Estimating soybean ground cover from satellite images using neural-networks models

The ground cover is a necessary parameter for agronomic and environmental applications. In Argentina, soybean (Glycine max (L.) Merill) is the most important crop; therefore it is necessary to determine its amount and configuration. In this work, neural-network (NN) models were developed to calculate soybean percentage ground cover (fractional vegetation cover, fCover) and to compare the accuracy of the estimate from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat satellites data. The NN design included spectral values of the red and near-infrared (NIR) bands as input variables and one neuron output, which expressed the estimated coverage. Data of fCover were acquired throughout the growing season in the central plains of Córdoba (Argentina); they were used for training and validating the networks. The results show that the NNs are an appropriate methodology for estimating the temporal evolution of soybean coverage fraction from MODIS and Landsat images, with coefficients of determination (R 2) equal to 0.90 and 0.91, respectively.

[1]  W. Fehr,et al.  Stage of Development Descriptions for Soybeans, Glycine Max (L.) Merrill , 1971 .

[2]  R. Colwell.  Remote sensing of the environment , 1980, Nature.

[3]  L. Biehl,et al.  Spectral Estimates of Agronomic Characteristics of Crops , 1984 .

[4]  R. Jackson,et al.  Spectral response of a plant canopy with different soil backgrounds , 1985 .

[5]  P. Chavez.  Radiometric calibration of Landsat Thematic Mapper multispectral images , 1989 .

[6]  Ferdinand Bonn,et al.  Vegetation indices derived from remote sensing for an estimation of soil protection against water erosion , 1995 .

[7]  J. Clevers,et al.  The robustness of canopy gap fraction estimates from red and near-infrared reflectances: A comparison of approaches , 1995 .

[8]  Gérard Dedieu,et al.  Grassland modeling and monitoring with SPOT-4 VEGETATION instrument during the 1997–1999 SALSA experiment , 2000 .

[9]  D. R. Upchurch,et al.  Vegetation index response to leaf area index and fractional vegetated area over cotton , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[10]  J. Scott Armstrong,et al.  Principles of forecasting , 2001 .

[11]  Gary E. Varvel,et al.  Use of Remote-Sensing Imagery to Estimate Corn Grain Yield , 2001 .

[12]  John H. Prueger,et al.  Application of MODIS-derived parameters for regional yield assessment , 2002, SPIE Remote Sensing.

[13]  J. Ord,et al.  Principles of forecasting: A handbook for researchers and practitioners , 2002 .

[14]  Raghavan Srinivasan,et al.  Using Satellite and Field Data with Crop Growth Modeling to Monitor and Estimate Corn Yield in Mexico , 2002 .

[15]  F. Baret,et al.  Validation of neural net techniques to estimate canopy biophysical variables from remote sensing data , 2002 .

[16]  Frédéric Baret,et al.  Training a neural network with a canopy reflectance model to estimate crop leaf area index , 2003 .

[17]  Paul M. Mather,et al.  The use of backpropagating artificial neural networks in land cover classification , 2003 .

[18]  Martha C. Anderson,et al.  A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery ☆ , 2004 .

[19]  R. Fensholt,et al.  Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements , 2004 .

[20]  Thomas J. Jackson,et al.  Crop condition and yield simulations using Landsat and MODIS , 2004 .

[21]  P. C. Doraiswamya,et al.  Crop condition and yield simulations using Landsat and MODIS , 2004 .

[22]  A. J. Stern,et al.  Application of MODIS derived parameters for regional crop yield assessment , 2005 .

[23]  Marco Mancini,et al.  Parsimonious modeling of vegetation dynamics for ecohydrologic studies of water‐limited ecosystems , 2005 .

[24]  T. Jackson,et al.  Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands , 2005 .

[26]  Jingfeng Xiao,et al.  A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA , 2005 .

[27]  Jin Chen,et al.  Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction , 2006 .

[28]  Mónica Bocco,et al.  Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina , 2006 .

[29]  Tim R. McVicar,et al.  On the importance of including vegetation dynamics in Budyko's hydrological model , 2006 .

[30]  Mónica Bocco,et al.  Neural Network Model for Land Cover Classification from Satellite Images , 2007 .

[31]  J. V. Soares,et al.  Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data , 2007 .

[32]  Rodrigo Rizzi,et al.  Imagens do sensor MODIS associadas a um modelo agronômico para estimar a produtividade de soja , 2007 .

[33]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .

[34]  Andreas Müller,et al.  Influence of Local Incidence Angle Effects on Ground Cover Estimates , 2007 .

[35]  Michele Meroni,et al.  Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series , 2008 .

[36]  Stephan J. Maas,et al.  Estimating Ground Cover of Field Crops Using Medium-Resolution Multispectral Satellite Imagery , 2008 .

[37]  S. Ustin,et al.  Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA , 2008 .

[38]  F. Baret,et al.  Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products , 2008 .

[39]  Stephen V. Stehman,et al.  Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss , 2008 .

[40]  Erivelto Mercante,et al.  Mapeamento de semeaduras de soja (Glycine max (L.)Merr.) mediante dados MODIS/Terra E TM/Landsat 5: um comparativo , 2008 .

[41]  A. Gitelson,et al.  Application of Spectral Remote Sensing for Agronomic Decisions , 2008 .

[42]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[43]  Minghua Zhang,et al.  Prediction of Soybean Growth and Development Using Artificial Neural Network and Statistical Models , 2009 .

[44]  Marouane Temimi,et al.  Effect of Land Cover Heterogeneity on Soil Moisture Retrieval Using Active Microwave Remote Sensing Data , 2009, Remote. Sens..

[45]  Minghua Zhang,et al.  Prediction of Soybean Growth and Development Stages Using Artificial Neural Network and Statistical Models: Prediction of Soybean Growth and Development Stages Using Artificial Neural Network and Statistical Models , 2009 .

[46]  D. Barrett,et al.  Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. , 2009 .

[47]  Sudhanshu Sekhar Panda,et al.  Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques , 2010, Remote. Sens..