Monitoring cotton crop condition through synergy of optical and radar remote sensing

Haldar, Dipanwita; Tripathy, Rojalin; Dave, Viral A.; Dave, Rucha; Bhattacharya, Bimal K.; Misra, Arundhati

doi:10.1080/10106049.2020.1726506

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…The 11 vegetation indices and their calculation formulas were shown in Table 2. These vegetation indices were composed of various combinations of water‐sensitive bands, used for retrieving VWC (Haldar et al., 2020; Wang and Qu, 2007; Zhang et al., 2018). As Sentinel‐2 has two short‐wave infrared bands, they can provide NDWI1, NDWI2, MSI1, and MSI2 (band 11 and band 12 of Sentinel‐2 are SWIR1 and SWIR2, respectively, NDWI1 and MSI1 correspond to band 11 of Sentinel‐2, and NDWI2 and MSI2 correspond to band 12 of Sentinel‐2).…”

Section: Methodsmentioning

confidence: 99%

Modeling and Assessment of Vegetation Water Content on Soil Moisture Retrieval via the Synergistic Use of Sentinel‐1 and Sentinel‐2

Wang

Jin

et al. 2022

Earth and Space Science

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As one of the important parameters of the fifty "basic climate variables", soil moisture plays an important role in ecology, hydrology, climatology, and meteorology (Attarzadeh et al., 2018;Laachrate et al., 2020). From an ecological point of view, soil moisture is an important water element absorbed by plant roots, which directly affects plant growth, soil respiration, and plant function (Blyth et al., 2011). For the hydrology, soil moisture controls surface infiltration and redistribution of surface runoff and is an important part of the global water cycle (Kornelsen & Coulibaly, 2013). From a climatological perspective, soil moisture regulates the distribution and circulation of water and energy on the land surface, which in turn affects the global climate pattern (Seneviratne et al., 2010).Studies on carbon and water cycles, climate change, and energy balance with large-scale and spatial-temporal characteristics often require the participation of global soil moisture products such as Soil Moisture Active and Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), etc (Carrera et al., 2017;Jones et al., 2017). However, these products with low spatial resolution cannot be used for research studies on crop yield evaluation, drought monitoring, and agricultural irrigation management (Carrao et al., 2016;Rivers et al., 2015). Ground measurements based on this point is often expensive and laborious. Therefore, it is extremely necessary and important to achieve high spatial and temporal resolutions of soil moisture retrieval. Optical and thermal infrared remote sensing are susceptible to surface coverings and severely affected by weather clouds and fog, and the accuracy of soil moisture retrieval is greatly reduced. High-resolution Synthetic Aperture Radar (SAR) data with microwave bands of band C, band X, and band L (e.g., COSMO-SkyMed, Sentinel-1, and ALOS), which have the advantage of being unaffected by weather, low frequency band, and being sensitive to soil moisture, and thus

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Section: Methodsmentioning

confidence: 99%

Modeling and Assessment of Vegetation Water Content on Soil Moisture Retrieval via the Synergistic Use of Sentinel‐1 and Sentinel‐2

Wang

Jin

et al. 2022

Earth and Space Science

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“…For instance, the normalized difference vegetation index (NDVI) is used to estimate crop biomass and leaf area index (LAI) [53][54][55]. Despite the sensitivity of optical wavelengths to crop biophysical and biochemical conditions, optical remote sensing is limited in the presence of clouds, cloud shadows, and other atmospheric conditions [56,57]. This limitation complicates the use of optical sensors for operational monitoring, considering that clouds cover two-thirds of the Earth for at least part of the growing season [58].…”

Section: Introductionmentioning

confidence: 99%

“…SARs measure the intensity and phase of target scattered signals [13]. The intensity and phase of microwave scattering depend on system configurations (incident angle, frequency, and polarization), as well as on the characteristics of the agricultural target (dielectric properties, soil surface roughness, and canopy structure) [4,56,[65][66][67][68]. SARs can provide important information about crop development throughout the growing season because the scattering, intensity, and phase are impacted by canopy structure and water content [69].…”

Section: Introductionmentioning

confidence: 99%

A Meta-Analysis of Remote Sensing Technologies and Methodologies for Crop Characterization

et al. 2022

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Climate change and population growth risk the world’s food supply. Annual crop yield production is one of the most crucial components of the global food supply. Moreover, the COVID-19 pandemic has stressed global food security, production, and supply chains. Using biomass estimation as a reliable yield indicator, space-based monitoring of crops can assist in mitigating these stresses by providing reliable product information. Research has been conducted to estimate crop biophysical parameters by destructive and non-destructive approaches. In particular, researchers have investigated the potential of various analytical methods to determine a range of crop parameters using remote sensing data and methods. To this end, they have investigated diverse sources of Earth observations, including radar and optical images with various spatial, spectral, and temporal resolutions. This paper reviews and analyzes publications from the past 30 years to identify trends in crop monitoring research using remote sensing data and tools. This analysis is accomplished through a systematic review of 277 papers and documents the methods, challenges, and opportunities frequently cited in the scientific literature. The results revealed that research in this field had increased dramatically over this study period. In addition, the analyses confirmed that the normalized difference vegetation index (NDVI) had been the most studied vegetation index to estimate crop parameters. Moreover, this analysis showed that wheat and corn were the most studied crops, globally.

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“…DSM is not only cost-effective; it permits ascertainment of a purpose quantitative measure of forecast apprehension, which is generally not offered in conventional techniques ( Hussain et al ). Remote sensing (RS) data promises secondary data sources to improve the digital soil map over the past few decades ( Haldar et al, 2022 ). Using RS techniques rather than traditional methods to get these data can be time taking and painstaking ( Stevens, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of soil type in Pakistan using remote sensing and machine learning

Haq

Shahbaz

Asif

et al. 2022

PeerJ Computer Science

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Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning.

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Monitoring cotton crop condition through synergy of optical and radar remote sensing

Cited by 9 publications

References 23 publications

Modeling and Assessment of Vegetation Water Content on Soil Moisture Retrieval via the Synergistic Use of Sentinel‐1 and Sentinel‐2

Modeling and Assessment of Vegetation Water Content on Soil Moisture Retrieval via the Synergistic Use of Sentinel‐1 and Sentinel‐2

A Meta-Analysis of Remote Sensing Technologies and Methodologies for Crop Characterization

Identification of soil type in Pakistan using remote sensing and machine learning

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