Effect of acquisition date and TM spectral bands on wheat, mustard and gram classification accuracies

Dadhwal, V. K.; Parihar, J. S.; Ruhal, D. S.; Jarwal, Som; Medhavy, T. T.; Khera, Ashish; Singh, Jogendra

doi:10.1007/bf03007237

Cited by 14 publications

(3 citation statements)

References 5 publications

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“…These results are physically plausible and expected since the MIR band carries additional and complementary information for crop assessment due to its sensitivity to plant water absorption. Similar results were also obtained from analysis of Feb 21 data and these results are in agreement with previous studies using Landsat TM (Dadhwal et al, 1989.…”

Section: Effect Of Spectral Band Combinationsupporting

confidence: 95%

Comparative Evaluation of Indian Remote Sensing Multi‐Spectral Sensors Data for Crop Classification

Singh

Sridhar

Dadhwal

et al. 2002

Geocarto International

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Digital data from four multi spectral sensors having different spatial resolution and spectral channel, LISS-III, LISS-II, LISS-I and WiFS acquired from two Indian Remote Sensing satellite platform ( IRS-1B, IRS-IC) were evaluated for wheat crop classification accuracy over central India. Classification accuracy was assessed at different spatial resolutions using common red and near infrared (nir) bands in the above four sensors data as well as simulated intermediate resolution data. It was observed that accuracy improved with better spatial resolution and there was a considerable increases in accuracy from 188 to 144 m resolution and from 72 m up to 23 m resolution with the modest increase in the intermediate range. Inclusion of the MIR band, and choice of proper acquisition date resulted in significantly higher crop classification accuracies. The results have been evaluated against the future sensor planned on IRS Platform.

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Section: Effect Of Spectral Band Combinationsupporting

confidence: 95%

Comparative Evaluation of Indian Remote Sensing Multi‐Spectral Sensors Data for Crop Classification

Singh

Sridhar

Dadhwal

et al. 2002

Geocarto International

Self Cite

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“…Inclusion of MIR band was found to increase the separability in these crop class pairs leading to better classification. Similar improvements in interclass separability of different crops using MIR band were reported by Dadhwal et al (1989). The raw data of the test site, along with the classified images without and with MIR bands are presented in Fig.…”

Section: Crop Discrimination Using Liss-iii Datasupporting

confidence: 75%

Utilization of Resourcesat-1 data for improved crop discrimination

Sai

Rao

2008

International Journal of Applied Earth Observation and Geoinfor

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“…In an early evaluation of SPOT of 20 m, a larger proportion of individual fields were discernible and multi-crop discrimination was feasible (Sahai et al, 1989). Improvements in multiple crops discrimination was possible through use of SWIR spectral bands or data acquisition of specific bio-window (Dadhwal et al, 1989). Multi-sensor comparative studies highlighted decrease in classification accuracy at larger spatial resolution.…”

Section: Growing Experience On Field-scale Agricultural Applications ...mentioning

confidence: 99%

Progress and Challenges in Earth Observation Data Applications for Agriculture at Field Scale in India and Small Farm Holdings Regions

Ray

Dadhwal

Navalgund

2022

J Indian Soc Remote Sens

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The paper traces the evolution of agricultural remote sensing in India through its four phases: initial exploratory and aerial data based (1969)(1970)(1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979)(1980)(1981)(1982), IRS Utilization Program led (1983( -1995( ), post-IRS-1C launch (1996( -2011 and since the establishment of a dedicated institution for EO-based crop forecasting and other agricultural applications (2012 onward). Published field-scale studies are discussed before introducing the submissions to the special issue of the journal, namely Advanced Geospatial Technologies for Agriculture with Special emphasis on field level Applications. A total of 17 articles grouped under crop discrimination and mapping, crop yield estimation, crop suitability analysis, irrigation management and others (UAV, fodder and field boundary extraction) and 15 of these describe work over India and remaining two have Iran as their study area. These studies are linked to other published work as well as challenges under major issues of data access, use of UAV, use of smartphones, data processing and crop simulation models. Taken together, these articles report substantial improvements in capabilities of field-scale monitoring for developing new class of agricultural applications in India as well as other regions in the world with small farm holdings.

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Effect of acquisition date and TM spectral bands on wheat, mustard and gram classification accuracies

Cited by 14 publications

References 5 publications

Comparative Evaluation of Indian Remote Sensing Multi‐Spectral Sensors Data for Crop Classification

Comparative Evaluation of Indian Remote Sensing Multi‐Spectral Sensors Data for Crop Classification

Utilization of Resourcesat-1 data for improved crop discrimination

Progress and Challenges in Earth Observation Data Applications for Agriculture at Field Scale in India and Small Farm Holdings Regions

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