Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery

With high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previous studies have found that there is an overestimation of FVC for the early growth stage of vegetation in the current algorithms. When the row crops are a form in the early stage of vegetation, their FVC may also have overestimation. Therefore, developing an algorithm to address this problem is necessary. This study used World-View 3 images as data sources and attempted to use the canopy reflectance model of row crops, coupling backward propagation neural networks (BPNNs) to estimate the FVC of row crops. Compared to the prevailing algorithms, i.e., empirical method, spectral mixture analysis, and continuous crop model coupling BPNNs, the results showed that the calculated accuracy of the canopy reflectance model of row crops coupling with BPNNs is the highest performing (RMSE = 0.0305). Moreover, when the structure is obvious, we found that the FVC of row crops was about 0.5–0.6, and the relationship between estimated FVC of row crops and NDVI presented a strong exponential relationship. The results reinforced the conclusion that the canopy reflectance model of row crops coupled with BPNNs is more suitable for estimating the FVC of row crops in high-resolution images.

Download Full-text

Fractional vegetation cover retrieval using multi-spatial resolution data and plant growth model

2010 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2010.5650399 ◽

2010 ◽

Cited By ~ 2

Author(s):

Xihan Mu ◽

Yaokai Liu ◽

Guangjian Yan ◽

Yanjuan Yao

Keyword(s):

Plant Growth ◽

Spatial Resolution ◽

Growth Model ◽

Vegetation Cover ◽

Fractional Vegetation Cover ◽

Plant Growth Model ◽

Resolution Data

Download Full-text

Monitoring habitat preserves in southern California using high spatial resolution multispectral imagery

Environmental Monitoring and Assessment ◽

10.1007/s10661-008-0320-8 ◽

2008 ◽

Vol 152 (1-4) ◽

pp. 343-356 ◽

Cited By ~ 5

Author(s):

Lloyd L. Coulter ◽

Douglas A. Stow

Keyword(s):

Spatial Resolution ◽

Southern California ◽

High Spatial Resolution ◽

Multispectral Imagery

Download Full-text

Mapping Urban Impervious Surfaces from Medium and High Spatial Resolution Multispectral Imagery

Remote Sensing Applications Series - Remote Sensing of Impervious Surfaces ◽

10.1201/9781420043754.ch4 ◽

2007 ◽

Author(s):

Dengsheng Lu ◽

Qihao Weng

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Multispectral Imagery ◽

Impervious Surfaces ◽

Urban Impervious Surfaces

Download Full-text

Spatiotemporal Comparison and Validation of Three Global-Scale Fractional Vegetation Cover Products

Remote Sensing ◽

10.3390/rs11212524 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2524 ◽

Cited By ~ 2

Author(s):

Duanyang Liu ◽

Kun Jia ◽

Xiangqin Wei ◽

Mu Xia ◽

Xiwang Zhang ◽

...

Keyword(s):

Vegetation Cover ◽

Land Surface ◽

Large Scale ◽

High Spatial Resolution ◽

Remote Sensing Data ◽

Global Scale ◽

Glass Product ◽

Fractional Vegetation Cover ◽

Temporal Profiles

Fractional vegetation cover (FVC) is an important parameter for many environmental and ecological models. Large-scale and long-term FVC products are critical for various applications. Currently, several global-scale FVC products have been generated with remote sensing data, such as VGT bioGEOphysical product Version 2 (GEOV2), PROBA-V bioGEOphysical product Version 3 (GEOV3) and Global LAnd Surface Satellite (GLASS) FVC products. However, studies comparing and validating these global-scale FVC products are rare. Therefore, in this study, the performances of three global-scale time series FVC products, including the GEOV2, GEOV3, and GLASS FVC products, are investigated to assess their spatial and temporal consistencies. Furthermore, reference FVC data generated from high-spatial-resolution data are used to directly evaluate the accuracy of these FVC products. The results show that these three FVC products achieve general agreements in terms of spatiotemporal consistencies over most regions. In addition, the GLASS and GEOV2 FVC products have reliable spatial and temporal completeness, whereas the GEOV3 FVC product contains much missing data over high-latitude regions, especially during wintertime. Furthermore, the GEOV3 FVC product presents higher FVC values than GEOV2 and GLASS FVC products over the equator. The main differences between the GEOV2 and GLASS FVC products occur over deciduous forests, for which the GLASS product presents slightly higher FVC values than the GEOV2 product during wintertime. Finally, temporal profiles of the GEOV2 and GLASS FVC products show better consistency than the GEOV3 FVC product, and the GLASS FVC product presents more reliable accuracy (R2 = 0.7878, RMSE = 0.1212) compared with the GEOV2 (R2 = 0.5798, RMSE = 0.1921) and GEOV3 (R2 = 0.7744, RMSE = 0.2224) FVC products over these reference FVC data.

Download Full-text