A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

Yang, Linqing; Jia, Kun; Liang, Shunlin; Wei, Xiangqin; Yao, Yunjun; Zhang, Xiaolin

doi:10.3390/rs9080857

Cited by 38 publications

(18 citation statements)

References 61 publications

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“…The original soil reflectances were resampled from an interval of 10 nm to 1 nm by cubic spline functions to conform to the spectral response function of Sentinel-2. Then, the soil reflectances were resampled to correspond to Sentinel-2 spectra using the following formula [71]:…”

Section: Generating the Learning Dataset Using The Prosail Modelmentioning

confidence: 99%

“…Because the three bands with 60-m spatial resolution were mainly dedicated to atmospheric correction and cloud screening, they were removed in the simulated dataset. According to the previous studies [71,75], and considering the computational efficiency, a medium quantity dataset that contained 200,000 items was simulated, of which 80% were randomly selected as the training dataset and the remaining 20% were used for validation.…”

Section: Generating the Learning Dataset Using The Prosail Modelmentioning

confidence: 99%

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Assessment of Sentinel-2 MSI Spectral Band Reflectances for Estimating Fractional Vegetation Cover

et al. 2018

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Fractional vegetation cover (FVC) is an essential parameter for characterizing the land surface vegetation conditions and plays an important role in earth surface process simulations and global change studies. The Sentinel-2 missions carrying multi-spectral instrument (MSI) sensors with 13 multispectral bands are potentially useful for estimating FVC. However, the performance of these bands for FVC estimation is unclear. Therefore, the objective of this study was to assess the performance of Sentinel-2 MSI spectral band reflectances on FVC estimation. The samples, including the Sentinel-2 MSI canopy reflectances and corresponding FVC values, were simulated using the PROSPECT + SAIL radiative transfer model under different conditions, and random forest regression (RFR) method was then used to develop FVC estimation models and assess the performance of various band reflectances for FVC estimation. These models were finally evaluated using field survey data. The results indicate that the three most important bands of Sentinel-2 MSI data for FVC estimation are band 4 (Red), band 12 (SWIR2) and band 8a (NIR2). FVC estimation using these bands has a comparable accuracy (root mean square error (RMSE) = 0.085) with that using all bands (RMSE = 0.090). The results also demonstrate that band 12 had a better performance for FVC estimation than the green band (RMSE = 0.097). However, the newly added red-edge bands, with low scores in the RFR model, have little significance for improving FVC estimation accuracy compared with the Red, NIR2 and SWIR2 bands.

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Section: Generating the Learning Dataset Using The Prosail Modelmentioning

confidence: 99%

Section: Generating the Learning Dataset Using The Prosail Modelmentioning

confidence: 99%

Assessment of Sentinel-2 MSI Spectral Band Reflectances for Estimating Fractional Vegetation Cover

et al. 2018

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“…λ 0 is the leaf dispersion or clumping. According to the definition of FVC, θ is equal to 0 when the FVC is calculated [44]. The soil reflectance data were also important parameters for the PROSAIL model.…”

Section: Training Samples Generation and Refinementmentioning

confidence: 99%

Fractional Vegetation Cover Estimation Algorithm for FY-3B Reflectance Data Based on Random Forest Regression Method

et al. 2021

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As an important land surface vegetation parameter, fractional vegetation cover (FVC) has been widely used in many Earth system ecological and climate models. In particular, high-quality and reliable FVC products on the global scale are important for the Earth surface process simulation and global change studies. Recently, the FengYun-3 (FY-3) series satellites, which are the second generation of Chinese meteorological satellites, launched with the polar orbit and provide continuous land surface observations on a global scale. However, there is rare studying on the FVC estimation using FY-3 reflectance data. Therefore, the FY-3B reflectance data were selected as the representative data to develop a FVC estimation algorithm in this study, which would investigate the capability of the FY-3 reflectance data on the global FVC estimation. The spatial–temporal validation over the regional area indicated that the FVC estimations generated by the proposed algorithm had reliable continuities. Furthermore, a satisfactory accuracy performance (R2 = 0.7336, RMSE = 0.1288) was achieved for the proposed algorithm based on the Earth Observation LABoratory (EOLAB) reference FVC data, which provided further evidence on the reliability and robustness of the proposed algorithm. All these results indicated that the FY-3 reflectance data were capable of generating a FVC estimation with reliable spatial–temporal continuities and accuracy.

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“…The last decades have seen the development of methods to estimate crop FVC based on remote-sensing images from unmanned aerial vehicle (UAV), aerial, or satellite platforms [ 5 , 29 – 33 ]. These methods can be divided into five categories: (i) physical model methods, (ii) semi-empirical methods, (iii) empirical methods, (iv) crop growth methods, and (v) hybrid methods.…”

Section: Introductionmentioning

confidence: 99%

Method for accurate multi-growth-stage estimation of fractional vegetation cover using unmanned aerial vehicle remote sensing

et al. 2021

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Background Fractional vegetation cover (FVC) is an important parameter for evaluating crop-growth status. Optical remote-sensing techniques combined with the pixel dichotomy model (PDM) are widely used to estimate cropland FVC with medium to high spatial resolution on the ground. However, PDM-based FVC estimation is limited by effects stemming from the variation of crop canopy chlorophyll content (CCC). To overcome this difficulty, we propose herein a “fan-shaped method” (FSM) that uses a CCC spectral index (SI) and a vegetation SI to create a two-dimensional scatter map in which the three vertices represent high-CCC vegetation, low-CCC vegetation, and bare soil. The FVC at each pixel is determined based on the spatial location of the pixel in the two-dimensional scatter map, which mitigates the effects of CCC on the PDM. To evaluate the accuracy of FSM estimates of the FVC, we analyze the spectra obtained from (a) the PROSAIL model and (b) a spectrometer mounted on an unmanned aerial vehicle platform. Specifically, we use both the proposed FSM and traditional remote-sensing FVC-estimation methods (both linear and nonlinear regression and PDM) to estimate soybean FVC. Results Field soybean CCC measurements indicate that (a) the soybean CCC increases continuously from the flowering growth stage to the later-podding growth stage, and then decreases with increasing crop growth stages, (b) the coefficient of variation of soybean CCC is very large in later growth stages (31.58–35.77%) and over all growth stages (26.14%). FVC samples with low CCC are underestimated by the PDM. Linear and nonlinear regression underestimates (overestimates) FVC samples with low (high) CCC. The proposed FSM depends less on CCC and is thus a robust method that can be used for multi-stage FVC estimation of crops with strongly varying CCC. Conclusions Estimates and maps of FVC based on the later growth stages and on multiple growth stages should consider the variation of crop CCC. FSM can mitigates the effect of CCC by conducting a PDM at each CCC level. The FSM is a robust method that can be used to estimate FVC based on multiple growth stages where crop CCC varies greatly.

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A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

Cited by 38 publications

References 61 publications

Assessment of Sentinel-2 MSI Spectral Band Reflectances for Estimating Fractional Vegetation Cover

Assessment of Sentinel-2 MSI Spectral Band Reflectances for Estimating Fractional Vegetation Cover

Fractional Vegetation Cover Estimation Algorithm for FY-3B Reflectance Data Based on Random Forest Regression Method

Method for accurate multi-growth-stage estimation of fractional vegetation cover using unmanned aerial vehicle remote sensing

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