Wheat crop biophysical parameters retrieval using hybrid-polarized RISAT-1 SAR data

2018 ◽  
Vol 216 ◽  
pp. 28-43 ◽  
Author(s):  
Sugandh Chauhan ◽  
Hari Shanker Srivastava ◽  
Parul Patel
Keyword(s):  
Wheat Crop ◽  
Biophysical Parameters ◽  
Sar Data

scholarly journals AN INSIGHT INTO THE VOLUME COMPONENT GENERATED FROM RISAT-1 HYBRID POLARIMETRIC SAR DATA FOR CROP BIOPHYSICAL PARAMETERS RETRIEVAL

2018 ◽  
Vol IV-5 ◽  
pp. 209-214 ◽  
Author(s):  
H. S. Srivastava ◽  
T. Sivasankar ◽  
P. Patel
Keyword(s):  
Cloud Model ◽  
Incidence Angle ◽  
Uttar Pradesh ◽  
Wheat Crop ◽  
Polarimetric Sar ◽  
Biophysical Parameters ◽  
Interaction Factor ◽  
Significant Difference ◽  
Water Cloud ◽  
Sar Data

<p><strong>Abstract.</strong> Polarimetric parameters have been extensively used for target parameters retrieval than backscattering coefficients. In previous studies, volume component generated from polarimetric SAR data has been considered as the return signal component from vegetation and intern used this for biophysical parameters retrieval. Un-polarized component of the return signal has been considered as volume component. The present study is mainly focused to analyze the volume component generated from C-band RISAT-1 hybrid polarimetric SAR data from wheat crop. Three temporal datasets acquired at &amp;sim;31&amp;deg; central incidence angle between Jan and Mar 2016 over parts of Bharatpur and Mathura districts located in Rajasthan and Uttar Pradesh (India) have been used in this study. Water Cloud Model with Gaps has been considered for modeling the first Stokes parameter (g<sub>0</sub>), which represents total intensity of return signal, from wheat crop using LAI and Interaction factor as vegetation descriptors. The vegetation component derived using calibrated Water Cloud Model with Gaps has been analyzed with volume component derived from RISAT-1 hybrid polarimetric SAR data. The analyses observed that a significant difference during lower LAI values and shown comparably during higher LAI values. The higher values of volume component derived from RISAT-1 SAR data than modeled vegetation component indicates that the volume component can also be generated by underneath soil. It is also observed the difference in derived un-polarized component and modeled vegetation component has shown higher correlation with underneath soil moisture than directly correlating with derived un-polarized component. This study indicates that the volume component derived from hybrid polarimetric SAR data has return signals from vegetation as well as underneath soil.</p>

scholarly journals Mapping Wheat Dry Matter and Nitrogen Content Dynamics and Estimation of Wheat Yield Using UAV Multispectral Imagery Machine Learning and a Variety-Based Approach: Case Study of Morocco

2021 ◽  
Vol 3 (1) ◽  
pp. 29-49
Author(s):  
Ghizlane Astaoui ◽  
Jamal Eddine Dadaiss ◽  
Imane Sebari ◽  
Samir Benmansour ◽  
Ettarid Mohamed
Keyword(s):  
Random Forest ◽  
Nitrogen Uptake ◽  
Dry Matter ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Wheat Crop ◽  
Multispectral Imagery ◽  
Biophysical Parameters ◽  
Phenological Stages ◽  
Wheat Varieties

Our work aims to monitor wheat crop using a variety-based approach by taking into consideration four different phenological stages of wheat crop development. In addition to highlighting the contribution of Red-Edge vegetation indices in mapping wheat dry matter and nitrogen content dynamics, as well as using Random Forest regressor in the estimation of wheat yield, dry matter and nitrogen uptake relying on UAV (Unmanned Aerial Vehicle) multispectral imagery. The study was conducted on an experimental platform with 12 wheat varieties located in Sidi Slimane (Morocco). Several flight missions were conducted using eBee UAV with MultiSpec4C camera according to phenological growth stages of wheat. The proposed methodology is subdivided into two approaches, the first aims to find the most suitable vegetation index for wheat’s biophysical parameters estimation and the second to establish a global model regardless of the varieties to estimate the biophysical parameters of wheat: Dry matter and nitrogen uptake. The two approaches were conducted according to six main steps: (1) UAV flight missions and in-situ data acquisition during four phenological stages of wheat development, (2) Processing of UAV multispectral images which enabled us to elaborate the vegetation indices maps (RTVI, MTVI2, NDVI, NDRE, GNDVI, GNDRE, SR-RE et SR-NIR), (3) Automatic extraction of plots by Object-based image analysis approach and creating a spatial database combining the spectral information and wheat’s biophysical parameters, (4) Monitoring wheat growth by generating dry biomass and wheat’s nitrogen uptake model using exponential, polynomial and linear regression for each variety this step resumes the varietal approach, (5) Engendering a global model employing both linear regression and Random Forest technique, (6) Wheat yield estimation. The proposed method has allowed to predict from 1 up to 21% difference between actual and estimated yield when using both RTVI index and Random Forest technique as well as mapping wheat’s dry biomass and nitrogen uptake along with the nitrogen nutrition index (NNI) and therefore facilitate a careful monitoring of the health and the growth of wheat crop. Nevertheless, some wheat varieties have shown a significant difference in yield between 2.6 and 3.3 t/ha.

Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data

2017 ◽  
Vol 33 (9) ◽  
pp. 942-956 ◽  
Author(s):  
P. Kumar ◽  
R. Prasad ◽  
D. K. Gupta ◽  
V. N. Mishra ◽  
A. K. Vishwakarma ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Growth Parameters ◽  
Crop Growth ◽  
Wheat Crop ◽  
Winter Wheat Crop ◽  
Sar Data

scholarly journals NEW VEGETATION INDICES FOR FULL AND COMPACT POLARIMETRIC SAR DATA: IN PREPARATION FOR THE RADARSAT CONSTELLATION MISSION (RCM)

2020 ◽  
Vol IV-3/W2-2020 ◽  
pp. 41-46
Author(s):  
D. Ratha ◽  
D. Mandal ◽  
S. Dey ◽  
A. Bhattacharya ◽  
A. Frery ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Geodesic Distance ◽  
Vegetation Indices ◽  
Series Data ◽  
Biophysical Parameters ◽  
Area Index ◽  
Plant Area Index ◽  
Sar Data

Abstract. In this paper, we present two radar vegetation indices for full-pol and compact-pol SAR data, respectively. Both are derived using the notion of a geodesic distance between observation and well-known scattering models available in the literature. While the full-pol version depends on a generalized volume scattering model, the compact-pol version uses the ideal depolariser to model the randomness in the vegetation. We have utilized the RADARSAT Constellation Mission (RCM) time-series data from the SAMPVEX16-MB campaign in the Manitoba region of Canada for comparing and assessing the indices in terms of the change in the biophysical parameters as well. The compact-pol data for comparison is simulated from the full-pol RCM time series. Both the indices show better performance at correlating with biophysical parameters such as Plant Area Index (PAI) and Volumetric Water Content (VWC) for wheat and soybean crops, in comparison to the state-of-art Radar Vegetation Index (RVI) of Kim and van Zyl. These indices are timely for the upcoming release of the data from the RCM, which will provide data in both full and compact-pol modes, aimed at better crop monitoring from space.

scholarly journals Role of Polarimetric SAR data for discrimination/biophysical parameters of crops based on canopy architecture

2014 ◽  
Vol XL-8 ◽  
pp. 737-744 ◽  
Author(s):  
D. Haldar ◽  
M. Chakraborty ◽  
K. R. Manjunath ◽  
J. S. Parihar
Keyword(s):  
Canopy Architecture ◽  
Biophysical Parameters ◽  
Sar Images ◽  
Tropical Regions ◽  
Tropical Crops ◽  
Study Evaluation ◽  
Wide Range ◽  
Agricultural Applications ◽  
Sar Data ◽  
Rabi Crops

Synthetic Aperture Radar (SAR) sensors have great potential for a wide range of agricultural applications, owing to their capability of all-weather observation. It is particularly useful in tropical regions in Asia where most of the crops are grown in rainy season. The use of SAR images for the assessment of rice-planted area is in operational stage in Asian countries owing to its characteristic temporal signature however, applications of SAR images for the estimation of biophysical plant variables are challenging, especially for crop scattering and discrimination in case of other tropical crops. Canopy geometry and architecture mainly govern the interaction of microwave signal with the vegetation. In this study evaluation of C-band SAR data at different polarization combinations in linear as well as circular polarimetric imaging modes for rabi crops and other associated landuse has been attempted. Also understanding the scattering response of various crops based on canopy architecture was attempted. The scattering parameters were found to vary for planofiles and erectophiles, partitioning of scattering and absorption were determined.

scholarly journals Inversion of Rice Biophysical Parameters Using Simulated Compact Polarimetric SAR C-Band Data

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2271 ◽  
Author(s):  
Xianyu Guo ◽  
Kun Li ◽  
Yun Shao ◽  
Zhiyong Wang ◽  
Hongyu Li ◽  
...  
Keyword(s):  
Water Content ◽  
Cloud Model ◽  
Volumetric Water Content ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Biophysical Parameters ◽  
Area Index ◽  
Water Cloud ◽  
Sar Data ◽  
New Generation

Timely and accurate estimation of rice parameters plays a significant role in rice monitoring and yield forecasting for ensuring food security. Compact-polarimetric (CP) synthetic aperture radar (SAR), a good compromise between the dual- and quad-polarized SARs, is an important part of the new generation of Earth observation systems. In this paper, the ability of CP SAR data to retrieve rice biophysical parameters was explored using a modified water cloud model. The results showed that S1 was superior to other CP variables in rice height inversion with a coefficient of determination (R2) of 0.92 and a root-mean-square error (RMSE) of 5.81 cm. RL was the most suitable for inverting the volumetric water content of the rice canopy, with an R2 of 0.95 and a RMSE of 0.31 kg/m3. The m-χ decomposition produced the highest accuracies for the ear biomass: R2 was 0.89 and RMSE was 0.17 kg/m2. The highest accuracy of leaf area index (LAI) retrieval was obtained for RH (right circular transmit and horizontal linear receive) with an R2 of 0.79 and a RMSE of 0.33. This study illustrated the capability of CP SAR data with respect to retrieval of rice biophysical parameters, especially for height, volumetric water content of the rice canopy, and ear biomass, and this mode may offer the best option for rice-monitoring applications because of swath coverage.

scholarly journals POTENTIAL OF C-BAND HYBRID POLARIMETRIC RISAT-1 SAR DATA FOR WHEAT CROP HEIGHT ESTIMATION

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 637-641
Author(s):  
H. S. Srivastava ◽  
T. Sivasankar
Keyword(s):  
Crop Insurance ◽  
Irrigation Management ◽  
Important Application ◽  
Stokes Parameters ◽  
Wheat Crop ◽  
Growth Monitoring ◽  
Polarimetric Sar ◽  
Sar Data ◽  
Moisture Estimation ◽  
Higher Sensitivity

<p><strong>Abstract.</strong> The advancement of hybrid polarimetry and the launch of Radar Imaging SATellite-1 (RISAT-1), the first Earth observing satellite with capability to acquire hybrid polarimetric SAR data, have given opportunity to explore the potentials of this data for various geo-spatial applications. Among these, crop growth monitoring and soil moisture estimation is one of the important application for proper irrigation management, yield forecasting and crop insurance systems. The objective of this paper is to provide a better understanding of the sensitivity of RISAT-1 hybrid polarimetric SAR data for wheat height. However, backscattering coefficients have shown less sensitive towards wheat height, polarimetric parameters such as Stokes parameters and scattering decomposition parameters have shown higher sensitivity. The analysis observed the highest correlation coefficient (|r|) and lowest RMSE (in m) for g<sub>3</sub> with 0.61 and 0.18 respectively followed by odd bounce with 0.59 and 0.2 respectively.</p>

scholarly journals Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

2015 ◽  
Vol XL-7/W3 ◽  
pp. 39-44 ◽  
Author(s):  
A. Kolotii ◽  
N. Kussul ◽  
A. Shelestov ◽  
S. Skakun ◽  
B. Yailymov ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Biophysical Model ◽  
Wheat Crop ◽  
Biophysical Parameters ◽  
Model Based ◽  
Winter Wheat Crop ◽  
Yield Forecasting ◽  
Crop Yield Forecasting

Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.

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