A neural network integrated approach for rice crop monitoring

Chen, C.; McNairn, Heather

doi:10.1080/01431160500421507

Cited by 107 publications

(52 citation statements)

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“…Many rice mapping techniques have been developed and utilization of temporal information has been a successful approach. For rice applications, microwave observations at the relevant configuration are sensitive to growth stages, biomass development, plant height, leaf-ground double bounce, soil moisture, and inundation frequency and duration [19][20][21][22][23][24][25][26]. During rice transplanting periods, the surface contribution of a rice paddy causes low backscatter.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2

et al. 2017

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Assessment and monitoring of rice agriculture over large areas has been limited by cloud cover, optical sensor spatial and temporal resolutions, and lack of systematic or open access radar. Dense time series of open access Sentinel-1 C-band data at moderate spatial resolution offers new opportunities for monitoring agriculture. This is especially pertinent in South and Southeast Asia where rice is critical to food security and mostly grown during the rainy seasons when high cloud cover is present. In this research application, time series Sentinel-1A Interferometric Wide images (632) were utilized to map rice extent, crop calendar, inundation, and cropping intensity across Myanmar. An updated (2015) land use land cover map fusing Sentinel-1, Landsat-8 OLI, and PALSAR-2 were integrated and classified using a randomforest algorithm. Time series phenological analyses of the dense Sentinel-1 data were then executed to assess rice information across all of Myanmar. The broad land use land cover map identified 186,701 km 2 of cropland across Myanmar with mean out-of-sample kappa of over 90%. A phenological time series analysis refined the cropland class to create a rice mask by extrapolating unique indicators tied to the rice life cycle (dynamic range, inundation, growth stages) from the dense time series Sentinel-1 to map rice paddy characteristics in an automated approach. Analyses show that the harvested rice area was 6,652,111 ha with general (R 2 = 0.78) agreement with government census statistics. The outcomes show strong ability to assess and monitor rice production at moderate scales over a large cloud-prone region. In countries such as Myanmar with large populations and governments dependent upon rice production, more robust and transparent monitoring and assessment tools can help support better decision making. These results indicate that systematic and open access Synthetic Aperture Radar (SAR) can help scale information required by food security initiatives and Monitoring, Reporting, and Verification programs.

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Section: Introductionmentioning

confidence: 99%

Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2

et al. 2017

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“…The basic methodology adopted was to use artificial neural networks as the predictors on a pixel by pixel basis, with the satellite image data as input variables. Neural networks were chosen because they have proven particularly effective in integrating multiple types of spatial data in the past 19 , including in agricultural applications [20][21][22][23] , and because they can easily be adapted to handle a large number of input variables, as required in a multi-temporal context and with large numbers of channels 24 (for example with hyperspectral imagery 25 ) or with combinations of multispectral imagery and multi-polarization imagery as required in this particular application.…”

Section: Yield Prediction Experimentsmentioning

confidence: 99%

Crop yield prediction using multipolarization radar and multitemporal visible/infrared imagery

Davis

Wilkinson

2006

SPIE Proceedings

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This paper describes research undertaken on the improvement of within-field late season yield forecasting for crops such as wheat using multi-temporal visible/infrared satellite imagery and multi-polarization radar satellite imagery. Experiments have been carried out using ASAR imagery from Envisat combined with nine bands of ASTER imagery from the NASA Terra satellite. An experimental test site in an agricultural area in the county of Lincolnshire, UK, has been used. The satellite imagery has been integrated using artificial neural networks which have been trained as predictors of the spatial distributions of yield per unit area in a variety of fields. Ground truth data in the form of yield maps from GPS-enabled combine harvesters have been used to train the neural networks and to evaluate accuracy. The results show that the combinations of ASTER and ASAR imagery can provide enhanced yield predictions with overall correlations of up to 0.77 between predicted and actual yield patterns. The results also show that the use of dual polarization radar data alone is not sufficient to give reasonable yield predictions even in a multi-temporal mode. It has also been shown that varying the architectures of the neural networks with ensembles can improve the overall results. KeywordsAgriculture, remote sensing, yield prediction, radar imagery, multi-temporal data, multi-polarization. Disciplines Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific ComputingComments ©Copyright 2009 SPIE Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. This paper was given at the SPIE ABSTRACTThis paper describes research undertaken on the improvement of within-field late season yield forecasting for crops such as wheat using multi-temporal visible/infrared satellite imagery and multi-polarization radar satellite imagery. Experiments have been carried out using ASAR imagery from Envisat combined with nine bands of ASTER imagery from the NASA Terra satellite. An experimental test site in an agricultural area in the county of Lincolnshire, UK, has been used. The satellite imagery has been integrated using artificial neural networks which have been trained as predictors of the spatial distributions of yield per unit area in a variety of fields. Ground truth data in the form of yield maps from GPS-enabled combine harvesters have been used to train the neural networks and to evaluate accuracy. The results show that the combinations of ASTER and ASAR imagery can provide enhanced yield predictions with overall correlations of up to 0.77 between predicted and actual yield patterns. The results also show that the use of dual polarization radar data alone is not sufficient to give reasonable yield predictions even in a multi-temporal mode. It...

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“…The above reasoning provides the foundation for using AGB to approximate yield through three major sources: (i) an optical data derived vegetation index (e.g., normalized difference vegetation index [NDVI], enhanced vegetation index [EVI]) (Chang, Shen, and Lo 2005;Patel et al 1991, Son et al 2013; (ii) microwave-based backscatters (mostly C-band in previous studies) (Chen and Mcnairn 2006;Inoue, Sakaiya, and Wang 2014a;Kurosu and Chiba 1995); or (iii) calculations based on net primary production using light-use efficiency (Peng et al 2014, Savin andIsaev 2011). Meanwhile, it is important to clarify that AGB does not explain all the variation in yield, and harvest index has to be separately modeled and incorporated in the yield modeling.…”

Section: Introductionmentioning

confidence: 99%