Kharif rice acreage estimation in Orissa using NOAA-AVHRR data

Panigrahy, Sushma; Parihar, J. S.; Patel, N. K.

doi:10.1007/bf02991883

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…The training samples were randomly located and visually interpreted from the ZY1-02C fused image (5 m in spatial resolution). The class separability of the training data set was analyzed using the Jeffries-Matusita (J-M) distance metric between classes [43,44]. A larger J-M distance indicates more distinct distributions between two classes.…”

Section: Remote Sensing Datamentioning

confidence: 99%

Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images

et al. 2015

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Rice is one of the most important crops in the world; meanwhile, the rice field is also an important contributor to greenhouse gas methane emission. Therefore, it is important to get an accurate estimation of rice acreage for both food production and climate change related studies. The eastern plain region is one of the major single-cropped rice (SCR) growing areas in China. Subjected to the topography and intensified human activities, the rice fields are generally fragmented and irregular. How remote sensing can meet this challenge to accurately estimate the acreage of the rice in this region using medium-resolution imagery is the topic of this study. In this study, the applicability of the Chinese HJ-1A/B satellites and a two-band enhanced vegetation index (EVI2) was investigated. Field campaigns were carried out during the rice growing season and ground-truth data were collected for classification accuracy assessments in 2012. A stepwise classification strategy utilizing the EVI2 signatures during key phenology stages, i.e., the transplanting and the vegetative to reproductive transition phases, of the SCR was proposed, and the overall classification accuracy was 91.7%. The influence of the mixed pixel and boundary effects to classification accuracy was also investigated. This work demonstrates OPEN ACCESSRemote Sens. 2015, 7 3468 that the Chinese HJ-1A/B data are suitable data source to estimating SCR cropping area under complex land cover composition.

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Section: Remote Sensing Datamentioning

confidence: 99%

Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images

et al. 2015

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“…Due to this, some research on monitoring crop growth have been carried out. Most of these research utilizes a medium resolution imagery such as NOAA-AVHRR and MODIS (Panigrahy et al, 1992, Xiao et al, 2005, Wataru et al, 2006, Sun et al, 2009, Kim and Yeom, 2012 and RADARSAT (Ribbes andToan, 1999 , Shao et al, 2001). In Indonesia, the prediction of the greenery rate of agricultural crops, especially rice has been conducted continuously by Space Agency (LAPAN) using NOAA and MODIS satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

Spectral Characteristics of High-Yielding Varieties of Rice Plants Using Landsat 8 Data

Dewi¹,

Supianto²,

Sutrisno³

2013

Agrivita.J.Agr.Sci

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In applying remote sensing technology for inventory, evaluation and estimation of rice crop production, required the data of spectral characteristics changing of the plants during its growth phase. By identifying spectral characteristics, it will be recognized the objects in the images. This study identifies the spectral characteristics of high-yielding varieties of rice plants during their growth in Malang regency. Based on the results of field survey, the high yielding varieties that are commonly planted consist of IR64, Ciherang and Membramo. Then, from the identification of vegetation index is known thet al.,l these three varieties have different growth patterns, where the most distinct pattern found in IR64.

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“…1997, Panigrahy & Sharma 1997, Oette et al 2000, Shao et al 2001, David et al 2004. Some of this research has been used globally as along with moderate image resolution, such as National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS), to monitor rice fields (Panigrahy et al 1992, Fang et al 1998, Wataru et al 2006, Xiao et al 2005. However, the use of moderate and global spatial resolution of satellite imaging has been restricted, particularly in small rice areas because many types of land cover appear in one pixel, which reduces the accuracy of the assessment (Strahler et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Spectral Characteristics and Mapping of Rice Plants Using Multi-Temporal Landsat Data

Nuarsa¹,

Nishio²,

Hongo³

2011

JAS

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The visible band of Landsat ETM+ (Band 1, Band 2, and Band 3) showed a weak exponential relationship to rice age; however, the reflective infrared band of Landsat ETM+ (Band 4 and B5) and the entire vegetation index showed a strong exponential relationship to rice age. Rice Growth Vegetation Index (RGVI) developed in this study is the best vegetation index compared to existing vegetation indices. The relationship between rice age and RGVI has shown the highest determination coefficient (R 2 ) of 0.9045. The RGVI then used to develop a model to rice plant mapping. Quantitative comparison of the rice plant area between analysis results and reference data showed a linear relationship, with the equation y = 0.920x -3.841 and R² = 0.971, where y is the rice plant area of reference data, and x is the rice plant area of the analysis results of the Landsat ETM+. The standard error of this estimation was 43.04 ha.

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Kharif rice acreage estimation in Orissa using NOAA-AVHRR data

Cited by 6 publications

References 7 publications

Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images

Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images

Spectral Characteristics of High-Yielding Varieties of Rice Plants Using Landsat 8 Data

Spectral Characteristics and Mapping of Rice Plants Using Multi-Temporal Landsat Data

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