Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data: Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands

Wang, Guangzhen; Wang, Jingpu; Zou, Xueyong; Chai, Guoqi; Wu, Mengquan; Wang, Zhoulong

doi:10.1016/j.jag.2018.11.006

Cited by 39 publications

(11 citation statements)

References 43 publications

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“…The NPV proportion is greater at the beginning and at the end of the growing season, while the proportion of the PV peaks in the middle of the vegetation growing season. This is consistent with the findings of Wang [40] estimating the f PV and f NPV in the typical grasslands of Xilingol, indicating that the GEMI-DFI model is feasible to estimate the f PV and f NPV in the lower reaches of the Tarim River. The accuracy of the f NPV estimation (R 2 = 0.58) is slightly lower than for the f PV estimation (R 2 = 0.69).…”

Section: Discussionsupporting

confidence: 90%

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

et al. 2021

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Estimating the fractional coverage of the photosynthetic vegetation (fPV) and non-photosynthetic vegetation (fNPV) is essential for assessing the growth conditions of vegetation growth in arid areas and for monitoring environmental changes and desertification. The aim of this study was to estimate the fPV, fNPV and the fractional coverage of the bare soil (fBS) in the lower reaches of Tarim River quantitatively. The study acquired field data during September 2020 for obtaining the fPV, fNPV and fBS. Firstly, six photosynthetic vegetation indices (PVIs) and six non-photosynthetic vegetation indices (NPVIs) were calculated from Sentinel-2A image data. The PVIs include normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), reduced simple ratio index (RSR) and global environment monitoring index (GEMI). Meanwhile, normalized difference index (NDI), normalized difference tillage index (NDTI), normalized difference senescent vegetation index (NDSVI), soil tillage index (STI), shortwave infrared ratio (SWIR32) and dead fuel index (DFI) constitutes the NPVIs. We then established linear regression model of different PVIs and fPV, and NPVIs and fNPV, respectively. Finally, we applied the GEMI-DFI model to analyze the spatial and seasonal variation of fPV and fNPV in the study area in 2020. The results showed that the GEMI and fPV revealed the best correlation coefficient (R2) of 0.59, while DFI and fNPV had the best correlation of R2 = 0.45. The accuracy of fPV, fNPV and fBS based on the determined PVIs and NPVIs as calculated by GEMI-DFI model are 0.69, 0.58 and 0.43, respectively. The fPV and fNPV are consistent with the vegetation phonological development characteristics in the study area. The study concluded that the application of the GEMI-DFI model in the fPV and fNPV estimation was sufficiently significant for monitoring the spatial and seasonal variation of vegetation and its ecological functions in arid areas.

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Section: Discussionsupporting

confidence: 90%

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

et al. 2021

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“…There are certain difficulties and uncertainties in the large-scale inversion of grassland traits based on remote sensing. The grassland ecosystem itself is complex and the grass species type, growth height, and growth stages (greening, heading, flowering, maturing, and yellowing) all lead to differences in grassland characteristics [66]. Moreover, the topography, climatic environment, nutrient content (N and P), water stress, etc.…”

Section: Uncertainties and Sources Of Errormentioning

confidence: 99%

Estimation of the Grassland Aboveground Biomass of the Inner Mongolia Plateau Using the Simulated Spectra of Sentinel-2 Images

et al. 2020

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An accurate assessment of the grassland aboveground biomass (AGB) is important for analyzing terrestrial ecosystem structures and functions, estimating grassland primary productivity, and monitoring climate change and carbon/nitrogen circulation on a global scale. Multispectral satellites with wide-width advantages, such as Sentinel-2, have become the inevitable choice for the large-scale monitoring of grassland biomass on regional and global scales. However, the spectral resolution of multispectral satellites is generally low, which limits the inversion accuracy of grassland AGB and restricts further application in large-scale grassland monitoring. For this reason, a satellite-scale simulated spectra method was proposed to enhance the spectral information of the Sentinel-2 data, and a simulated spectrum (SS) was constructed using this algorithm. Then, the raw spectrum (RS) of Sentinel-2 and the SS were used as data sources to calculate the vegetation indices (RS-VIs and SS-VIs, which represent vegetation indices calculated using RS and SS data, respectively), and the multi-granularity spectral segmentation algorithm (MGSS) was employed to extract spectral segmentation features (RS-SF and SS-SF, which represent segmentation features extracted by RS and SS data, respectively). Following this, these spectral features (RS-SF, SS-SF, RS-VIs, and SS-VIs) were used to estimate AGB by partial least-squares regression (PLSR) and multiple stepwise regression (MSR) models. Finally, the spatial distribution law and the reasons for the latitude zone of the Inner Mongolia Plateau were analyzed, based on precipitation, the average temperature, topography, etc. The conclusions are as follows. Firstly, the SS has more spectral information and its sensitivity to biomass is higher than the RS of Sentinel-2 in some bands, and the correlation between the SS-VIs and biomass is higher than that of the RS-VIs. Secondly, among the spectral features, the most accurate AGB estimation was obtained by SS-SF, which gave R2 = 0.95. The root mean square error (RMSE) was 10.86 g/m2 and the estimate accuracy (EA) was 82.84% in the MSR model. Additionally, RMSE = 10.89 g/m2 and EA = 82.78% in the PLSR model. Compared with the traditional estimation methods using RS and VI, R2 was increased by at least 0.2, RMSE was reduced by at least 14.08 g/m2, and EA was increased by 22.26%. Therefore, the simulated spectra method can help improve the estimation accuracy of AGB, and a new idea about regional and global large-scale biomass acquisition is provided.

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“…Triangle-space techniques (e.g., DFI-NDVI [16], CAI-NDVI [39].) are widely used to estimate cropland CRC because crops and vegetation both appear in remote-sensing images.…”

Section: Estimate Of Crop Residue Covermentioning

confidence: 99%

“…In recent decades, spectral remote-sensing data acquired from ground platforms, unmanned aerial vehicles, airborne platforms, and satellite platforms have been used to capture field spectra in narrow bands and have provided information about the soil surface. As a result, tillage practices and estimating CRC based on remote-sensing data has become a topic of significant interest to environmental and agricultural researchers [11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Using Hyperspectral Crop Residue Angle Index to Estimate Maize and Winter-Wheat Residue Cover: A Laboratory Study

et al. 2019

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Crop residue left in the field after harvest helps to protect against water and wind erosion, increase soil organic matter, and improve soil quality, so a proper estimate of the quantity of crop residue is crucial to optimize tillage and for research into environmental effects. Although remote-sensing-based techniques to estimate crop residue cover (CRC) have proven to be good tools for determining CRC, their application is limited by variations in the moisture of crop residue and soil. In this study, we propose a crop residue angle index (CRAI) to estimate the CRC for four distinct soils with varying soil moisture (SM) content and crop residue moisture (CRM). The current study uses laboratory-based tests ((i) a dry dataset (air-dried soils and crop residues, n = 392); (ii) a wet dataset (wet soils and crop residues, n = 822); (iii) a saturated dataset (saturated soils and crop residues, n = 402); and (iv) all datasets (n = 1616)), which allows us to analysis the soil and crop residue hyperspectral response to varying SM/CRM. The CRAI combines two features that reflect the moisture content in soil and crop residue. The first is the different reflectance of soil and crop residue as a function of moisture in the near-infrared band (833 nm) and short-wave near-infrared band (1670 nm), and the second is different reflectance of soils and crop residues to lignin, cellulose, and moisture in the bands at 2101, 2031, and 2201 nm. The effects of moisture and soil type on the proposed CRAI and selected traditional spectral indices ((i) hyperspectral cellulose absorption index; (ii) hyperspectral shortwave infrared normalized difference residue index; and (iii) selected broad-band spectral indices) were compared by using a laboratory-based dataset. The results show that the SM/CRM significantly affects the broad-band spectral indices and all other spectral indices investigated are less correlated with CRC when using all datasets than when using only the dry, wet, or saturated dataset. Laboratory study suggests that the CRAI is promising for estimating CRC with the four soils and with varying SM/CRM. However, because the CRAI was only validated by a laboratory-based dataset, additional field testing is thus required to verify the use of satellite hyperspectral remote-sensing images for different crops and ecological areas.

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Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data: Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands

Cited by 39 publications

References 43 publications

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

Estimation of the Grassland Aboveground Biomass of the Inner Mongolia Plateau Using the Simulated Spectra of Sentinel-2 Images

Using Hyperspectral Crop Residue Angle Index to Estimate Maize and Winter-Wheat Residue Cover: A Laboratory Study

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