Accuracy assessment in cotton acreage estimation using Indian remote sensing satellite data

Dutta, Sujay Kumar; Sharma, Shruti; Khera, Ashish; Ajai,; Yadav, Manoj Kumar; Hooda, R. S.; Mothikumar, K E; Manchanda, M. L.

doi:10.1016/0924-2716(94)90011-6

Cited by 27 publications

(9 citation statements)

References 6 publications

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“…Multi-temporal analysis techniques have been applied as well to coarser resolution data such as NOAA-AVHRR [18] and NASA-MODIS data [1,19], taking advantage of high revisit time for these sensors [20]. Other satellite data too, with spectral and spatial features similar to Landsat, have been used for crop mapping achieving satisfactory results, e.g., IRS LISS data [21,22].…”

Section: Introductionmentioning

confidence: 99%

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

et al. 2015

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Abstract:The work focuses on developing a classification tree approach for in-season crop mapping during early summer, by integrating optical (Landsat 8 OLI) and X-band SAR (COSMO-SkyMed) data acquired over a test site in Northern Italy. The approach is based on a classification tree scheme fed with a set of synoptic seasonal features (minimum, maximum and average, computed over the multi-temporal datasets) derived from vegetation and soil condition proxies for optical (three spectral indices) and X-band SAR (backscatter) data. Best performing input features were selected based on crop type separability and preliminary classification tests. The final outputs are crop maps identifying seven crop types, delivered during the early growing season (mid-July). Validation was carried out for two seasons (2013 and 2014), achieving overall accuracy greater than 86%. Results highlighted the contribution of the X-band backscatter (σ°) in improving mapping accuracy and promoting the transferability of the algorithm over a different year, when compared to using only optical features.

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Section: Introductionmentioning

confidence: 99%

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

et al. 2015

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“…Area inventory deviations of less than 2 per cent were obtained by Dutta et al (1994) and decreasing accuracy with lower crop proportion was shown by Chakraborty et al (1995). However, both these studies were undertaken for sites characterized by single dominant crop.…”

Section: Introductionmentioning

confidence: 73%

“…Independent accuracy evaluation can be done by comparing the classified image with wall-to-wall ground truth information as has been done by Dutta et al (1994) for cotton crop in Hissar, Haryana and Chakraborty et al (1995) for rice crop in Orissa. Area inventory deviations of less than 2 per cent were obtained by Dutta et al (1994) and decreasing accuracy with lower crop proportion was shown by Chakraborty et al (1995).…”

Section: Introductionmentioning

confidence: 99%

Accuracy of mustard and wheat inventory at village-level using liss-II digital data in pali district, Rajasthan

Purohit

Joshi

Sankhla

et al. 1997

J Indian Soc Remote Sens

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“…Localised catastrophes could be verified, and variance-covariance analyzed components. In prior investigations, NDVI has a moderate-to-high accuracy in forecasting crop production [3][4][5][6][7][8][9][10][11], but boundary areas (transition from drought to normal conditions) have not been analyzed. In the present study, the NDVI is stable in the transition zone, and strong enough to detect statistically significant differences in plant growth (irrigated vs. non-irrigated), even early in the plant growth cycle (Figure 3).…”

Section: Discussionmentioning

confidence: 99%

Remote sensing and US crop insurance program integrity: data mining satellite and agricultural data

Little¹,

Schucking²,

Gartrell³

et al. 2007

WIT Transactions on Information and Communication Technologies, Vol 38

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The objective of this investigation is to (1) integrate remote sensing data into an existing data warehouse of the US crop insurance program 1990 to 2007, (2) test remote sensing correlations with crop production, and (3) use remotely sensed time series data to assess variation in crop production. Previously (2000 to 2007) data mining of the data warehouse was based upon probabilistic and algorithmic approaches to identification of possible fraud, waste, or abuse. The value of adding satellite data warehouse to data mining resources is provision of (1) an additional empirical metric, (2) objective data on vegetative health, (3) measurable metrics for capturing the variance of plant health, and (4) a means for measuring the covariance of location and production. Refinement of data mining through the addition of satellite data for routine use to reduce fraud, waste, and abuse will ultimately diminish the frequency of false positives.

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Accuracy assessment in cotton acreage estimation using Indian remote sensing satellite data

Cited by 27 publications

References 6 publications

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

Accuracy of mustard and wheat inventory at village-level using liss-II digital data in pali district, Rajasthan

Remote sensing and US crop insurance program integrity: data mining satellite and agricultural data

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