Estimating Fractional Vegetation Cover From Landsat-7 ETM+ Reflectance Data Based on a Coupled Radiative Transfer and Crop Growth Model

Wang, Xiaoxia; Jia, Kun; Liang, Shunlin; Li, Qiangzi; Wei, Xiangqin; Yao, Yunjun; Zhang, Xiaolin; Tu, Yixuan

doi:10.1109/tgrs.2017.2709803

Cited by 40 publications

(23 citation statements)

References 28 publications

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“…The SAR data are immune to atmospheric conditions and have large temporal and spatial coverage. But when it is used for FVC estimation, the accuracy is low because of the strong penetration of radar signals [26], [27]. Multispectral data with moderate spatial resolutions, such as the Landsat imagery, have been used for FVC estimation [28]- [31].…”

Section: Introductionmentioning

confidence: 99%

“…The Landsat imagery has achieved good results in mapping the subpixel urban fractional cover. However, its relatively low spatial resolution may cause a spectrum mixture for estimating the FVC in heterogeneous urban areas [27], [29]. The Sentinel-2A satellites obtain multispectral instrument (MSI) images with higher spatial (10,20, and 60 m) and spectral resolutions (13 bands) than that of Landsat [32]- [35], which can bring higher vegetation monitoring accuracy and FVC estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Estimating the Urban Fractional Vegetation Cover Using an Object-Based Mixture Analysis Method and Sentinel-2 MSI Imagery

Cai

Zhang

Lin

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate and efficient identification of the urban vegetation abundance is of great importance for urban planning and management. A lot of efforts have been made to estimate the urban fractional vegetation cover (FVC) using multispectral images by the pixel-based mixture analysis method. However, urban FVC maps comprising various meaningful landscapes have wider applications. Compared with other moderate spatial resolution multispectral imagery (e.g., SPOT, Landsat 8), the Sentinel-2 multispectral instrument (MSI) imagery has higher resolution, larger coverage, and shorter revisit time. So it may provide higher accuracy for urban FVC mapping. This article derives an accurate object-based urban FVC map for Changsha city, China, from the 10-m resolution Sentinel-2 data acquired in 2017. For producing the urban FVC maps, the mixture analysis methods were applied on segmental image objects instead of pixels. The results demonstrate that the object-based mixture analysis method achieved a higher FVC estimation accuracy than the pixel-based mixture analysis did, and it effectively removed the "salt and pepper" phenomena. The object-based linear model fully constrained least squares and achieved the best estimation accuracy (R 2 = 0.92, RMSE = 0.0956). The red-edge band reflectance information of the MSI imagery can improve the accuracy of the FVC maps, but not significantly. The object-based urban FVC maps would be a good alternative to the traditional pixel-based maps. Index Terms-Fractional vegetation cover (FVC), object-based mixture analysis (OBMA), red edge band, Sentinle-2, urban.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating the Urban Fractional Vegetation Cover Using an Object-Based Mixture Analysis Method and Sentinel-2 MSI Imagery

Cai

Zhang

Lin

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Similarly, GPP estimated from field observation and from Landsat-MODIS fused images shows a strong agreement in wheat (R 2 = 0.85, p ≤ 0.01) and sugarcane (R 2 = 0.86, p ≤ 0.01) in India [86]. In addition, timely monitoring of crop growth is important for farming management [87]. Synthesized data could be used to provide near real-time estimate of external conditions (e.g., water and soil conditions) and internal attributes (e.g., leaf chlorophyll and protein content) of crops.…”

Section: Agriculturementioning

confidence: 82%

Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions

et al. 2018

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Satellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies have experienced rapid development in recent years, data acquired from a single satellite sensor are often unable to satisfy our demand. As a result, integrated use of data from different sensors has become increasingly popular in the past decade. Many spatiotemporal data fusion methods have been developed to produce synthesized images with both high spatial and temporal resolutions from two types of satellite images, frequent coarse-resolution images, and sparse fine-resolution images. These methods were designed based on different principles and strategies, and therefore show different strengths and limitations. This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets. To this end, this review paper investigates literature on current spatiotemporal data fusion methods, categorizes existing methods, discusses the principal laws underlying these methods, summarizes their potential applications, and proposes possible directions for future studies in this field.

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“…In addition to optical remote-sensing techniques, other remote-sensing techniques [e.g., synthetic aperture radar [ 30 , 46 ]] have also been developed and applied to estimate FVC based on remote sensing. Hybrid methods involve the combined use of several of the methods mentioned above; for example, the model of Wang et al [ 31 , 47 ] uses crop modeling and remote-sensing-data assimilation. In recent years, the use of convolutional neural networks (CNNs) and high ground spatial resolution (GSD) images for estimating vegetation cover fractions has developed rapidly [ 48 , 49 ].…”

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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Estimating Fractional Vegetation Cover From Landsat-7 ETM+ Reflectance Data Based on a Coupled Radiative Transfer and Crop Growth Model

Cited by 40 publications

References 28 publications

Estimating the Urban Fractional Vegetation Cover Using an Object-Based Mixture Analysis Method and Sentinel-2 MSI Imagery

Estimating the Urban Fractional Vegetation Cover Using an Object-Based Mixture Analysis Method and Sentinel-2 MSI Imagery

Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions

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

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