List of SAR systems.
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"2280",leadTitle:null,fullTitle:"Remote Sensing - Applications",title:"Remote Sensing",subtitle:"Applications",reviewType:"peer-reviewed",abstract:"Nowadays it is hard to find areas of human activity and development that have not profited from or contributed to remote sensing. Natural, physical and social activities find in remote sensing a common ground for interaction and development.\nThis book intends to show the reader how remote sensing impacts other areas of science, technology, and human activity, by displaying a selected number of high quality contributions dealing with different remote sensing applications.",isbn:null,printIsbn:"978-953-51-0651-7",pdfIsbn:"978-953-51-5002-2",doi:"10.5772/2670",price:159,priceEur:175,priceUsd:205,slug:"remote-sensing-applications",numberOfPages:530,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"ceea9c29b9b1f05fc128ae2cf564f110",bookSignature:"Boris Escalante-Ramirez",publishedDate:"June 13th 2012",coverURL:"https://cdn.intechopen.com/books/images_new/2280.jpg",numberOfDownloads:70892,numberOfWosCitations:83,numberOfCrossrefCitations:49,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:115,numberOfDimensionsCitationsByBook:1,hasAltmetrics:0,numberOfTotalCitations:247,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 9th 2011",dateEndSecondStepPublish:"June 6th 2011",dateEndThirdStepPublish:"October 11th 2011",dateEndFourthStepPublish:"November 10th 2011",dateEndFifthStepPublish:"March 9th 2012",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"111500",title:"Dr.",name:"Boris",middleName:null,surname:"Escalante",slug:"boris-escalante",fullName:"Boris Escalante",profilePictureURL:"https://mts.intechopen.com/storage/users/111500/images/system/111500.jpg",biography:"He obtained the title of Electrical Mechanical Engineer at the National Autonomous University of Mexico in 1985, the Master's Degree in Electronic Engineering at the Philips International Institute of Technological Studies in Eindhoven, the Netherlands in 1987, and the Doctorate at the Technische Universiteit Eindhoven in 1992.\nFrom 1986 to 1987 he worked as a Design Engineer at ELCOMA Laboratories in Philips, Eindhoven, The Netherlands, developing speech recognition algorithms for DSPs. From 1987 to 1992 he was an Assistant Researcher at the Institute for Perception Research in the same city, where he began his research activities in the area of Visual Perception.\nSince 1992, he has been a Professor at the Faculty of Engineering of the National Autonomous University of Mexico, and he is currently a full-time Professor 'C'. 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Agricultural ecosystems are highly dynamic and usually display apparent seasonal phenological patterns that are strongly dependent on local management practices. The timely and frequent determination of indicators of crop development and productivity, including phenological stage and biophysical parameters such as leaf (or plant) area index and above-ground biomass, is critical for supporting land management decision making in near-real-time. Synthetic aperture radars (SARs) are active systems that provide their own source of energy to illuminate ground targets in the microwave domain. Because the Earth’s atmosphere is largely transparent to microwaves, SAR sensors can be operated day or night and under almost at all weather conditions to acquire high-resolution earth observation data. Given that many regions of the world experience frequent cloud cover, SAR has become an essential remote sensing tool for the operational monitoring of agricultural production systems around the world.
Radar backscattering is highly sensitive to the structural (roughness, orientation, and spatial distribution of scattering components) and the dielectric properties of targets. The backscattering intensity is also strongly related to the transmitted microwave frequency, incident angle and the transmitted and received polarizations. Several microwave scattering models have been developed to relate backscattering to target properties and radar acquisition parameters. Examples of theoretical microwave models include the Integral Equation Model (IEM) and the MIMICS (Michigan Microwave Canopy Scattering) model [1, 2, 3]. Semi-empirical models maintain some theoretical basis but use empirical data to simplify the mathematical relationships between scattering and target properties as well as sensor parameters. Examples of this approach to modeling include the Water Cloud Model (WCM) used to characterize SAR response from vegetation and soil [4], as well as the Oh [5] and Dubois [6] models that relate soil properties to radar backscattering. Two simplified scenarios, based on which radar backscattering models have been developed, are shown in Figure 1. The first simplifies vegetation canopy as a layer of scattering elements uniformly distributed above the soil surface, and radar backscattering from the soil is modeled by a two-way attenuation through the canopy (left). The second takes into consideration of canopy geometric structure, and models three backscattering components, surface scattering from plant or soil, double-bounce scattering from plant and soil (plant-soil and soil-plant), and multiple scattering by the plant-soil mix (volume scattering) (right).
Simplified scenarios for modeling radar backscattering from vegetation canopy. Left: vegetation as a water cloud, and backscattering is modeled by a two-way attenuation through a canopy with a path length H/cos(ϴ). Right: vegetation as a 3-D scattering medium inducing three scattering mechanisms, surface scattering, double-bounce, and volume scattering.
Radar backscattering models have been used for estimation of crop parameters such as Leaf Area Index (LAI), canopy water content, and biomass [7, 8, 9, 10], and soil parameters such as soil moisture and surface roughness [11, 12], using SAR data acquired at different incident angles, frequencies, and/or polarizations. Fully polarimetric (or quad-pol) SAR systems measure the complete complex scattering from a target. Microwaves are transmitted and received in two orthogonal polarizations and the phase is preserved during processing. With the complete scattering matrix, quad-pol data can be analyzed to provide polarimetric features and the signal can be decomposed using coherent (e.g., Pauli and Cameron) or incoherent (e.g., Freeman-Durden and Cloude-Pottier) techniques [13, 14]. Variables derived through polarimetric decomposition can be used both in classification [15, 16] and parameter estimation, such as crop phenology or soil moisture [17, 18]. Time-series SAR data have also been used for the detection of crop seeding and harvest using change detection approaches [19, 20, 21].
A few satellite SAR constellations have been launched during the past few years, and more small satellite SAR constellations will be continuously developed in near future. The increasing availability of a large amount of SAR data, in companion with big data analytics, provides an unprecedented opportunity for effective and operational monitoring of agricultural ecosystems. However, the effective use of SAR data requires a full understanding of how the information it provides relates to agricultural targets. The objectives of this article are to summarize the path of SAR system development and to review the information sources of SAR data, the applications in agricultural ecosystem monitoring with a focus on crop classification, crop parameter estimation, and change detection using dense time-series data.
Early studies in radar remote sensing applications in agriculture relied extensively on ground-based microwave scatterometers [22, 23, 24, 25, 26, 27]. The portability of scatterometers allows them to be rapidly deployed to agricultural test sites to collect temporally dense data at different frequencies, polarizations, and incidence angles. Experiments using scatterometers have been critical for developing an understanding of how microwaves interact with soils and crops, and the development and testing of microwave models [28]. However, despite the important contributions of such research, scatterometer data are geographically limited to smaller test plots.
The deployment of SAR on aircraft and satellite platforms provides data at field and sub-field scales over much broader geographic extents. Airborne SAR campaigns, such as the NASA/JPL AIRSAR and UAVSAR, Canadian Convair-580 C/X SAR, and German DLR E-SAR/F-SAR, have served as theoretical testbeds to develop applications pre-launch of space-based SARs. Space-based SAR observations from the Shuttle Imaging Radar (SIR) missions, in particular the SIR-C/X SAR missions in 1994, provided imaging opportunities from a space platform and delivered data in different frequencies and polarizations.
Systematic acquisitions from SAR satellites began with the launch of ESA’s ERS-1 satellite in 1991. Several other space agencies followed, launching SAR satellites operating at different frequencies, and with different capacities to select imaging modes at a variety of spatial resolutions, swath widths, polarization, and incident angles (Table 1). RADARSAT-2, for example, supports the acquisition of data at single, dual, or quad polarization, at different spatial resolutions, and various incident angles. However, while the capability of each of these space-based systems was extensive, they demanded large and heavy payloads. For example, the mass of the Canadian C-band RADARSAT-1 and -2 satellites, and the ESA Sentinel-1A and 1B satellites each exceeded two tons at launch. More recently, technological developments that include standard electronic components and semiconductor materials (GaN) [29, 30] make it possible to produce compact SAR sensors in a shorter amount of time, and at a relatively low cost. These advancements have led to commercial investments in microsatellite constellations of space-borne SARs. For instance, PredaSAR plans to launch a constellation of 48 satellites equipped with a large swath C-band or a high-power X-band sensor. The Japanese QPS Institute is developing an X-band constellation that will eventually comprise 36 micro-satellites. These SAR sensors are typically small (<500 kg), but are more limited in the diversity of imaging modes, typically operating in only single or dual polarizations.
Platform | Country/organization | SAR system | Frequency | Mode | Active years | Note |
---|---|---|---|---|---|---|
Airborne | Canada | Convair-580 | X, C | Polarimetric | 1986–present | |
USA/NASA | AirSAR | C, L, P | Polarimetric | 1988–2004 | ||
USA/NASA | UAVSAR | Ka, L, P | Polarimetric | 2007–present | ||
German/DLR | E-SAR/F-SAR | X, C, S, L, P | Polarimetric | 1988–present | ||
USA.JPL | SIR-C/X | C, X | Polarimetric | 1994 | ||
Large satellites | ESA | ERS-1/2 | C | VV | 1991–2011 | |
ESA | ASAR | C | Various | 2002–2012 | ||
Japan/NASDA | JERS-1/2 | L | HH | 1992–1998 | ||
Canada | RADARSAT-1 | C | HH | 1995–2013 | ||
Canada | RADARSAT-2 | C | Various | 2007–present | ||
Canada | RCM | C | Various | 2019–present | 3 satellites | |
German | TerraSAR-X | X | Single or dual | 2007 | ||
Argentina | SAOCOM | L | Polarimetric | 2018–present | 2 satellites | |
ESA | Sentinel-1 | X | Single or dual | 2014–present | 4 | |
Italy | COSMO-SkyMed | X | Various | 2007–2010 | COSMO: 4 | |
2019–present | CSG: 2 | |||||
Japan/JAXA | ALOS-PALSAR | L | Various | 2006–present | 4 | |
USA/India | NISAR | L, S | Polarimetric | 2023–present | ||
Micro-satellites | Finland | ICEYE-X | X | VV | 2018–present | 18 |
Japan/Synspective | StriX | X | VV | 2020–present | 30 | |
Japan/QPS | QPS-SAR | X | Circular | 2019–present | 36 | |
USA | Capella | X | HH | 2018–present | 36 | |
USA | PredarSAR | C, X | — | 48 | ||
USA | Umbra-SAR | X | — | 2021–present | 12 |
List of SAR systems.
For reference, a non-exclusive list of SAR systems that are of interest to agricultural applications is given in Table 1. Over the past 15 years or so, the general trend of governments and space agencies has been to focus on larger wide-swath SARs whose data are free and open (or partially open) to the public. In comparison, the commercial SAR satellite ecosystems have focused on constellations of smaller satellites providing, for a fee, access to data at finer spatial and temporal resolutions. Data from such constellations may provide the near-continuous monitoring of land surfaces.
Crop growth dynamics are characterized by structural or moisture changes, which can be captured by time-series SAR data for the detection and mapping of phenological development stages [17, 31, 32, 33, 34]. Because different crops have different growth dynamics, time-series SAR is also useful for crop classification [35, 36, 37, 38]. Time-series optical and SAR data have been used to derive phenological metrics for phenology-based crop classification [35, 39, 40]. Using a dense stack of Sentinel-1 SAR data, Bargiel [35] proposed a crop classification scheme using phenological sequence patterns (PSP), which outperformed the Random Forest and the Maximum Likelihood classifiers for cereal crops. Phenology-based classifiers can be more generic than conventional classifiers, and may be more resilient to differences in management practices and growth conditions because they take crop-specific growth dynamics into consideration [35, 39].
Important target information is also revealed by different SAR polarizations. Polarizations that interact more strongly with plant volume will likely be more useful for crop parameter estimation and for discriminating different crops. For example, VV performs well in characterizing vertical vegetation structure, and VH is sensitive to multiple scattering events in the canopy [41], and thus their use in combination provides better classification capabilities in most cases. HH polarization is found to be inferior in many cases for these specific applications [28, 42]; however, it is more sensitive to the structural variation of rice, and thus useful for mapping this crop [43].
SAR backscatter intensity and other polarimetric parameters can be derived from fully polarimetric SAR using coherent or incoherent target decomposition methods, as summarized in Cloude and Pottier [13], Touzi et al. [14], and Lee and Pottier [44]. Simple or canonical targets—such as dipoles, diplanes, or cylinders—show higher coherence than distributed targets—such as rough soil surfaces or vegetation—where random scattering occurs. Criteria to determine coherent and incoherent targets are provided by Touzi and Charbonneau [45], using the maximum symmetric component derived from the Cameron decomposition. Coherent target decomposition is applied to express the complex scattering matrix as linear combinations of a set of simpler and independent bases, each representing certain physical scattering mechanisms. Examples include the Pauli decomposition, the Krogager decomposition, and the Cameron decomposition. Incoherent decomposition methods are used when a pixel contains distributed targets, and express the second-order statistics of coherency or covariance matrices with a combination of simpler components. Examples include the Freeman-Durden, Huynen, and Cloude-Pottier decompositions. For satellite SAR sensors, the power and pulse repetition required to operate a fully polarimetric system limits swath widths, and thus hybrid architectures, such as compact polarimetric systems, have been proposed [46]. Compact polarimetry offers a partial solution by transmitting a single circularly polarized wave and receiving two orthogonal waves coherently [46, 47, 48]. Methods for compact polarimetric data decomposition have also been developed and summarized in Charbonneau et al. [47], Cloude et al. [49], and Ponnurangam and Rao [50].
The distribution and orientation of plant components and their sizes relative to SAR wavelengths vary over the growing season and from crop to crop. Microwave scattering occurs when the SAR wavelength is similar to or smaller than the size of canopy components. SAR frequency also influences the penetration depth of microwave radiation into crop canopies. Lower frequencies (e.g., L-band) penetrate deeper into the canopy than higher frequencies (e.g., C-/X-band). The optimal depth of penetration, and the matching of wavelength to the size of plant components, vary from crop to crop and throughout the crop development cycle. As a result, the selection of a single best frequency for SAR is challenging. Higher frequency SAR is better for classifying low biomass canopies, while lower frequency SAR is more useful for identifying high biomass vegetation [42]. The integration of data at different frequencies brings enriched information for crop classification and has thus been widely recommended [42, 51, 52, 53, 54, 55, 56, 57]. However, implementing a multi-frequency approach is challenging due to limitations in the availability of data from sensors at different frequencies, especially for operational applications. Temporal signatures created by different frequencies have also been exploited for crop area mapping using dense time series of SAR data. Kraatz et al. [58] used the temporal coefficient of variation of the VH polarization from both Sentinel-1 C-Band and PALSAR L-band data, and an optimal threshold, to discriminate crops from non-crops in western Canada. A higher mapping accuracy was achieved using C-band data (84%) than L-band data (74%), though performance varied among different cover types. Here, L-band performed poorly for soybean and some non-crop types (urban, grassland, and pasture), while C-band was relatively poor for corn, urban, and pasture. A time series of data from both frequencies would likely have improved these accuracies.
The variation of radar backscattering with incident angle is another important consideration for mapping agricultural landscapes with SAR. This is reflected in vegetation backscattering models, such as the MIMICS model [3] and the Karam-Fung model [59]. These models, developed for forest and adapted for crops [43, 60], require incident angle as an explicit parameter. For example, Prevot et al. [61] showed that using a simple parametrization of the angular effect of soil roughness in the Water Cloud Model [4], the vegetation water content can be estimated satisfactorily from C- and X-band SAR data acquired at two different incident angles, for example, 20° and 40°. Various studies have demonstrated the impacts of incident angle on land cover classification. Poirier et al. [62] studied the impacts of incident angle on classification performance by acquiring C-band data near-coincident at two different angles (30° and 53°) with the Convair-580 airborne system. Results showed that SAR data collected at the larger incident angle interacted more with the upper canopy, delivering an improved classification. Kothapalli Venkata et al. [63] conducted a study to assess the separation of corn from other land cover types (wheat, fallow, water, and urban) using multi-incident angles (28°, 42°, and 52°) C-band hybrid polarimetric data acquired over 3 days by RISAT. The study showed that corn can be discriminated from other crop types using volume and double-bounce scattering at both 28° and 42°, and using odd bounce and volume scattering combinations at 52°. Xu et al. [64] acquired RADARSAT-2 data at three different incident angles and showed that multi-angle SAR improved the classification accuracy of some land cover types (though it should be noted that the images were acquired at different times during 1 month, confounding the effects of the time of acquisition and change in incident angle). In summary, SAR data acquired at different incident angles contribute to target information extraction.
The classification of land covers and crop types is one of the earliest applications of SAR in agriculture. In the broadest sense, crop classification from SAR involves the implementation of automated techniques to sort image data into one of a finite number of crop classes based on their backscatter characteristics. Crop classification is an important agricultural application because it can be used to derive the area seeded to individual crops, and to predict or forecast food production if crop growth conditions are incorporated. Obtaining this information requires detailed, routine, and frequent mapping of croplands with sufficiently high accuracy. SAR has been shown to be particularly useful for the operational monitoring of crop dynamics in agricultural ecosystems.
A broad array of approaches for classifying satellite images have been developed in the past few decades. Until recently, the Maximum Likelihood (ML) classifier was the most widely used method for the supervised classification of remote sensing data [65, 66, 67], mostly due to its simplicity in implementation. While this approach has been widely applied in different studies for satellite image classification of agricultural regions [68, 69, 70, 71], limitations associated with the ML approach mean that alternative supervised classification techniques are more preferable. Of these new methods, artificial neural networks (ANN) [72, 73, 74, 75], support vector machines (SVM) [76, 77, 78, 79], Decision Trees (DT) and ensembles of classification trees such as Random Forest (RF) [80, 81, 82, 83, 84] have all shown great promise.
A detailed comparison of classification methods is beyond the scope of this article, and indeed, would only provide limited insight into the best classification approaches for SAR-based crop type mapping. This is because the success of crop classification procedures is as much—if not more—dependent on the quality of the ground (in situ) observations used for training and validating the classification, than the actual algorithm chosen to do the classification. Instead, we direct readers to a comprehensive synthesis of this body of work provided by Khatami et al. [85], who conducted a statistical meta-analysis of research on land-cover classification. This study was conducted to provide coherent guidance on the relative performance of different classification processes for generating land cover products and showed that the highest mapping accuracies were provided by implementations of SVMs, ANNs, and RF. While it is important to note that these results are not necessarily predictive of the relative performance of any specific classifier in any specific application (due to the unique features of that application), they do provide an insight into how each classification algorithm may perform under various circumstances [85].
In addition to the general classifiers presented above, two other classification schemes have been developed specifically for SAR data. These are classifications based on the Cloude-Pottier decomposition and classification based on the complex Wishart distribution. The Cloude-Pottier decomposition [15] produces three parameters—entropy (H), anisotropy (A), and the alpha angle (α). Entropy is a metric of the degree of randomness of scattering from within the resolution cell, anisotropy is an indicator of the presence of secondary or tertiary scattering mechanisms, and the alpha angle represents the dominant scattering mechanism. A classification scheme was developed to divide the H-α space into eight possible scattering zones, from which land cover classifications can be performed. The advantage of this classification scheme is the improved understanding of SAR signal scattering mechanisms where there is less
SAR data are typically multi-look processed for speckle noise reduction. The covariance matrix of the multi-look processed SAR data follows a multivariate complex Wishart distribution. With this condition, Lee et al. [91] proposed a classification scheme using Bayes maximum likelihood or minimum distance (MD) classifier. In practice, each class is characterized by an elementary covariance matrix derived from training samples, and each pixel is classified according to the Bayes likelihood with the elementary covariance matrices under a given
The coordination of Earth Observations (EO) data for agricultural monitoring necessitates the articulation of spatially explicit EO data requirements, including where [92], when [93], how frequently [94], over which spectral range, and at what spatial resolution these data are needed [95]. Because cropping systems are often diverse and complex, and the types of crops grown and the timing of their growth vary from region to region, the best choice of sensors to be used, the optimal number of images required, and the timing of image acquisitions are usually geographically specific. Where SAR has been used in operational national-scale crop mapping programs, it has usually been integrated with optical remote sensing data. Both optical and SAR provide unique and valuable information relating to plant growth and type, primarily due to their different wavelengths. Optical imagery acquired in the near-infrared and shortwave-infrared is sensitive to canopy biochemistry such as composition and concentration of pigments, water content, biomass, and leaf internal structure, while SAR imagery is sensitive to plant structure. SAR observations are also critical for filling gaps in the optical image record brought about by the presence of clouds during key growth stages.
The integration of optical and SAR data can be as simple as combining data from different sources into raster stacks for classification, sometimes applying mathematical transformations to fuse and enhance features or reduce data dimensionality [96, 97, 98, 99]. In some cases, SAR data are not used directly in the classification process but are first transformed into higher-level data products. This has included the derivation of phenological metrics from SAR time series (e.g., Torbick et al. [100] and the use of SAR-based texture [101].
One of the most well-known applications of SAR in national-scale crop mapping comes from Canada. Since 2010, Agriculture and Agri-Food Canada—Canada’s Ministry of Agriculture—has integrated C-band SAR (RADARSAT, Sentinel-1) with optical data streams (Landsat-5, -7 and -8, SPOT, DMC, RapidEye, and Resourcesat-1) to generate its Annual Space-Based Crop Inventory for Canada [102]. Figure 2 shows the mapping result for 2020, which covers the agricultural land and includes all crops and a few other land cover types.
National scale crop type mapping in Canada, 2020. The map is produced by Agriculture, Geomatics and Earth Observation Division, Science & Technology Branch, Agriculture, and Agri-Food Canada.
Results from research and operations suggest that optical and SAR satellite data are both required to best characterize the key crop growing (phenological) stages required for high-accuracy crop mapping at a national scale [39, 56, 80, 103, 104, 105, 106]. The addition of dual-pol SAR has been shown to increase accuracies over the use of optical data alone by as much as 16% [42, 104]. Nonetheless, the decision to use optical and/or SAR is usually determined by the trade-off among a number of factors, including—(a) the heterogeneous and dynamic intrinsic nature of the agro-ecosystem being studied; (b) the geographical extent to be mapped; (c) the minimum mapping unit required to resolve individual fields and other meaningful ecological units (e.g., wetlands, woodlots, etc.); (d) differences in crop cycles; (e) differences in cropping practices and calendars within the same class; (f) the spectral similarity with other land cover classes; (g) the engineering constraints of the remote sensing systems (i.e., swath size; spatial, temporal, spectral and radiometric resolutions; cloud coverage for optical systems), and (h) data availability (i.e., open, fee-based).
Microwave scattering, represented by both intensity and phase characteristics, changes with variations in the structure of crop canopies and canopy water content. Canopy structure and water content vary as crops develop and are thus indicative of crop development and productivity. Figure 3 shows seasonal variation of radar backscattering intensity of annual crops over a growing season in an agricultural region in northern Ontario, Canada, using dual-polarization C-band SAR data acquired by Sentinel-1 in 2019. Both VV and VH polarizations of annual crops show obvious seasonal variation patterns characteristic to crop development cycle, whereas that of forest targets (the two dotted lines) remain at a relatively stable and higher level throughout the season. This clearly shows a positive correlation between radar backscattering intensity and crop live biomass, based on which different crop parameters can be estimated from SAR data.
Seasonal profiles of radar backscattering intensity for annual crops in northern Ontario, Canada, using C-band SAR data acquired by Sentinel-1 in 2019. The two dotted lines represent two forest patches.
The potential of SAR for supporting crop growth monitoring through the quantitative estimation of crop parameters—such as Leaf (or Plant) Area Index (LAI or PAI), plant height and density, fresh and dry biomass, and plant water content—depends on SAR sensor characteristics (frequency, polarization, and incident angle), crop type, and growth stage [107, 108].
The characteristics of a SAR determine the depth to which a pulse of microwave energy can penetrate a plant canopy and, in turn, influence the ability to determine canopy conditions from SAR observations. Because of this, the optimal choice of SAR frequency will vary over time, depending on canopy type and growth stage, and thus the use of multiple SAR frequencies for crop mapping, where available, is recommended. SAR scattering is also polarization-dependent [44, 109, 110]. Overall, the best polarization for crop characterization has been the linear cross polarization (either HV or VH) [110, 111]. This is mainly due to re-polarization that occurs during multiple scattering within targets with complex structures, such as crop canopies consisting of randomly oriented and distributed stems and leaves [112]. Using RADARSAT-2 SAR data, Liao et al. [113] studied the sensitivity of C-band SAR polarimetric parameters for the estimation of crop height and fractional vegetation cover. They found that cross polarization or combinations of dual polarizations (HH-VV or HV-VV) were strongly correlated with crop height and fractional cover of broadleaf crops, such as corn, with degraded performance toward the later growing stages. For narrow-leaf crops, such as wheat, the sensitivity of SAR parameters to crop height and cover fraction was relatively low or inconsistent. Wali et al. [114] assessed Sentinel-1 C-band SAR VV and VH backscatter for estimating biophysical parameters of rice, including plant height, green vegetation cover, LAI, and total dry biomass. The results of this study showed that both VH and VV were strongly and linearly correlated with biophysical parameters until backscatter saturated during the mid-reproductive stage (60 days after transplanting), and the beginning of the reproductive stage for VV (though VH showed stronger correlations in most cases). Chauhan et al. [115] were able to obtain better estimates of vegetation parameters by accounting for soil backscatter effects. Other studies include those by Xie et al. [110], who demonstrated the capability of RADARSAT-2 polarimetric SAR variables for crop height estimation, and Hosseini et al. [116], who used WCM and SVM to estimate LAI using RADARSAT-2 SAR intensity collected over multiple international sites (Argentina, Canada, Germany, India, Poland, Ukraine, and the U.S).
Polarimetric SAR allows the complete scattering characteristics of crop canopies to be determined, and parameters derived from these complex data can improve estimates of crop conditions. Many recent examples of this come from studies over agricultural regions in Canada. Using C-band RADARSAT-2 polarimetric data, Wiseman et al. [117] extracted and evaluated 21 polarimetric parameters to estimate dry biomass for canola, corn, soybean, and spring wheat crops. This study found that most SAR parameters were significantly correlated with dry biomass accumulation, while several proved to be good indicators of changes in crop structure and phenology. For instance, four SAR responses (linear HV and circular LR backscatter, volume scattering, and pedestal height) increased during canola ripening. However, as canola flowered, the importance of these parameters declined. Homayouni et al. [118] used the ratio of volume-to-surface scattering derived from C-band RADARSAT-2 polarimetric data to monitor the growth of canola, corn, spring wheat, and soybeans fields in western Canada. They found that this ratio was strongly correlated with optical vegetation indices (e.g., the normalized difference vegetation index NDVI, and the Soil Adjusted Vegetation Index SAVI). Using time-series RADARSAT-2 polarimetric data, and RapidEye optical imagery, Jiao et al. [119] applied a semi-empirical Canopy Structure Dynamics Model, Growing Degree Days, and SAR parameters calibrated to optical NDVI to derive daily estimates of canola crop condition over an entire growing season. Correlations (R values) of 0.63–0.84 were reported when SAR parameters were related to optical NDVI, with results varying among three growing seasons.
A growing literature focusing on SAR-based vegetation indices demonstrates the potential of such techniques. Kim and van Zyl [120] proposed a radar vegetation index (RVI) based on the SAR backscatter intensities at VV, HH, and VH polarizations, which has since been simplified to accommodate data obtained from dual-polarized systems [121, 122]. Using Sentinel-1 observations, Periasamy [123] proposed a Dual Polarization SAR Vegetation index (DPSVI) by exploiting the data distribution of VV and VH backscatter coefficients in two-dimensional space. Such radar indices show strong potential for the better discrimination of bare soil from vegetation, as well as for crop structural parameter estimation. Other SAR-based vegetation indices include the SAR simple difference (SSD) index, applied to estimate rice yield in China, and based on the difference in Sentinel-1 VH backscatter between the end of the rice tillering stage and the end of grain filling stage [124]. Results of the study showed a strong exponential relationship between the SSDVH and rice yield.
Other applications of SAR for crop parameter estimation and growth condition monitoring include its use in radiative models. Attema and Ulaby [4] developed a Water Cloud Model (WCM) to simulate SAR backscatter from the crop-soil system as an incoherent sum of contributions from plants and background soil after a two-way attenuation by canopies. Through time, the model has been modified to reflect different approaches to the interaction and parameterization of soil and vegetation contributions. For example, various studies have used LAI, canopy water content, and biomass to characterize the vegetation component in the WCM [7, 8, 10, 116].
In the context of this article, the objective of change detection using remote sensing data is to identify and characterize changes in agricultural land cover and/or use (e.g., conversions from one crop class to another) or changes in condition within a land cover and/or use (e.g., modifications within a crop class) over a specified period of time. These changes can be described as—(a) binary change/non-change (e.g., harvest); (b) from-to trajectories (e.g., forest to cropland conversion); (c) causes of change (e.g., fire, flooding); and (d) continuous variable change (e.g., reduced productivity within a class due to insect infestation or drought) [125]. Understanding the types of change sought is critical for selecting suitable remote sensing data sources, determining processing methods, and developing and implementing robust and effective change detection algorithms.
For agricultural resource management, it is important to detect intra-annual landscape changes, such as changes in crop phenology [17, 20, 33], field operations [19, 21], and field conditions [126, 127]. This type of monitoring requires dense remote sensing time series that usually cannot be fulfilled using optical data alone due to the presence of the cloud. As a result, spatially and temporally comprehensive and consistent coverages from operational wide-swath SAR satellites will continue to be a critical source of free and open SAR data for national-scale change detection. As new SAR missions are launched and existing missions expand, multi-frequency SAR is expected to play an increasingly important role in monitoring and measuring change on agricultural landscapes. The application of SAR for within-season change detection will require well-calibrated data from multiple satellites within a constellation, if satellites from different constellations are used together.
Change detection using SAR backscatter—as opposed to its indirect detection from SAR-derived value-added products such as crop type maps or modeled biophysical parameters—belong to one of two broad types. These are Incoherent Change Detection (ICD) and Coherent Change Detection (CCD) [128]. ICD methods identify changes in mean backscatter intensity without considering SAR phase information. Here, the difference can be calculated as a ratio, a log ratio (LR), a mean ratio (MR), the normalized compression distance [129], or using pointwise approaches based on graph theory [130], convolutional neural networks [131], or the generalized likelihood ratio test (GLRT) [132]. In comparison, CCD methods identify change based on the complex conjugate correlation coefficient of the two images, thus taking into consideration of both backscatter intensity and phase. If dense stacks of time-series SAR images are available, changes can be inferred from these methods. For example, Shang et al. [21] used CCD to detect crop seeding and harvest using time-series Sentinel-1 SAR. The study integrated time-series coherence and VH backscatter intensity to detect changes at the beginning and at the end of a growing season, with the assumption that coherence is comparatively higher before crops emerge and after crop harvest. Figure 4 shows the example for mapping crop seeding dates, and details of the approach are given in Shang et al. [21].
Estimation of crop seeding dates through change detection using C-band SAR data acquired by Sentinel-1. Left: detection of annual crop fields using a simple threshold of seasonal variation amplitude of VH; right: mapping of crop seeding dates.
Timely and continuous observations from satellite systems are critical for providing the data and information required by decision-makers to manage agricultural lands. High-quality satellite observations can be obtained from SAR sensors; however, they must be collected at a spatial resolution that allows sufficient detail to be resolved, and at times, during the growing season, that coincides with the key growth stages of crops being assessed. The most accurate detailed national crop mapping generally occurs when moderate-resolution spectrally rich time series are acquired that contain no gaps.
Because of its near all-weather capacity, SAR technology has been shown to be particularly useful in agricultural monitoring, especially in regions with frequent cloud cover. The agricultural applications summarized in this article cover examples of information extraction for crops. Despite the gains made over the past 15 years in methods for crop monitoring from SAR, some challenges remain. A major challenge is the separation of backscattering signals from soils and crops, where it is difficult to differentiate the geometrical and dielectric properties of these two targets. While theoretical and semi-empirical models have been developed to simulate backscattering signals, model inversion for solving surface parameters with high accuracy remains a challenge. Much attention has been focused on the integration of SAR and optical remote sensing for improving target parameter retrieval accuracies. With temporally dense imaging capabilities of current and future satellite SAR systems, changes in agricultural land should be more accurately detected. Methods for change detection based on SAR and optical time series show large future potential.
Future opportunities for the use of SAR in agricultural monitoring will come from the adoption of new and improved satellite missions that, in combination or isolation, will allow a better characterization of crop-specific growth cycles at the field level. Of particular interest is the integration of SAR imagery acquired at multiple frequencies, especially if these multi-frequency data sets are collected in wide swaths, with consistent coverages, and under open data policies. However, this will not be without a challenge. The ability of national mapping agencies to incorporate this information in a timely and efficient manner will require significant investment in information technology infrastructure to facilitate the processing of significantly greater volumes of data.
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The focus has passed from the administrative management tasks to becoming a strategic partner of the overall organization strategy, largely with the strong support of information technologies’ evolution in this field of knowledge area. The extended use of information systems has a deep effect in the way HRM is managed nowadays. It boosted a major transformation of human resources (HR) processes and practices within organizations, namely on how they collect, store, use, and share information. Several HRM processes have become more efficient and the impact of this service level improvement allowed a greater involvement of HR in the business strategy. This new role in business strategy adds significant changes to HR function and to its professionals. Along this chapter, we discuss the effects of information systems in HRM, considering the existing literature on the topic, and describe the benefits and possible limitations of using them. 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Management Functions and Information System Components as an integration model are (1) to explain Management Functions, Information System Components, Goals and Benefit related to Information System and (2) to explain integration process of Management Functions with Information System Component to get goals and benefits as an integrated model. Research method using expert method has done an integration of management function, which includes the cycle of P, O, A, C, E and I, to run management process, must be step by step, and as a cycle. Information components include S, H, F, B, and T and must have minimum requirement. Management of Information System needs goals and benefits that can be calculated clearly and specifically. To get goals and benefits in excellence performance are needed the integrated process to coordinate management functions and information system components, as an Integrated Model with an example in applications of software in Nosocomial Infection Control for Hospital, as the figure below.",book:{id:"6259",slug:"management-of-information-systems",title:"Management of Information Systems",fullTitle:"Management of Information Systems"},signatures:"Boy Subirosa Sabarguna",authors:[{id:"200387",title:"Ph.D.",name:"Boy Subirosa",middleName:null,surname:"Sabarguna",slug:"boy-subirosa-sabarguna",fullName:"Boy Subirosa Sabarguna"}]},{id:"57870",title:"Information and Information Technologies in Conflict Management",slug:"information-and-information-technologies-in-conflict-management",totalDownloads:1259,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This paper analyzes information and modern information technologies as applied in different organizational environment and considers the content and peculiarities of conflict management process based on implementation of communicative scenarios. Currently, the need for escalated organizational transformations has become imminent, taking into account the intensifying development of the differentiated information society, which requires properly interactive and transparent policy-making. Correct understanding of information and effective implementation of information technologies is a rational attempt to harmonize the modern organizational environment reducing the level of conflict and improving efficiency indexes.",book:{id:"6259",slug:"management-of-information-systems",title:"Management of Information Systems",fullTitle:"Management of Information Systems"},signatures:"Andrei Aleinikov, Daria Maltseva, Alexander Kurochkin and Tatiana\nKoulakova",authors:[{id:"212005",title:"Dr.",name:"Andrey",middleName:null,surname:"Aleynikov",slug:"andrey-aleynikov",fullName:"Andrey Aleynikov"},{id:"219746",title:"Dr.",name:"Tatiana",middleName:null,surname:"Koulakova",slug:"tatiana-koulakova",fullName:"Tatiana Koulakova"},{id:"219747",title:"Dr.",name:"Daria",middleName:null,surname:"Maltseva",slug:"daria-maltseva",fullName:"Daria Maltseva"},{id:"219748",title:"Dr.",name:"Alexander",middleName:null,surname:"Kurochkin",slug:"alexander-kurochkin",fullName:"Alexander Kurochkin"}]},{id:"59493",title:"Some Methods for Evaluating Performance of Management Information System",slug:"some-methods-for-evaluating-performance-of-management-information-system",totalDownloads:1356,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Recently, several kinds of information systems are developed for purposes and needs of business and play an important role in business organizations and management operations. 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Its performance relates to many other information systems, for instance, DSS or decision support system, SIS or strategic information system, etc. Methods of testing statistical hypotheses concerning the performance of MIS are absolutely essential to support management activities and decision-making.",book:{id:"6259",slug:"management-of-information-systems",title:"Management of Information Systems",fullTitle:"Management of Information Systems"},signatures:"Khu Phi Nguyen and Hong Tuyet Tu",authors:[{id:"150274",title:"Associate Prof.",name:"Khu",middleName:"Phi",surname:"Nguyen",slug:"khu-nguyen",fullName:"Khu Nguyen"}]},{id:"63078",title:"Decision Support Systems",slug:"decision-support-systems",totalDownloads:2378,totalCrossrefCites:8,totalDimensionsCites:14,abstract:"The current decision-making problems is more complex than it was in the past, prompting the need for decision support. 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He previously worked as a post-doctoral fellow at the Ben-Gurion University of Negev, Israel; University of the Free State, South Africa; and Central University of Technology Bloemfontein, South Africa. He obtained his Ph.D. in Organic Chemistry from Nagaoka University of Technology, Japan. He has published more than seventy-four journal articles and attended several national and international conferences as speaker and chair. Dr. Kendrekar has received many international awards. He has several funded projects, namely, anti-malaria drug development, MRSA, and SARS-CoV-2 activity of curcumin and its formulations. He has filed four patents in collaboration with the University of Central Lancashire and Mayo Clinic Infectious Diseases. His present research includes organic synthesis, drug discovery and development, biochemistry, nanoscience, and nanotechnology.",institutionString:"Visiting Scientist at Lipid Nanostructures Laboratory, Centre for Smart Materials, School of Natural Sciences, University of Central Lancashire",institution:null},{id:"428125",title:"Dr.",name:"Vinayak",middleName:null,surname:"Adimule",slug:"vinayak-adimule",fullName:"Vinayak Adimule",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/428125/images/system/428125.jpg",biography:"Dr. Vinayak Adimule, MSc, Ph.D., is a professor and dean of R&D, Angadi Institute of Technology and Management, India. He has 15 years of research experience as a senior research scientist and associate research scientist in R&D organizations. He has published more than fifty research articles as well as several book chapters. He has two Indian patents and two international patents to his credit. Dr. Adimule has attended, chaired, and presented papers at national and international conferences. He is a guest editor for Topics in Catalysis and other journals. He is also an editorial board member, life member, and associate member for many international societies and research institutions. His research interests include nanoelectronics, material chemistry, artificial intelligence, sensors and actuators, bio-nanomaterials, and medicinal chemistry.",institutionString:"Angadi Institute of Technology and Management",institution:null},{id:"284317",title:"Prof.",name:"Kantharaju",middleName:null,surname:"Kamanna",slug:"kantharaju-kamanna",fullName:"Kantharaju Kamanna",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284317/images/21050_n.jpg",biography:"Prof. K. Kantharaju has received Bachelor of science (PCM), master of science (Organic Chemistry) and Doctor of Philosophy in Chemistry from Bangalore University. He worked as a Executive Research & Development @ Cadila Pharmaceuticals Ltd, Ahmedabad. He received DBT-postdoc fellow @ Molecular Biophysics Unit, Indian Institute of Science, Bangalore under the supervision of Prof. P. Balaram, later he moved to NIH-postdoc researcher at Drexel University College of Medicine, Philadelphia, USA, after his return from postdoc joined NITK-Surthakal as a Adhoc faculty at department of chemistry. Since from August 2013 working as a Associate Professor, and in 2016 promoted to Profeesor in the School of Basic Sciences: Department of Chemistry and having 20 years of teaching and research experiences.",institutionString:null,institution:{name:"Rani Channamma University, Belagavi",country:{name:"India"}}},{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/158492/images/system/158492.jpeg",biography:"Prof. Dr. Yusuf Tutar conducts his research at the Hamidiye Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Turkey. He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. Prof. Emeje was a national chairman of academic pharmacists in Nigeria and the 2021 winner of the May & Baker Nigeria Plc–sponsored prize for professional service in research and innovation.",institutionString:"National Institute for Pharmaceutical Research and Development",institution:{name:"National Institute for Pharmaceutical Research and Development",country:{name:"Nigeria"}}},{id:"436430",title:"Associate Prof.",name:"Mesut",middleName:null,surname:"Işık",slug:"mesut-isik",fullName:"Mesut Işık",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/436430/images/19686_n.jpg",biography:null,institutionString:null,institution:{name:"Bilecik University",country:{name:"Turkey"}}},{id:"268659",title:"Ms.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/268659/images/8143_n.jpg",biography:"Dr. Zhan received his undergraduate and graduate training in the fields of preventive medicine and epidemiology and statistics at the West China University of Medical Sciences in China during 1989 to 1999. He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. 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He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. 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