was calculated by decreasing values of SVIs from their peak values at heading stage, while variance for senescence rate among genotypes and between treatments could be explained by SVIs variations. Under limited irrigation, 10% to 15% higher senescence rate was detected as compared with full irrigation. Principle component analysis corroborated the negative association of high senescence rate with TGW and GY. Some genotypes, such as Beijing 0045, Nongda 5181, and Zhongmai 175, were selected with low senescence rate, stable TGW and GY in both full and limited irrigation treatments, nearly in accordance with the actual performance of these cultivars in field. Thus, SVIs derived from UAV appeared as a promising tool for rapid and precise estimation of senescence rate at maturation stages.

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Section: Introductionmentioning

confidence: 99%

Time-Series Multispectral Indices from Unmanned Aerial Vehicle Imagery Reveal Senescence Rate in Bread Wheat

et al. 2018

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“…Sensitivities between the above-mentioned biophysical parameters and SAR backscatter were described by Pearson Correlation Coefficient (R), which is useful for linear correlation analysis between two variables ( Ahlgren, 2003 ); Maximal Information Coefficient (MIC), which measures the dependence of two-variable relationship, both linear and non-liner ( Reshef et al, 2011 ); the coefficient of determination (R 2 ) of linear, Logarithmic and Quadratic, which describes the data relative to the regression function ( Zhang et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

“…Tree-species composition at the sub-compartment was available at the study area, however, the analysis including tree-species was not considered in this study, because all of the left 3 tree-species are coniferous species, which has insignificant tree-species dependence of the L-band SAR backscatter. Sensitivities between the above-mentioned biophysical parameters and SAR backscatter were described by Pearson Correlation Coefficient (R), which is useful for linear correlation analysis between two variables (Ahlgren, 2003); Maximal Information Coefficient (MIC), which measures the dependence of two-variable relationship, both linear and non-liner (Reshef et al, 2011); the coefficient of determination (R 2 ) of linear, Logarithmic and Quadratic, which describes the data relative to the regression function (Zhang et al, 2018).…”

Section: Sensitivity Analysis Of Sarσ 0 To Forest Biophysical Parametersmentioning

confidence: 99%

Forest structure dependency analysis of L-band SAR backscatter

Huang

et al. 2020

PeerJ

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Forest structure plays an important role in forest biomass inversion using synthetic aperture radar (SAR) backscatter. Synthetic aperture radar (SAR) sensors with long-wavelength have the potentiality to provide reliable and timely forest biomass inversion for their ability of deep penetration into the forest. L-band SAR backscatter shows useful for forest above-ground biomass (AGB) estimation. However, the way that forest structure mediating the biomass-backscatter affects the improvement of the related biomass estimation accuracy. In this paper, we have investigated L-band SAR backscatter sensitivity to forests with different mean canopy density, mean tree height and mean DBH (diameter at breast height) at the sub-compartment level. The forest species effects on their relationship were also considered in this study. The linear correlation coefficient R, non-linear correlation parameter, Maximal Information Coefficient (MIC), and the determination coefficient R2 from linear function, Logarithmic function and Quadratic function were used in this study to analyze forest structural properties effects on L-band SAR backscatter. The HV channel, which is more sensitive than HH to forest structure parameters, was chosen as the representative of SAR backscatter. 6037 sub-compartment were involved in the analysis. Canopy density showed a great influence on L-band backscatter than mean forest height and DBH. All of the R between canopy density and L-band backscatter were greater than 0.7 during the forest growth cycle. The sensitivity of L-band backscatter to mean forest height depends on forest canopy density. When canopy density was lower than 0.4, R values between mean forest height are smaller than 0.5. In contrast, the values of R were greater than 0.8 if canopy density was higher than 0.4. The sensitivity SAR backscatter to DBH fluctuated with canopy density, but it only showed obvious sensitivity when canopy density equals to 0.6, where both the linear and non-liner correlation values are higher than others. However, their effects on L-bang HV backscatter are affected by forest species, the effects on three forest structural parameters depend on tree species.

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“…I and Q are the real and imaginary portions of the level 1A SLC product, respectively, Q V is the maximum value of the original image data, which can be found from the QualifyValue in the metadata, and K db is the calibration cons in the metadata. For the calibration of C-band RADARSAT-2 data, readers are referred to our previous studies [35,36].…”

Section: Sar Data Acquisition and Processingmentioning

confidence: 99%

Forest Total and Component Above-Ground Biomass (AGB) Estimation through C- and L-band Polarimetric SAR Data

Zeng

Zhang

et al. 2022

Forests

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Forest biomass plays an essential role in forest carbon reservoir studies, biodiversity protection, forest management, and climate change mitigation actions. Synthetic Aperture Radar (SAR), especially the polarimetric SAR with the capability of identifying different aspects of forest structure, shows great potential in the accurate estimation of total and component forest above-ground biomass (AGB), including stem, bark, branch, and leaf biomass. This study aims to fully explore the potential of polarimetric parameters at the C- and L-bands to achieve high estimation accuracy and improve the estimation of AGB saturation levels. In this study, the backscattering coefficients at different polarimetric channels and polarimetric parameters extracted from Freeman2, Yamaguchi3, H-A-Alpha, and Target Scattering Vector Model (TSVM) decomposition methods were optimized by a random forest algorithm, first, and then inputted into linear regression models to estimate the total forest AGB and biomass components of two test sites in China. The results showed that polarimetric observations had great potential in total and component AGB estimation in the two test sites; the best performances were for leaves at test site I, with R2 = 0.637 and RMSE = 1.27 t/hm2. The estimation of biomass components at both test sites showed obvious saturation phenomenon estimation according to their scatter plots. The results obtained at both test sites demonstrated the potential of polarimetric parameters in total and component biomass estimation.

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Rape (Brassica napus L.) Growth Monitoring and Mapping Based on Radarsat-2 Time-Series Data

Cited by 14 publications

References 32 publications

Time-Series Multispectral Indices from Unmanned Aerial Vehicle Imagery Reveal Senescence Rate in Bread Wheat

Time-Series Multispectral Indices from Unmanned Aerial Vehicle Imagery Reveal Senescence Rate in Bread Wheat

Forest structure dependency analysis of L-band SAR backscatter

Forest Total and Component Above-Ground Biomass (AGB) Estimation through C- and L-band Polarimetric SAR Data

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