scholarly journals THE SENSITIVITY OF C-BAND HYBRID POLARIMETRIC RISAT-1 SAR DATA TO LEAF AREA INDEX OF PADDY CROP

2018 ◽  
Vol IV-5 ◽  
pp. 215-222
Author(s):  
H. S. Srivastava ◽  
T. Sivasankar ◽  
P. Patel
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Incidence Angle ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Biophysical Parameters ◽  
Area Index ◽  
Crop Cycle ◽  
Paddy Crop ◽  
Active Microwave Remote Sensing

<p><strong>Abstract.</strong> Active microwave remote sensing data has become an important source to retrieve crop biophysical parameters due to its unique sensitivity towards geometrical, structural and dielectric properties of various crop components. The temporal variability of various crop biophysical parameters during crop cycle has significant impact on the overall crop yield. In this study, two RISAT-1 hybrid polarimetric temporal SAR datasets at &amp;sim;32&amp;deg; incidence angle were acquired during 2015 Kharif season. The in-situ leaf area index (LAI) values from seventeen paddy fields were measured in synchrony to the satellite passes during both the campaigns. Analysis observed the decreasing trend of backscattering coefficients (&amp;sigma;&amp;deg;<sub>RH</sub>, &amp;sigma;&amp;deg;<sub>RV</sub>) with increase in LAI. Results indicate that the sensitivity of hybrid polarimetric parameters towards LAI, also depends on the change in crop structure due to crop growth. This study investigate the sensitivity of backscattering coefficients (σ&amp;deg;<sub>RH</sub>, σ&amp;deg;<sub>RV</sub>) and polarimetric parameters (even bounce, odd bounce and volume component) generated from m-&amp;delta;, m-&amp;chi; and m-&amp;alpha; space decompositions towards LAI using empirical analysis. An increase of 0.16 in R<sup>2</sup> (from 0.63 to 0.79) clearly indicates that the polarimetric parameters (even bounce, odd bounce and volume component) are more sensitive to LAI of paddy crop than the backscattering coefficients (&amp;sigma;&amp;deg;<sub>RH</sub>, &amp;sigma;&amp;deg;<sub>RV</sub>). It has been identified that the combined use of backscattering coefficients as well as polarimetric parameters (even bounce, odd bounce and volume component) in the model, can significantly improve the accuracy of the LAI estimation.</p>

scholarly journals Estimation of Leaf Area Index Using Remote Sensing Data.

1999 ◽  
Vol 12 (3) ◽  
pp. 210-220 ◽  
Author(s):  
Takashi ISHII ◽  
Makoto NASHIMOTO ◽  
Hisashi SHIMOGAKI
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Remote Sensing Data ◽  
Area Index ◽  
Sensing Data

Assessment of different methods for estimating forest leaf area index from remote sensing data

2014 ◽  
Vol 34 (16) ◽  
Author(s):  
王修信 WANG Xiuxin ◽  
孙涛 SUN Tao ◽  
朱启疆 ZHU Qijiang ◽  
刘馨 LIU Xin ◽  
高凤飞 GAO Fengfei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Remote Sensing Data ◽  
Area Index ◽  
Sensing Data

scholarly journals Sugarcane leaf area index modeling under different soil water conditions

1997 ◽  
Vol 54 (spe) ◽  
pp. 39-44 ◽  
Author(s):  
D.A. Teruel ◽  
V. Barbieri ◽  
L.A. Ferraro Jr.
Keyword(s):  
Water Stress ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Period ◽  
Plant Growth And Development ◽  
Area Index ◽  
Stress Levels ◽  
Crop Cycle ◽  
Soil Water Conditions ◽  
Growth Deficit

The knowledge of the Leaf Area Index (LAI) variation during the whole crop cycle is essential to the modeling of the plant growth and development and, consequently, of the crop yield. Sugarcane LAI evolution models were developed for different crop cycles, by adjusting observed LAI values and growing degree-days summation data on a power-exponential function. The resultant equations simulate adequately the LAI behavior during the entire crop cycle. The effect of different water stress levels was calculated in different growth periods, upon the LAI growth The LAI growth deficit was correlated with the ratio between actual evapotranspiration and máximum evapotranspiration, and a constant named kuu was obtained hi each situation. It was noticed that the kLAI must be estimated not Just for different growth periods, but also for different water stress levels in each growth period.

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 Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation

2020 ◽  
Author(s):  
Francesco Novelli ◽  
Heide Spiegel ◽  
Taru Sandén ◽  
Francesco Vuolo
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Field Data ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Area Index ◽  
Sentinel 2

&lt;p&gt;The work is based on a previously published study with the aim to further analyse the results obtained. Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields.&lt;br&gt;LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. Better RMSE and RRMSE were obtained in 2017 compared to 2016 (RMSE = 0.44 vs. 0.46) (RRMSE = 17% vs. 19%). In 2016 year, a slightly lower R&lt;sup&gt;2&lt;/sup&gt; value was found compared to 2017 (R&lt;sup&gt;2&lt;/sup&gt; = 0.72 vs. 0.89). A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The work shows that the assimilation of remote sensing data into the crop growth model can help to overtake some structural problems of the model.&amp;#160; The assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.&lt;/p&gt;

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.

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