Karkee, M, Steward, BL, Tang, L, Aziz, SA (2009). Quantifying sub-pixel signature of paddy rice field using an artificial neural network. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 65(1), 65-76.
Remote sensing (RS) can be used for regional and global scale monitoring of crop development, crop health, and cropping practices and also for water resources planning and designing. Lower spatial resolution, such as 1 km. MODIS imagery, for example, is useful for national, regional and global scale studies, but sub-pixel mixing of different landuses may occur. In the case of rice production, one pixel of such resolution may include land area with rice grown under different cropping practices such as one, two and three crops per year rice. A method was developed for quantifying sub-pixel landuses of individual rice types using an artificial neural network (ANN). Temporal patterns of normalized differential vegetation index (NDVI) depend on and result from the complex relationship between NDVI and cropping practice parameters associated with a pixel. In the case of a rice field, these parameters consist of the area fractions of different types of rice and their emergence dates. An ANN was used as a model inverter to estimate these parameters. The data for this research were produced numerically using the soil-water-atmosphere-plant (SWAP) crop growth model. Crop area fractions within a pixel were predicted with an RMSE of 1.3% and an average estimated emergence date error of 4 days. A representative experiment conducted using a 2.4 GHz Pentium processor based desktop computer took about 1.22 mu s per pixel, which was substantially faster than the genetic algorithm based approach of Ines and Honda [Ines, A.V.M., Honda, K., 2005. On quantifying agricultural and water management practices from low spatial resolution RS data using genetic algorithms: a numerical study for mixed-pixel environment. Advances in Water Resources 28 (8), 856-870]. The ANN based approach was computationally, very efficient and thus practical to apply to satellite imagery consisting of millions of pixels. (C) 2008 Elsevier B.V. All rights reserved.