The use of multi-temporal MODIS images with ground data to distinguish cotton from maize and sorghum fields in smallholder agricultural landscapes of Southern Africa

In this study, we test whether we can significantly (p < 0.05) distinguish cotton (Gossypium hirsutum L.) fields from maize (Zea mays L.) and sorghum (Sorghum bicolor) fields in smallholder agricultural landscapes of the Mid-Zambezi Valley, Zimbabwe, using a temporal series of 16-day Moderate Resolution Imaging Spectroradiometer – normalized difference vegetation index (MODIS NDVI) data. We test this hypothesis at different phenological stages over the growing season, that is, early green-up onset, late green-up onset, green-peak, early senescence and late senescence. We also statistically compare the rate of change in the greenness of the three crops at the three phenological stages. Results show that we can significantly (p < 0.05) distinguish cotton fields from maize and sorghum fields using 16-day MODIS NDVI data during the late green-up onset as well as during the green-peak stage of the three crops. Our results indicate that cotton can successfully be distinguished from maize and sorghum in spatially heterogeneous smallholder agricultural landscapes using temporal MODIS NDVI.

[1]  Peng Gong,et al.  Removing shadows from Google Earth images , 2010 .

[2]  M. Boschetti,et al.  Multi-year monitoring of rice crop phenology through time series analysis of MODIS images , 2009 .

[3]  Chris Funk,et al.  Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe , 2009 .

[4]  J. Mustard,et al.  Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil , 2008 .

[5]  V. Simonneaux,et al.  The use of high‐resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco , 2008 .

[6]  P. C. Doraiswamy,et al.  Crop classification in the U.S. Corn Belt using MODIS imagery , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[7]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[8]  J. Arnold,et al.  Assessment of MODIS-EVI, MODIS-NDVI and VEGETATION-NDVI Composite Data Using Agricultural Measurements: An Example at Corn Fields in Western Mexico , 2006, Environmental monitoring and assessment.

[9]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[10]  Changsheng Li,et al.  Mapping paddy rice agriculture in southern China using multi-temporal MODIS images , 2005 .

[11]  A. Skidmore,et al.  The response of elephants to the spatial heterogeneity of vegetation in a Southern African agricultural landscape , 2005, Landscape Ecology.

[12]  N. Gaidet,et al.  A participatory counting method to monitor populations of large mammals in non-protected areas: a case study of bicycle counts in the Zambezi Valley, Zimbabwe , 2003, Biodiversity & Conservation.

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

[14]  A. J. Stern,et al.  Crop Yield Assessment from Remote Sensing , 2003 .

[15]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[16]  D. Roy,et al.  Achieving sub-pixel geolocation accuracy in support of MODIS land science , 2002 .

[17]  DISCRIMINATING CROPPING PATTERNS FOR THE U.S. CENTRAL GREAT PLAINS REGION USING TIME-SERIES MODIS 250-METER NDVI DATA - PRELIMINARY RESULTS , 2002 .

[18]  D. Legates,et al.  Crop identification using harmonic analysis of time-series AVHRR NDVI data , 2002 .

[19]  L. Venkataratnam,et al.  Influence of plant pigments on spectral reflectance of maize, groundnut and soybean grown in semi-arid environments , 2001 .

[20]  Collectif.  Hommes et les animaux dans la moyenne vallée du Zambèze, Zimbabwe / The Mankind and the Animal in the Mid Zambezi Valley , 2001 .

[21]  D. Fuller,et al.  Trends in NDVI time series and their relation to rangeland and crop production in Senegal, 1987-1993 , 1998 .

[22]  A. Bondeau,et al.  Combining agricultural crop models and satellite observations: from field to regional scales , 1998 .

[23]  M. S. Rasmussen.  Operational yield forecast using AVHRR NDVI data: reduction of environmental and inter-annual variability , 1997 .

[24]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[25]  Thomas S. Pagano,et al.  Moderate Resolution Imaging Spectroradiometer (MODIS) , 1993, Defense, Security, and Sensing.

[26]  C. Tucker,et al.  Satellite remote sensing of primary production , 1986 .

[27]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[28]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .