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

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.

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Using a Modified Water Cloud Model to Retrive Leaf Area Index (LAI) from Radarsat-2 SAR Data Over an Agriculture Area

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8518645 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yichuan Ma ◽

Minfeng Xing ◽

Xiliang Ni ◽

Jinfei Wang ◽

Jiali Shang ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Cloud Model ◽

Area Index ◽

Water Cloud ◽

Sar Data

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ESTIMATION OF BIOPHYSICAL PARAMETERS OF WHEAT CROP THROUGH MODIFIED WATER CLOUD MODEL USING SATELLITE DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-239-2018 ◽

2018 ◽

Vol IV-5 ◽

pp. 239-244 ◽

Cited By ~ 4

Author(s):

V. P. Yadav ◽

R. Prasad ◽

R. Bala ◽

A. K. Vishwakarma ◽

S. A. Yadav

Keyword(s):

Satellite Data ◽

Cloud Model ◽

Parameters Estimation ◽

Optical Data ◽

Wheat Crop ◽

Landsat 8 ◽

Biophysical Parameters ◽

Area Index ◽

Vegetation Fraction ◽

Water Cloud

<p><strong>Abstract.</strong> A modified water cloud model (WCM) was used to estimate the biophysical parameters of wheat crop using Sentinel-1A and Landsat-8 satellite images. The approach of combining the potential of SAR and optical data provided a new technique for the estimation of biophysical parameters of wheat crop. The biophysical parameters estimation was done using non-linear least squares optimization technique by minimizing the cost function between the backscattering coefficients (&sigma;<sup>0</sup>) computed from the Sentinel-1A image and simulated by the modified WCM followed by look up table algorithm(LUT). The modified WCM integrates the full account of backscattering response on vegetation and bare soil by adding vegetation fraction. The modified WCM was found more sensitive than the original WCM because of incorporation of vegetation fraction (f<sub>veg</sub>) derived from the Landsat-8 satellite data. The estimated values of leaf area index (LAI) by modified WCM at VV polarization shows good correlation (R<sup>2</sup><span class="thinspace"></span>=<span class="thinspace"></span>83.08<span class="thinspace"></span>% and RMSE<span class="thinspace"></span>=<span class="thinspace"></span>0.502<span class="thinspace"></span>m<sup>2</sup>/m<sup>2</sup>) with the observed values. Whereas, leaf water area index (LWAI) shows comparatively poor correspondence (R<sup>2</sup><span class="thinspace"></span>=<span class="thinspace"></span>76<span class="thinspace"></span>% and RMSE<span class="thinspace"></span>=<span class="thinspace"></span>0.560<span class="thinspace"></span>m<sup>2</sup>/m<sup>2</sup>) with the observed data in comparison to LAI estimation at VV polarization. The performance indices show that the modified WCM was found more accurate for the estimation of wheat crop parameters during the whole growth season in Varanasi district, India. Thus, the modified WCM shows significant potential for the accurate estimation of LAI and LWAI of wheat crop on incorporating both SAR and optical satellite data.</p>

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

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-209-2018 ◽

2018 ◽

Vol IV-5 ◽

pp. 209-214 ◽

Cited By ~ 1

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 &sim;31&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>

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Construction and calibration of TDR probes for volumetric water content estimation in a Distroferric Red Latosol

Engenharia Agrícola ◽

10.1590/s0100-69162013000500004 ◽

2013 ◽

Vol 33 (5) ◽

pp. 919-928 ◽

Cited By ~ 3

Author(s):

Rosimaldo Soncela ◽

Silvio C. Sampaio ◽

Marcio A. Vilas Boas ◽

Maria H. F. Tavares ◽

Adriana Smanhotto

Keyword(s):

Water Content ◽

Irrigation Management ◽

Time Domain Reflectometry ◽

Volumetric Water Content ◽

Coefficient Of Determination ◽

Calibration Model ◽

Advantages And Disadvantages ◽

Disturbed Soil ◽

Red Latosol ◽

The Time Domain

The determination of volumetric water content of soils is an important factor in irrigation management. Among the indirect methods for estimating, the time-domain reflectometry (TDR) technique has received a significant attention. Like any other technique, it has advantages and disadvantages, but its greatest disadvantage is the need of calibration and high cost of acquisition. The main goal of this study was to establish a calibration model for the TDR equipment, Trase System Model 6050X1, to estimate the volumetric water content in a Distroferric Red Latosol. The calibration was carried out in a laboratory with disturbed soil samples under study, packed in PVC columns of a volume of 0.0078m³. The TDR probes were handcrafted with three rods and 0.20m long. They were vertically installed in soil columns, with a total of five probes per column and sixteen columns. The weightings were carried out in a digital scale, while daily readings of dielectric constant were obtained in TDR equipment. The linear model θν = 0.0103 Ka + 0.1900 to estimate the studied volumetric water content showed an excellent coefficient of determination (0.93), enabling the use of probes in indirect estimation of soil moisture.

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Leaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data

Geocarto International ◽

10.1080/10106049.2019.1624984 ◽

2019 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Vijay Pratap Yadav ◽

Rajendra Prasad ◽

Ruchi Bala

Keyword(s):

Time Series ◽

Leaf Area Index ◽

Leaf Area ◽

Satellite Data ◽

Cloud Model ◽

Wheat Crop ◽

Area Index ◽

Water Cloud ◽

Optical Satellite Data ◽

Index Estimation

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Linking an agro-meteorological model and a water cloud model for estimating soil water content over wheat fields

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2020.105833 ◽

2020 ◽

Vol 179 ◽

pp. 105833

Author(s):

Dong Han ◽

Pengxin Wang ◽

Kevin Tansey ◽

Xijia Zhou ◽

Shuyu Zhang ◽

...

Keyword(s):

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Cloud Model ◽

Meteorological Model ◽

Water Cloud

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MODEL SIMULASI PERKEMBANGAN, PERTUMBUHAN DAN NERACA AIR TANAMAN KENTANG PADA DATARAN TINGGI DI INDONESIA

Informatika Pertanian ◽

10.21082/ip.v22n1.2013.p53-64 ◽

2015 ◽

Vol 22 (1) ◽

pp. 53 ◽

Cited By ~ 1

Author(s):

NFN Salwati

Keyword(s):

Water Content ◽

Field Experiments ◽

Potato Crop ◽

Model Development ◽

Potato Production ◽

Coefficient Of Determination ◽

Leaf Biomass ◽

Area Index ◽

Root Tuber ◽

Model Predictions

<div data-canvas-width="788.5249999999997">This research aims to construct a simulation model of development, growth and waterbalance of potato crop. Reasearch</div><div data-canvas-width="802.7183333333334">also predicts climate change impact on potato productivity in several potato production center in Indonesia. The crop</div><div data-canvas-width="802.6816666666664">model being constructed explains process mechanism of development and growth during crop life cycle as a response</div><div data-canvas-width="802.7166666666672">to fluctuation of climatic. Three field experiments were conducted at three locations at Pacet and Galudra in West Java</div><div data-canvas-width="802.7066666666665">Province, and at Kerinci in Jambi Province, to support the model development; for model calibration (Pacet) and model</div><div data-canvas-width="802.6899999999999">validation (Galudra and Kerinci). Paired t-test between model predictions of Granola variety with observations showed</div><div data-canvas-width="802.7116666666665">that there were not significant differences (P> 0,05) on all variables tested, except leaf biomass. In Atlantic variety, there</div><div data-canvas-width="802.6650000000001">were not significant differences (P> 0,05) on root, tuber biomass and soil water content. Based on graphical test showed</div><div data-canvas-width="244.87999999999997">coefficient of determination were (R</div><div>2</div><div data-canvas-width="552.9633333333334">) greater than 0,80 for all variables.Generally, results on validation suggested that</div><div data-canvas-width="802.6866666666666">model predictions were not significantly different with field measurements at Galudra (Granola variety) and Kerinci</div><div data-canvas-width="802.6966666666668">(Atlantis and Granola variety) for variable of plant ages, biomass of root, stem, leaf and tuber, leaf area index, and soil</div><div data-canvas-width="98.58333333333331">water content. </div>

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

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-3-w2-2020-41-2020 ◽

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.

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First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields

Remote Sensing ◽

10.3390/rs11232769 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2769 ◽

Cited By ~ 2

Author(s):

Mohammad El Hajj ◽

Nicolas Baghdadi ◽

Jean-Pierre Wigneron ◽

Mehrez Zribi ◽

Clément Albergel ◽

...

Keyword(s):

Water Content ◽

Optical Depth ◽

Water Loss ◽

Vegetation Index ◽

Temporal Dynamics ◽

Irrigation Management ◽

Coefficient Of Determination ◽

Vegetation Monitoring ◽

Growth Cycles ◽

Sar Data

Monitoring crop status at plot scale in agricultural areas is essential for crop and irrigation management and yield optimization. The Vegetation Optical Depth (VOD) of canopy is directly related to the canopy water content, and thus, it represents an effective tool for crop health monitoring. Currently, VOD is provided at low spatial resolution which makes these estimations useless for vegetation monitoring at plot scale. Therefore, the aim of this study is to provide the first approach to estimate VOD at plot scale for non-irrigated plots from C-band Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data. The proposed approach was tested on a study site of 50 km × 50 km located in Catalonia, Spain. VOD estimates were provided for two crop growth cycles of non-irrigated crop types (barley, fallow, oat, wheat, and rapeseed). The relevance of VOD estimates was investigated for both growth cycles using temporal profiles of the Normalized Difference Vegetation Index (NDVI). It is shown that the temporal dynamics of VOD values computed from VV polarization fits that of NDVI with a medium to good coefficient of determination (R2 ranging from 0.39 to 0.61 for barley, fallow, oat, and wheat respectively). However, during the beginning of the senescence period in both cycles (mainly in May for winter crops), VOD decreases with the decrease in Vegetation Water Content (VWC) while NDVI keeps increasing as photosynthetic activity continues developing. This illustrates the importance of VOD in crop water loss (stress and/or transpiration) monitoring. The potential of VOD to spot water loss in vegetation is also demonstrated as the evening (18h00) VOD values are lower than those of morning (06h00) due to high daytime temperature that reduces water content in vegetation. Finally, it is shown that VOD values computed from VH polarization are not correlated with NDVI.

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