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Radar Vegetation Index as an Alternative to NDVI for Monitoring of Soyabean and Cotton
Abstract and Figures
The study explores the possibility of Radar Vegetation Index (RVI) for
vegetation monitoring in Cotton and Soya bean fields as an alternative to monitoring with
Normalized Difference Vegetation Index (NDVI). The RVI is the measure randomness of
scattering has been proposed as a method for monitoring the level of vegetation growth,
particularly when time series of data are available. The SAR can penetrate the clouds
thus it has the potential to monitor the crop growth in all the seasons. This point is very
important in the context of Indian subcontinent since monsoon clouds hampering the
monitoring of crops in such season. The present study carried out in Vidharba region
of Maharashtra, India using four Radarsat-2 data sets acquired in the monsoon season
of 2011 (ie: 13thJuly 2011, 06thAugust 2011, 30 August 2011, 23rdSeptember 2011). The
derived RVI was compared with the MODIS NDVI product of same date. The research
shows some significant improvements in the RVI technique than NDVI in some context.
The RVI is linearly increasing as crop grows unlike the NDVI becomes saturated after
level of growth in the crop. The Cultivation started in the first week of July 2011, the
NDVI becomes saturated mostly in second week of August, 2011 however RVI shows
further increase in 30th August 2011 with the growth in the vegetation. These particular
findings attributed to the fact that maximum randomness of scattering in microwave
signal occurs where plant spread fully whereas peak greenness (NDVI) occurs before
that. The present study found that RVI can be utilised in place of NDVI for vegetation
monitoring thus monitoring of crops is possible even in the monsoon season of India.
This method can be better used with RISAT descending mode since its acquiring image
on optimum incident angle of around 360 look angle thus it is highly suitable for
vegetation monitoring. The Radar Vegetation Index can also have the prospect of using
in soil moisture models in vegetated fields where NDVI is a critical one (Kim and J. Van
Zyl ., 2009). The result of this study can be effectively used for monitoring soyabean
and cotton and also can be used as a vegetation mask for soil moisture monitoring. The
further study is required to derive biophysical parameters from RVI and application of
RVI in soil moisture estimation in vegetated fields
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... approach, which is commonly employed in research using optical image processing (De Luca et al., 2022). RVI, which has a value between 0 and 1 were used to assess the randomness of scattering in SAR (Kumar et al., 2013). Sentinel-1, being equipped with dual polarization capabilities, offers both VH and VV polarizations, as discussed in the studies conducted by Charbonneau et al. (2005) and Kim and Van Zyl (2009). ...
... The range of the RVI typically spans from 0 to 1. According to (Ratha et al., 2019) the RVI values for vegetation areas range from 0.3 to 0.6 and For a smooth bare surface, the RVI is near zero and it increases as the crop grows as volume scattering (Kumar et al., 2013). This study's RVI value range was around 0.309-0.360 ...
... These models enable the monitoring of plant phenological changes during the growing season. Also, according to Kumar et al. (2013), RVI is highly suitable than NDVI and can be used for monitoring crops throughout the crop cycle. However, RVI is impacted by vegetation and soil moisture, respectively, and these indices are also affected by long-term weather conditions (S. W. Kim et al., 2020). ...
Estimating the biophysical parameters during the phenology cycle are very important and the key parameter for indicating the productivity of oil palm plantations. In many countries, the oil palm plantation has a very large area, therefore remote sensing technology is needed to estimate biophysical parameters in large areas. The special characteristics and potential of Synthetic Aperture Radar (SAR) data in acquiring geometric and dielectric properties of biophysical parameters have led to their identification in the context of vegetation monitoring. This study, we have investigated and developed models for estimating the oil palm phenology by applying multiple linear regression (MLR). The methodology includes the biophysical parameters estimated using Sentinel-1A for extracting the canopy height model (CHM), radar vegetation index (RVI), backscattering on VV and VH, aboveground biomass, texture entropy, and texture energy. Then applied multiple linear regression (MLR) analysis for developing model and assess its ability. The result found the best model for estimating oil palm phenology using 4 parameters. The parameters are CHM, RVI, Backscatter on VV, Backscatter on VH and the best model for estimating oil palm phenology is + (-0.893) with R2 is 0.977 and RMSE is 1.290.
... However, NDVI is limited by its dependence on optical data. In contrast, the Radar Vegetation Index (RVI), which is calculated from radar data, is a powerful tool for monitoring and analyzing temporal variations in vegetation and offers a reliable alternative that allows for consistent, all-weather monitoring (Kumar et al., 2013). While other indices like the Dual-Polarization RVI (DpRVI) exist, RVI's simpler formulation and effectiveness in time series agricultural monitoring support its use in this study (Salsabila et al., 2021;Szigarski et al., 2018). ...
... The RVI is a widely recognized microwave-based vegetation index utilized for crop growth monitoring. For dual-pol SAR data, RVI is derived using the following formula (Kumar et al., 2013) Here, the radar backscattering coefficient (σ 0 ) indicates the intensity of the signal scattered by the surface. It is also known as radar cross-section per unit area (Kaplan et al., 2021). ...
Paddy crop mapping is essential for agricultural monitoring, ensuring food security, and enhancing resource allocation. This study observes the Cauvery Delta Zone (CDZ), recognized as the rice bowl of Tamil Nadu and a crucial area for paddy farming in India. The study seeks to elucidate rice-growing trends over three years (2021–2023) by examining the regional variability of the Radar Vegetation Index (RVI) throughout a paddy crop growing season (June to September). A temporal examination of the RVI and the cross-polarization ratio (VH/VV) demonstrates a good correlation of 0.79, enhancing the comprehension of paddy crop dynamics. Additionally, machine learning algorithms such as random forest (RF), support vector machine (SVM), and decision tree (DT) are utilized on radar data in both VV and VH polarizations to improve the classification of paddy fields. The accuracy evaluation indicates that the RF algorithm exhibits superior performance, achieving accuracies of 86.72% in VH mode and 86.42% in VV mode. The results underscore the efficacy of integrating radar-based indices with machine learning methodologies for proficient agricultural surveillance. These findings offer essential assistance for enhancing crop yield, optimizing resource management, and enabling informed decision-making in the Cauvery Delta Zone.
... From these images, backscattering coefficients were extracted for σ 0 VV (vertical transmit and receive polarization), σ 0 VH (vertical transmit and horizontal receive polarization), σ 0 VV + σ 0 VH, and the Radar Vegetation Index (RVI). The use of RVI (Equation 1) is the replacement for NDVI which is crucial for vegetation health monitoring (Kumar et al., 2013). These backscattering values are sensitive to soil moisture and vegetation characteristics, making them suitable indicators for modeling soil moisture levels (Wagner et al., 2007). ...
Soil moisture is a fundamental variable in the Earth’s hydrological cycle and vital for development of agricultural water management practices. The present study provided a comprehensive evaluation of a wide range of advanced machine learning algorithms for Soil Moisture (SM) estimation from microwave Synthetic Aperture Radar (SAR) backscatter observations over the wheat fields. From the wheat fields, samplings were performed to collect the in situ datasets on three different dates concurrent to the Sentinel-1 overpasses. The backscattering coefficients were taken as the input variables and SM as the output variable for the training and testing of different models. The performance analysis of RMSE, R-squared, and correlation coefficients revealed that the Random Forest (RF) and Convolutional Neural Network (CNN) models demonstrated superior performance for SM estimation over the wheat field. Specifically, the RF model exhibited outstanding accuracy and robustness in both the training [RMSE (%): 3.44, R-squared: 0.88, correlation: 0.95] and validation phases [RMSE (%): 7.06, R-squared: 0.61, correlation: 0.8], marking it as the most effective model followed by the CNN model with [RMSE (%): 3.9, R-squared: 0.84, correlation: 0.92] during training and [RMSE (%): 8.44, R-squared: 0.43, correlation: 0.67] for validation, highlighting challenges in the model generalisation.
... Where FVC RVI represents the community structure and is computed by means of the pixel dichotomy model. Given the correlation between RVI and NDVI (Kim et al. 2015;Kumar, Rao, and Sharma 2013), as well as the intermittent nature of NDVI acquisitions, the combination of the more temporally continuous RVI and the pixel dichotomy model (Gao et al. 2020) is employed to obtain FVC RVI : ...
Addressing challenges in crop growth monitoring, such as limited assessment dimensions, incomplete coverage of growth cycles, and limited deep learning (DL) interpretability, a novel dual-pol SAR rice growth monitoring method using a new crop growth index (CGI) and an interpretable DL architecture is proposed. Initially, radar vegetation indices, polarimetric decomposition parameters, and backscattering coefficients characterize rice growth in multiple dimensions. Subsequently, a CGI is designed, combining fractional vegetation cover and leaf chlorophyll content to accurately depict rice growth status, capturing both the structural and physiological activity of rice. Finally, an interpretable DL architecture incorporating a feature selection explainer and a feature-aware enhanced segmentation model is introduced. This architecture incorporates feature-wise variable learning networks, interprets the importance of individual features, and optimizes the feature composition, significantly enhancing the interpretability of the DL model. This architecture is also applicable to other neural networks. The method is applied in Suihua City, Heilongjiang Province, China, using Sentinel-1 dual-pol data from 2022 and 2023. Experiments indicate that the proposed method achieves an overall accuracy of 90.57% in the test set and a generalization accuracy of 90.43%. The integration of CGI and the interpretable DL architecture enhances the reliability of rice growth monitoring results.
... Otherwise, RVI is a measurement of volumetric scattering, whereas NDVI is a measure of greenness due to the presence of chlorophyll. The plant attains maximum greenness before it reaches volumetric maturity (Kumar et al. 2013). ...
Remote sensing plays an increasingly important role in agriculture, especially in monitoring the quality of agricultural crops. Optical sensing is often limited in Central Europe due to cloud cover; therefore, synthetic aperture radar data is increasingly being used. However, synthetic aperture radar data is limited by more difficult interpretation mainly due to the influence of speckles. For this reason, its use is often limited to larger territorial units and field blocks. The main aim of this study therefore was to verify the possibility of using satellite synthetic aperture radar images to assess the within-field variability of winter wheat. The lowest radar vegetation index values corresponded to the area of the lowest production potential and the greatest damage to the stand. Also for VH and VV polarizations, the highest values corresponded to the area of the lowest stand quality. Qualitative changes in the stand across the zones defined by frost damage and production potential were assessed with the help of the logistic regression model with resampled data for 10, 50, and 100 m pixel size. The best correlation coefficients were achieved at a spatial resolution of 50 m for both options. The F-score still yielded a promising result ranging from 0.588 to 0.634 for frost damage categories. The regression model of the production potential per-formed slightly better in terms of the F-score, recall, and precision at higher resolutions. It was proved that modern statistical methods could be used to reduce problems associated with speckles of radar images for practical purposes.
Leaf Area Index (LAI) is a crucial indicator for assessing plant growth, canopy structure, photosynthetic capacity, and overall productivity. The Radar Vegetation Index (RVI), a well-established microwave metric, serves as an effective tool for retrieving the LAI due to its sensitivity to vegetation characteristics. The primary objective of utilizing RVI in LAI studies is to improve the accuracy and reliability of LAI estimation, where optical methods may be hindered by atmospheric conditions. Over the past decade, numerous studies have explored the relationship between RVI and LAI, highlighting the potential of RVI for accurate LAI estimation in crops. In particular, for rice crop analysis in this study, the RVI is derived by incorporating the Degree of Polarization (DOP) from a 2 × 2 covariance matrix as the coefficient, along with the polarization backscatter of Sentinel-1 C-band Synthetic Aperture Radar (SAR) data. The study also explores RVI derivation from M-chi (m-χ) and M-delta (m-δ) decomposition (assuming circularity in dual-polarized data) and linear backscattering intensities. Using the RVI’s, machine learning regression models are applied to retrieve LAI. The DOP over crop period, the temporal analysis of RVI, and in-situ LAI has been employed to examine trends during crop growth. Notably, among all derived RVIs, the one obtained using the DOP technique, particularly when combined with random forest regression, consistently exhibits superior performance for rice crop LAI estimation (R = 0.91; RMSE = 0.25 m2/m2), whereas, the R value for other models ranges a lower value of 0.63 to a higher value of 0.83 with RMSE of higher value 0.64 m2/m2 to a lower value of 0.32 m2/m2. The findings in the study highlights the sensitivity of SAR data to the DOP and the vegetation structure of rice crops in small-scale agricultural fields.
As a crucial economic crop, the health status of cotton directly impacts farmers' income and the national economy. Therefore, timely and accurate detection and identification of cotton diseases and pests are of significant importance, aiding in reducing the adverse effects of diseases and pests on cotton yield and quality. Existing research struggles to address the balance between resource consumption and detection accuracy in cotton disease and pest detection. Moreover, diseases and pests often occur beneath the canopy, and the orthorectification of drone imagery may result in insufficient feature information and prolonged processing time, among other issues. To address the aforementioned issues, this paper proposes a precise detection method for cotton Verticillium wilt based on unmanned aerial vehicle multi-angle remote sensing guided by a satellite time series monitoring model. Specifically, first, combining Sentinel-1 microwave and Sentinel-2 optical time series images, we constructed a cotton Verticillium wilt monitoring model based on extreme gradient boosting algorithm to identify areas affected by the disease invasion. Subsequently, after identifying the blocks affected by the disease, we collected multi-spectral remote sensing data captured from multiple angles by unmanned aerial vehicles and compared different combinations of vegetation indices and bands. Finally, we constructed a precise classification model for cotton Verticillium wilt based on support vector machine radial basis function classification method. Experimental results indicate that the joint microwave and optical time-series monitoring model achieved OA of 81.73% and Kappa coefficient of 0.63, meeting the monitoring requirements of the first stage. Based on the SVM with RBF and the optimal band combination, the OA value of the comprehensive image captured at -58° angle reached 96.74%, with Kappa coefficient of 0.93, meeting the requirements of precise classification detection in the second stage.
The use of SAR data to monitoring agricultural crops is a promising area of research designed to complement existing methods and technologies based on the analysis of multispectral images. The main advantages of vegetation indices calculated from SAR data include their sensitivity to the polarimetric properties of the backscatter intensity, its scattering characteristics, and independence from cloud cover. This is especially important for the territory of the south of the Russian Far East, whose monsoon climate provides humid and cloudy weather during the period when crops gain maximum biomass. For arable lands in the Khabarovsk Territory and the Amur Region, a total of 64 Sentinel-1 SAR images were obtained from May to October 2021. For each date, the values of the DpRVI, RVI, VH/VV indices were calculated and time series were constructed for the entire observation period for individual fields (342 fields in total). NDVI time series were constructed from Sentinel-2 multispectral images using a cloud mask. The characteristics of time series extremes were calculated for different types of arable land: soybeans, oats, and fallows. It was shown that for each crop the seasonal curves DpRVI, RVI, VH/VV had a characteristic appearance. It was found that the DpRVI demonstrated the highest stability – the coefficients of variation of the seasonal variation of DpRVI were significantly lower than those for RVI and VH/VV. It was also revealed that the similarity between the curves of these indices remained for regions quite distant from each other - the Khabarovsk Territory and the Amur Region. The main characteristics of the seasonal variation of time series of radar indices were calculated in comparison with NDVI - the magnitude of the maximum, the date of the maximum and the values of the coefficient of variation for these indicators. It was found, firstly, that the values of these indicators in different regions are similar to each other; secondly, the variability of the maximum and the day of the maximum for DpRVI is lower than for RVI and VH/VV; thirdly, the variability of the maximum and the day of the maximum for DpRVI is comparable to NDVI. Thus, time series of radar indices DpRVI, RVI, VH/VV for the main types of agricultural lands in the south of the Far East have distinctive features and can be used in classification problems, yield modeling and crop rotation control.
The preservation and augmentation of soil organic carbon (SOC) stocks is critical to designing climate change mitigation strategies and alleviating global warming. However, due to the susceptibility of SOC stocks to environmental and topo-climatic variability and changes, it is essential to obtain a comprehensive understanding of the state of current SOC stocks both spatially and vertically. Consequently, to effectively assess SOC storage and sequestration capacity, precise evaluations at multiple soil depths are required. Hence, this study implemented an advanced Deep Neural Network (DNN) model incorporating Sentinel-1 Synthetic Aperture Radar (SAR) data, topo-climatic features, and soil physical properties to predict SOC stocks at multiple depths (0–30 cm, 30–60 cm, 60–100 cm, and 100–200 cm) across diverse land-use categories in the KwaZulu-Natal province, South Africa. There was a general decline in the accuracy of the DNN model’s prediction with increasing soil depth, with the root mean square error (RMSE) ranging from 8.34 t/h to 11.97 t/h for the four depths. These findings imply that the link between environmental covariates and SOC stocks weakens with soil depth. Additionally, distinct factors driving SOC stocks were discovered in both topsoil and deep-soil, with vegetation having the strongest effect in topsoil, and topo-climate factors and soil physical properties becoming more important as depth increases. This underscores the importance of incorporating depth-related soil properties in SOC modelling. Grasslands had the largest SOC stocks, while commercial forests have the highest SOC sequestration rates per unit area. This study offers valuable insights to policymakers and provides a basis for devising regional management strategies that can be used to effectively mitigate climate change.
There are several challenges in estimating soil moisture from radar remote sensing over agricultural fields in eastern Canada. To begin, snow cover or frozen ground is observed from November to April. From April to May, agricultural activities (e.g., ploughing and sowing) change the surface roughness from week to week thereby limiting the applicability of multitemporal and multi-incidence angle approaches. Techniques using a priori information on surface roughness are difficult to apply since the type of crop often changes from year to year. Here, we present an approach using effective roughness parameters (i.e., effective root mean square height and effective correlation length) that are obtained using an empirical relationship (independent of the crop type) between the root mean square height and the correlation length. The effective parameters allow us to resolve the Integral Equation Model for observed incidence angle and backscattering coefficient in HH and VV polarizations (σ °HH and σ °VV) using a look-up table. An additional challenge is posed by the growth of vegetation that begins in May. Three-component decompositions and radar vegetation indices were used to characterize the vegetation in agricultural fields. Surface backscattering coefficients in HH and VV polarizations (σ °SURF-HH and σ °SURF-VV) were calculated using the decompositions. An improvement in estimates of soil moisture was observed with the use of surface backscattering coefficients for bare soil and sparsely vegetated fields instead of the total backscattering.
Vegetation water content (VWC) is an important biophysical parameter and has a significant role in the retrieval of soil moisture using microwave remote sensing. Here, the radar vegetation index (RVI) was evaluated for estimating VWC. Analysis utilized a data set obtained by a ground-based multifrequency polarimetric scatterometer system, with a single incidence angle of 40, during an entire growth period of rice and soybean. Temporal variations of the backscattering coefficients for the L-, C-, and X-bands, RVI, VWC, leaf area index, and normalized difference vegetation index were analyzed. The L-band RVI was found to be correlated to the different vegetation indices. Prediction equations for the estimation of VWC from the RVI were developed. The results indicated that it was possible to estimate VWC with an accuracy of 0.21 using L-band RVI observations. These results demonstrate that valuable new information can be extracted from current and future radar satellite systems on the vegetation condition of two globally important crop types. The results are directly applicable to systems such as the proposed NASA Soil Moisture Active Passive satellite.
Electromagnetic scattering from a rough surface is a function of both surface roughness and dielectric constant of the scattering surface. Therefore, in order to estimate soil moisture of a bare surface accurately from radar measurements, the effects of surface roughness must be compensated for properly. Several algorithms have been developed to estimate soil moisture from a polarimetric radar image, all with limited ranges of applicability. No theoretical algorithm has been reported to retrieve volumetric soil moisture of a vegetated surface. In this paper, we examine a different approach to estimate soil moisture that exploits the fact that the backscattering cross section from a natural object changes over short timescales mainly due to variations in soil moisture. We develop a model function that expresses copolarized backscattering cross sections (sigmahh and sigmavv) in terms of volumetric soil moisture using L-band experimental data for both bare and vegetated surfaces. In order to estimate soil moisture, two unknowns in the model function must be determined. We propose a viable approach to determine these two unknowns using combined radiometer and radar data. This time-series approach also provides a framework to utilize a priori knowledge on soil moisture to improve the retrieval accuracy of volumetric soil moisture. We demonstrate that this time-series algorithm is a simple and robust way to estimate soil moisture for both bare and vegetated surfaces.
Several successful algorithms have been developed to estimate soil moisture of bare surfaces. We previously reported a new algorithm using the tilted Bragg approximation. However, these algorithms are only applicable to bare surfaces. When vegetation is present, soil moisture is typically underestimated by bare surface algorithms. In order to derive soil moisture under vegetation, we have to understand the complex scattering process due to vegetation. Our main interest is to retrieve the global soil moisture information using Hydros L-band polarimetric radar data. The Hydros mission will provide the first global view of land soil moisture using L-band radar and radiometer. The unique characteristics of the Hydros data are the availability of the low resolution soil moisture information from radiometer data and the continuous time series radar data collected at the same incidence angle. In this paper, we will examine a potential inversion algorithm to retrieve soil moisture under vegetation canopies using Hydros L-band polarimetric radar data
Use of Dual Polarization and Multi-Incidence SAR for soil permeability mapping
- F Charbonneau
- M Trudel
- R Fernandes
Charbonneau, F., Trudel, M., and Fernandes, R.
2005. Use of Dual Polarization and Multi-Incidence SAR for soil permeability mapping.
In: Advanced Synthetic Aperture Radar (ASAR)
2005, St-Hubert, Canada.