Crop classification based on multi-temporal satellite remote sensing data for agro-advisory services

Yogita Y. Karale; Jayant Mohite; Bhushan Jagyasi

doi:10.1117/12.2069278

8 November 2014 Crop classification based on multi-temporal satellite remote sensing data for agro-advisory services

Yogita Y. Karale, Jayant Mohite, Bhushan Jagyasi

Proceedings Volume 9260, Land Surface Remote Sensing II; 926004 (2014) https://doi.org/10.1117/12.2069278
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

In this paper, we envision the use of satellite images coupled with GIS to obtain location specific crop type information in order to disseminate crop specific advises to the farmers. In our ongoing mKRISHI R project, the accurate information about the field level crop type and acreage will help in the agro-advisory services and supply chain planning and management. The key contribution of this paper is the field level crop classification using multi temporal images of Landsat-8 acquired during November 2013 to April 2014. The study area chosen is Vani, Maharashtra, India, from where the field level ground truth information for various crops such as grape, wheat, onion, soybean, tomato, along with fodder and fallow fields has been collected using the mobile application. The ground truth information includes crop type, crop stage and GPS location for 104 farms in the study area with approximate area of 42 hectares. The seven multi-temporal images of the Landsat-8 were used to compute the vegetation indices namely: Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR) and Difference Vegetation Index (DVI) for the study area. The vegetation indices values of the pixels within a field were then averaged to obtain the field level vegetation indices. For each crop, binary classification has been carried out using the feed forward neural network operating on the field level vegetation indices. The classification accuracy for the individual crop was in the range of 74.5% to 97.5% and the overall classification accuracy was found to be 88.49%.

Citation Download Citation

Yogita Y. Karale, Jayant Mohite, and Bhushan Jagyasi "Crop classification based on multi-temporal satellite remote sensing data for agro-advisory services", Proc. SPIE 9260, Land Surface Remote Sensing II, 926004 (8 November 2014); https://doi.org/10.1117/12.2069278

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