Dipankar Mandal

Indian Institute of Technology Guwahati | IIT Guwahati · School of Agro and Rural Technology

Studies on the sensitivity of microwave scattering to vegetation canopies have led the researchers to conclude that crop biophysical parameters can be modeled from Synthetic Aperture Radar (SAR) backscatter. In this study, we assess different methods of modeling the Leaf Area Index (LAI), an important biophysical indicator of crop productivity, fro...

Crop growth monitoring using compact-pol Synthetic Aperture Radar (CP-SAR) data is gaining attention with the rapid advancements toward operational applications. In this study, we propose a vegetation index for compact polarimetric (CP) SAR data (CpRVI). The CpRVI is derived using the concept of a geodesic distance between Kennaugh matrices project...

Sentinel-1 Synthetic Aperture Radar (SAR) data have provided an unprecedented opportunity for crop monitoring due to its high revisit frequency and wide spatial coverage. The dual-pol (VV-VH) Sentinel-1 SAR data are being utilized for the European Common Agricultural Policy (CAP) as well as for other national projects, which are providing Sentinel...

Estimation of bio-and geophysical parameters from Earth observation (EO) data is essential for developing applications on crop growth monitoring. High spatio-temporal resolution and wide spatial coverage provided by EO satellite data are key inputs for operational crop monitoring. In Synthetic Aperture Radar (SAR) applications , a semi-empirical mo...

Accurate crop classification with Synthetic Aperture Radar (SAR) data is a significant area of research and translating into practice from local to regional scale crop inventory mapping. With the growing accessibility to abundant data sources from both current and upcoming dual-polarimetric SAR missions, the capability to generate precise crop maps...

Potential of mobile fluorescence sensor measurements have been in focus for quantifying plant nitrogen (N) variability early in the crop growing season. Real time estimation of such N status indicators at field scale would enable precision management of N fertilizers. In standard practice, linear regression analysis involves the use of several fluo...

This paper proposes a novel multivariate Gaussian Process Regression (GPR) approach for multi-class crop classification. We have trained and validated the proposed model utilising backscatter information from E-SAR C- and L-band dual-polarimetric data acquired during the AGRISAR 2006 campaign. Further, we use the Product of Experts (PoE) fusion str...

This paper proposes a novel multivariate Gaussian Process Regression (GPR) approach for multi-class crop classification. Backscatter information from E-SAR L- and C-band dual-polarimetric data acquired during the AGRISAR 2006 campaign were used to train and validate the proposed Gaussian Process Classifier (GPC) model. The model’s accuracy was asse...

Delineating management zones (MZs) is considered one of the most important steps towards precision nitrogen (N) management, as MZs are required to optimize N inputs and improve environmental health. However, no reports have fully explored the optimization of regional MZs related to policymaking to achieve long-term Sustainable Development Goals. Th...

Groundwater (GW) resources are influenced by several factors like over exploitation, geological formation, climate etc. A long- term GW level trend detection is necessary for sustainable groundwater usage planning in future. In the present study, long- term (1990-2013) spatio-temporal analysis of GW levels during pre-monsoon and post-monsoon was do...

Remote sensing presents great potential for soil moisture mapping, although a lack of high-resolution, all-season suitable data and model-based complications hinders continuous mapping at the farm scale. The current study attempts to access optical (Sentinel-2) and microwave (Sentinel-1) based Water Cloud Model (WCM), optical and/or thermal-based T...

Soil moisture estimation from agriculture fields using SAR measurements is a challenging process owing to the presence of vegetation canopy. In this study, the soil moisture (SM) is retrieved from multi-polarization airborne Land C-band E-SAR data of different agriculture fields by using the radar parameter, Radar Vegetation Index (RVI). The retrie...

The term spatial and temporal analysis deals with applying transformation functionalities to the geometry and time content of data. Spatial variability is defined as deviation of a measured quantity parameter across different locations. It represents techniques to analyze locations, distribution, and association of spatial phenomena. Change in obje...

This chapter investigates multi-frequency (C-, L-, and P-bands) single-date AIRSAR data using Random Forest (RF) based polarimetric parameter selection for crop separation and classification. The RF classifier has an inherent parameter ranking and partial probability plot ability which gives not only the important parameters but also their optimal...

Characterizing nutrient variability has been the focus of precision agriculture research for decades. Previous research has indicated that in situ fluorescence sensor measurements can be used as a proxy for nitrogen (N) status in plants in greenhouse conditions employing static sensor measurements. Practitioners of precision N management require de...

Biophysical parameters are descriptors of crop growth and production estimates. Retrieval of these biophysical parameters from synthetic aperture radar sensors at operational scales is highly interesting given the increase in access to data from radar missions. Vegetation backscattering can be simulated using the water cloud model (WCM). Crop bioph...

Techniques such as proximal soil sampling are investigated to increase the sampling density and hence the resolution at which nutrient prescription maps are developed. With the advent of a commercial mobile fluorescence sensor, this study assessed the potential of fluorescence to estimate soil chemical properties and fertilizer recommendations. Thi...

Vegetation cover significantly influences the hydrometeorological processes of land surfaces. The heterogeneity of vegetation cover makes these processes more complex and impacts the interaction between water held in the soil matrix and vegetation cover. The backscatter measured by Synthetic Aperture Radar (SAR) is sensitive to target dielectric an...

This study investigates the performances of three radar vegetation indices derived from full (HH-VH-VV), compact (RH-RV), and dual (VV-VH or HH-HV) polarimetric Synthetic Aperture Radar (SAR) data for Leaf Area Index (LAI) and biomass estimation. We use the notion of a geodesic distance between the incoherent representation of radar measurements an...

Traditional crop cutting experiment-based yield estimation method captures the regional yield variability but lacks field-level information. Satellite images hold enormous crop information at finer spatial resolution. Crop yield mapping with optical images is particularly challenging if cloud-free images are unavailable during the crucial crop deve...

Using the cross-validation approach, strategies for estimating biophysical parameters are still pre-operational with synthetic aperture radar (SAR) data. In this regard, the Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR inter-comparison experiments provide an opportunity for the potential implementation of cross-validation strateg...

Semi-empirical models for radar scattering from vegetation are discussed in this chapter. The evolution of semi-empirical approaches from the dielectric slab model to the Water Cloud Model (WCM) and its modified forms are presented with their theoretical development. A section is dedicated to evaluating the theoretical aspect of WCM parameterizatio...

This chapter provides full- and dual-pol SAR data to assess multi-target inversion approaches for the semi-empirical Water Cloud Model. In addition to comparative analysis between retrieval results from multi-target techniques, analyzing the correlation between the estimated biophysical parameters and the observed ones against single-target approac...

In this monograph, the utilization of SAR data to retrieve biophysical parameters is described for agricultural crops. Crop biophysical parameters include the foliar area (LAI or PAI) and plant biomass, particularly sensitive to environmental and agronomic practices. Timely information about these biophysical parameters and their spatio-temporal va...

Vegetation indices (VI) are often used as a proxy to plant growth indicators. SAR data are usually processed by several downstream users and are often interpreted by non-radar specialists. This paradigm requires the utility of radar-derived vegetation indices prototypical for Analysis Ready Data (ARD) products. This chapter covers the methodologies...

In this chapter, we describe the methodology for crop biophysical parameter estimation using compact-pol SAR data. Here, we detail the modified form of the semi-empirical Water Cloud Model (MWCM). The scattering power components obtained from the \(iS-\Omega \) decomposition are used to invert the Modified WCM (MWCM). Results are analyzed with the...

Classification of crop types using Earth Observation (EO) data is a challenging task. The challenge increases many folds when we have diverse crops within a resolution cell. In this regard, optical and Synthetic Aperture Radar (SAR) data provide complementary information about the characteristics of a target. Therefore, we propose to leverage the s...

Accurate and high-resolution spatio-temporal information about crop phenology obtained from Synthetic Aperture Radar (SAR) data is an essential component for crop management and yield estimation at a local scale. Crop growth monitoring studies seldom exploit complete polarimetric information contained in dual-pol GRD SAR data. In this study, we pro...

This chapter briefly discusses Synthetic Aperture Radar (SAR) imaging principles and the theory of SAR polarimetry. The descriptions of several polarimetric parameters and their expressions are presented in this chapter. SAR imaging principles are introduced, followed by the description of wave and polarimetric scattering concepts. Several polarime...

This chapter is devoted to several modeling aspects of EM wave interactions with agricultural crops. Comprehensive information on physical and empirical approaches for vegetation modeling with Synthetic Aperture Radar (SAR) polarimetric data are presented in this chapter. Development of various physical models starting from complex wave theory appr...

We require spatio-temporal information about rice for executing and planning diverse management practices. In this regard, data obtained from Synthetic Aperture Radar (SAR) sensors are well suited for tracking morphological developments of rice across its phenology stages. This study proposes different target characterization parameters from polari...

The present study is carried out in a major vegetable growing area of sub-urban Kolkata, India (a 33 year old dumpsite) to assess health risks due to the consumption of metal-laden vegetables. A total of 91 soil samples, 21 water samples, and 10 types of vegetables were analysed for six potentially toxic elements viz., copper (Cu), zinc (Zn), manga...

Accurate and high-resolution spatio-temporal information about crop phenology obtained from Synthetic Aperture Radar (SAR) data is an essential component for crop management and yield estimation at a local scale. Crop growth monitoring studies seldom exploit complete polarimetric information contained in dual-pol GRD SAR data. In this study, we pro...

Accurate and high-resolution spatio-temporal information about crop phenology obtained from Synthetic Aperture Radar (SAR) data is an essential component for crop management and yield estimation at a local scale. Crop growth monitoring studies seldom exploit complete polarimetric information contained in dual-pol GRD SAR data. In this study, we pro...

The demand for processing tools increases with the increasing number of Synthetic Aperture Radar (SAR) satellite missions and datasets. However, to process SAR data, a minimal number of free tools are available (PolSARpro, SNAP) that consolidate all necessary pre-processing steps. Bearing this in mind, there is a need to develop specific tools for...

The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI...

The demand for processing tools increases with the increasing number of Synthetic Aperture Radar (SAR) satellite missions and datasets. However, to process SAR data, a minimal number of free tools are available (PolSARpro, SNAP), which consolidates all necessary pre-processing steps. Bearing this in mind, there is a need to develop specific tools f...

This book presents a timely investigation of radar remote sensing observations for agricultural crop monitoring and advancements of research techniques and their applicability for crop biophysical parameter estimation. It introduces theoretical background of radar scattering from vegetation volume and semi-empirical modelling approaches that are th...

Soil moisture retrieval over the vegetated soil surfaces using Synthetic Aperture Radar (SAR) data is a challenging issue. Presence of vegetation over soil surface makes the interaction of the radar signal with the soil more complex. Several studies used the Water Cloud Model (WCM) to separate vegetation effect on the soil backscatter while estimat...

Information on rice phenological stages from Synthetic Aperture Radar (SAR) images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among...

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 us...

In this study, we propose a new vegetation index (DpRVI) for dual polarimetric synthetic aperture radar (SAR) data. The evaluation of this new index is performed with a particular attention towards the preparation of the NASA-ISRO SAR (NISAR) L-band system science objective. The proposed vegetation index is derived for two dual-pol (HH-HV and VV-VH...

The scattering information from targets is either estimated by fitting suitable scattering models or by optimizing the received wave intensity through the diagonalization of the coherency (or covariance) matrix. In this study, a new roll-invariant scattering-type parameter is introduced, which jointly uses the 3D Barakat degree of polarisation and...

In radar remote sensing applications, soil moisture retrieval over the vegetated surface is a challenging issue due to complex interaction of radar waves with vegetation layer and the underlying soil. Several studies utilized the Water Cloud Model (WCM) directly or by coupling it with surface inversion models, to compensate vegetation effects while...

Food security can be assured with a reasonable crop yield forecast at a national and regional scale. The agencies require advance estimates of production of major crops at a regional scale for taking various policy decisions. Hence, it is necessary to develop operational systems for crop monitoring and yield forecasting. Unlike the traditional annu...

In this study, we propose a sequence-to-sequence neural network architecture to jointly estimate the plant area index (PAI) and wet biomass of canola and soybean. The PAI and wet biomass have considerable importance for crop growth stage mapping and monitoring. RADARSAT-2 quad-pol data along with in situ measurements of canola and soybean obtained...

Crop biophysical parameters such as phenological stages, Leaf area index (LAI) or Plant Area Index (PAI), plant biomass are of particular interest for crop condition monitoring and production forecasts. Estimation of bio and geophysical parameters from Earth Observation (EO) data is essential for developing applications on crop growth monitoring. U...

The present state of the art technologies for flood mapping are typically tested on small geographical regions due to limitation of resources, which hinders the implementation of real-time flood management activities. We proposed a unified framework (GEE4FLOOD) for rapid flood mapping in Google Earth Engine (GEE) cloud platform. With the unexpected...

Crop classification is an integral part of crop monitoring and production forecasting, which in turn helps in making a strategic decision for food security. Crop discrimination with synthetic aperture radar (SAR) data primarily depends on the characterization of crop geometry using radar backscatter response. Differences in phenological development...

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 us...

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4...

Crop characterization using Compact-Pol Synthetic Aperture Radar (CP-SAR) data is of prime interest with the rapid advancements of SAR systems towards operational applications. It is noteworthy that as a good compromise between the dual and quad-polarized SAR systems, the CP-SAR offer advantages in terms of the larger swath and lower data rate. The...

In radar polarimetry, incoherent target decomposition techniques help extract scattering information from polarimetric SAR data. This is achieved either by fitting appropriate scattering models or by optimizing the received wave intensity through the diagonalization of the coherency (or covariance) matrix. As such, the received wave information dep...

Sentinel-1 SAR data preprocessing is essential for several earth observation applications, including land cover classification, change detection, vegetation monitoring, urban growth, natural hazards, etc. The information can be extracted from the 2x2 covariance matrix [C2] of Sentinel-1 dual-pol (VV-VH) acquisitions. To generate the covariance matr...

RADARSAT-2 images are acquired in SLC format. The full-polarimetric images are multi-looked (in the range and azimuth) to generate the coherency matrix T. At first calibration is performed with a complex format output option. Then the 3x3 coherency matrix T is generated, following multi-looking and polarimetric speckle filtering. Finally, the despe...

RADARSAT-2 images are acquired in SLC format. The full-polarimetric images are multi-looked (in the range and azimuth) to generate the coherency matrix T. At first calibration is performed with a complex format output option. Then the 3x3 coherency matrix T is generated, following multi-looking and polarimetric speckle filtering. Finally, the despe...

This work deals with the classification of wheat phenology by regressing the synthetic aperture radar (SAR) backscatter coefficients (VV, VH) to vegetation water content (VWC) and plant area index (PAI) through a representation learning network. The representation network architecture consists of a pair (VV, VH) of two regression layers (VWC, PAI)...

In this study, we propose a vegetation index for compact polarimetric (CP) SAR data (CpRVI) using a geodesic distance between two Kennaugh matrices projected on a unit sphere, as given in Ratha et. al. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and the Kennaugh matrix of an isotropic depolarizer....

In this letter, we propose a novel vegetation index from polarimetric synthetic-aperture radar (PolSAR) data using the generalized volume scattering model. The geodesic distance between two Kennaugh matrices projected on a unit sphere proposed by Ratha et al. is used in this letter. This distance is utilized to compute a similarity measure between...

Collaborative field experiment for SAR and agriculture.

Tuber initiation and tuber bulking stages are critical part of various phenological phases for potato production. Tuber initiation covers the period from the formation of spherical rhizome ends, the flowering and the start of tuber bulking. In general, the tuberization spans from 3 to 5 weeks after emergence and ends with the row closer i.e. canopi...

Spatio-temporal variability of crop growth descriptors is of prime importance for crop risk assessment and yield gap analysis. The incorporation of three bands (viz., B5, B6, B7) in ‘red-edge’ position (i.e., 705nm, 740nm, 783nm) in Sentinel-2 with 10–20m spatial resolution images with five days revisit period have unfolded opportunity for meticulo...

In this paper, a multi-target inversion scheme is adopted for joint estimation of crop biophysical parameters from dual-pol SAR data. The single-output support vector regression (SVR) method is extended to a multi-output support vector regression (MSVR) method to estimate biophysical parameters. The MSVR is implemented for simultaneous retrieval of...

Feature selection techniques intent to select a subset of features that minimizes redundancy and maximizes relevancy for classification problems in machine learning. Standard methods for feature selection in machine learning seldom take into account the domain knowledge associated with the data. Multitemporal crop classification studies with full-p...

Accurate spatio-temporal information about rice growth is an important factor for agronomic management and regional grain yield estimation. In this letter, a unified framework for monitoring and mapping of rice using dense time-series of Sentinel-1 synthetic aperture radar (SAR) images is proposed. A processing chain for such dense time-series Sent...

Grading of rice intents to discriminate broken and whole grain from a sample. Standard techniques for image-based rice grading using advanced statistical methods seldom take into account the domain knowledge associated with the data. In the context of a high product value basmati rice with an image based grading process, one ought to consider the p...