scholarly journals Combination of Linear Regression Lines to Understand the Response of Sentinel-1 Dual Polarization SAR Data with Crop Phenology—Case Study in Miyazaki, Japan

2020 ◽  
Vol 12 (1) ◽  
pp. 189 ◽  
Author(s):  
Emal Wali ◽  
Masahiro Tasumi ◽  
Masao Moriyama
Keyword(s):  
Linear Regression ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Cover ◽  
Polynomial Regression ◽  
Backscattering Coefficient ◽  
Biophysical Parameters ◽  
Area Index ◽  
Dry Biomass ◽  
Green Vegetation

This study investigated the relationship between backscattering coefficients of a synthetic aperture radar (SAR) and the four biophysical parameters of rice crops—plant height, green vegetation cover, leaf area index, and total dry biomass. A paddy rice field in Miyazaki, Japan was studied from April to July of 2018, which is the rice cultivation season. The SAR backscattering coefficients were provided by Sentinel-1 satellite. Backscattering coefficients of two polarization settings—VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving)—were investigated. Plant height, green vegetation cover, leaf area index, and total dry biomass were measured at ground level, on the same dates as satellite image acquisition. Polynomial regression lines indicated relationships between backscattering coefficients and plant biophysical parameters of the rice crop. The biophysical parameters had stronger relationship to VH than to VV polarization. A disadvantage of adopting polynomial regression equations is that the equation can have two biophysical parameter solutions for a particular backscattering coefficient value, which prevents simple conversion from backscattering coefficients to plant biophysical parameters. To overcome this disadvantage, the relationships between backscattering coefficients and the plant biophysical parameters were expressed using a combination of two linear regression lines, one line for the first sub-period and the other for the second sub-period during the entire cultivation period. Following this approach, all four plant biophysical parameters were accurately estimated from the SAR backscattering coefficient, especially with VH polarization, from the date of transplanting to about two months, until the mid-reproductive stage. However, backscattering coefficients saturate after two months from the transplanting, and became insensitive to the further developments in plant biophysical parameters. This research indicates that SAR can effectively and accurately monitor rice crop biophysical parameters, but only up to the mid reproductive stage.

Leaf Area Index and Dry Biomass of Taro 1

1984 ◽  
Vol 76 (5) ◽  
pp. 750-753 ◽  
Author(s):  
S. F. Shih ◽  
G. H. Snyder
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Dry Biomass

scholarly journals Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape

2020 ◽  
Vol 12 (19) ◽  
pp. 3121
Author(s):  
Roya Mourad ◽  
Hadi Jaafar ◽  
Martha Anderson ◽  
Feng Gao
Keyword(s):  
Linear Regression ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Above Ground Biomass ◽  
Area Index ◽  
Ground Biomass ◽  
Semi Arid ◽  
Sentinel 2

Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R2 value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R2: ~0.60.

scholarly journals Estimation of Biomass and Leaf Area Index in the Western Ghats Forest Ecosystem by the Integrated Analysis of Hyperspectral Data and Space Borne LiDAR Data

2019 ◽  
pp. 1-12
Author(s):  
Indu Indirabai ◽  
M. V. Harindranathan Nair ◽  
Jaishanker R. Nair ◽  
Rama Rao Nidamanuri
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Forest Ecosystem ◽  
Western Ghats ◽  
Hyperspectral Data ◽  
Spectral Information ◽  
Lidar Data ◽  
Biophysical Parameters ◽  
Area Index ◽  
The Western Ghats

The Western Ghats regions of India are characterised by highly complex and biodiverse forest ecosystem with heterogeneous tree species. The integration of LiDAR data with multispectral remote sensing has limitations in the case of spectral information abundance. The objective of this study was to undertake biophysical characterisation in the Western Ghats regions of India by the integration of GLAS ICESat data and AVIRIS-NG hyperspectral data. The methodology of the study includes pre-processing of the hyperspectral and ICESat GLAS data followed by the integration of the two data sets based on pixel based fusion strategy in order to estimate the biophysical parameters of forests. Biomass was estimated by Support Vector Regression method. The structural characteristics extracted from the LiDAR data are integrated with spectral characteristics from the AVIRIS NG imagery based on the pixel level so that biophysical characteristics including canopy height, biomass, Leaf Area Index are estimated. The integrated product on further analysis revealed the applicability of this approach to extract more spectral information and forest parameters. The key findings of the study include biophysical parameters both structural as well as abundant spectral information can be retrieved successfully by the methodology used which have strong correlation with the in situ measurements. The study concluded that biophysical parameters including Leaf Area Index, biomass and canopy height can be effectively estimated by the integration of AVIRIS-NG imagery and GLAS data, which cannot be possible when used independently. It is recommended to have continuous retrieval of LiDAR foot prints instead of discrete, to make modelling of the biophysical parameters a little more effective.

scholarly journals  Estimation of leaf area index and assessment of its allometric equations in oak forests: Northern Zagros, Iran

2012 ◽  
Vol 58 (No. 3) ◽  
pp. 116-122 ◽  
Author(s):  
S. Khosravi ◽  
M. Namiranian ◽  
H. Ghazanfari ◽  
A. Shirvani
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Allometric Equations ◽  
Oak Forests ◽  
Biophysical Parameters ◽  
Destructive Sampling ◽  
Area Index ◽  
Zagros Forests ◽  
Crown Volume ◽  
Oak Trees

The focus of the present study is the estimation of leaf area index (LAI) and the assessment of allometric equations for predicting the leaf area of Lebanon oaks (Quercus libani Oliv.) in Iran&rsquo;s northern Zagros forests. To that end, 50 oak trees were randomly selected and their biophysical parameters were measured. Then, on the basis of destructive sampling of the oak trees, their specific leaf area (SLA) and leaf area were measured. The results showed that SLA and LAI of the Lebanon oaks were 136.9 cm&middot;g<sup>&ndash;1 </sup>and 1.99, respectively. Among all the parameters we measured, the crown volume exhibited the highest correlation with LAI (r<sup>2</sup> = 0.65). The easily measured tree parameters such as diameter at breast height did not show a high correlation with leaf area (r<sup>2</sup> = 0.36). Our obtained moderate correlations in the allometric equations could be due to the fact that branches of these trees had been pollarded by the local people when the branches were only 3 or 4 years old; therefore, the natural structure of the crowns in these trees might have been damaged. &nbsp;

scholarly journals Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2115
Author(s):  
Zhijun Zhen ◽  
Shengbo Chen ◽  
Tiangang Yin ◽  
Eric Chavanon ◽  
Nicolas Lauret ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Surface Reflectance ◽  
Adjustment Factor ◽  
Area Index ◽  
Dense Vegetation ◽  
Saturation Effects

Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs’ saturation in the Apiacás area (i.e., X = −0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = −0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

Sign in / Sign up

Export Citation Format

Share Document