| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
a Laboratorio Nacional de Predicción de Cosechas y Monitoreo Climático, Campo Experimental de Pabellón, INIFAP, Km. 32.5 Carr. Zac-Ags., Ap. Postal #20, Pabellón de Arteaga, Aguascalientes, Mexico
b Spatial Science Lab., Dep. of Forest Science, Texas A&M Univ., College Station, TX 77843, USA
* Corresponding author abaez{at}pabellon.inifap.conacyt.mx
The large-scale monitoring and estimation of crop yield is essential for food security in Mexico. This study developed and validated a method of monitoring and estimating corn (Zea mays L.) yield by means of satellite and ground-based data. In autumn–winter 1999 and spring–summer 2000, eight locations under irrigated and nonirrigated conditions in corn valleys of Mexico were localized by Global Positioning Systems (GPS) and were sampled every 15 d. Photosynthetic active radiation (PAR), leaf area index (LAI), crop development stage (DVS), planting dates, and grain yield data were gathered from the field. The normalized difference vegetation index (NDVI) was derived from NOAA-Advanced Very High Resolution Radiometer (AVHRR) images. A growth model was developed to integrate satellite and ground data. Net primary productivity (NPP) was estimated using PAR and NDVI. Dry weight increase (kg ha-1 d-1) was determined considering NPP and the partitioning factor. Results indicated that the model accounts for 89% of the variability in yields under irrigated conditions and 76% under nonirrigated conditions. The methodology seems advantageous in large-scale monitoring and assessment of corn yield.
Abbreviations: AVHRR, advanced very high resolution radiometer • DAS, days after sowing • DVS, development stage of the crop • GPS, global positioning system • HRPT, high resolution picture transmission • INIFAP, agricultural and forestry national research institute of Mexico • LAI, leaf area index • MVC, maximum value composite • NDVI, normalized difference vegetation index • NIR, near-infrared channel • NPP, net primary productivity • PAR, photosynthetic active radiation • SD, Standard deviation • TOA, top-of-the atmosphere reflectance • VCI, vegetation coefficient index • VIS, visible channel
This article has been cited by other articles:
|
|
 |
|
  D. Inman, R. Khosla, R. Reich, and D. G. Westfall Normalized Difference Vegetation Index and Soil Color-Based Management Zones in Irrigated Maize Agron. J., January 11, 2008; 100(1): 60 - 66. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. Shi, G. R. Ruecker, M. Mueller, C. Conrad, N. Ibragimov, J. P. A. Lamers, C. Martius, G. Strunz, S. Dech, and P. L. G. Vlek Modeling of Cotton Yields in the Amu Darya River Floodplains of Uzbekistan Integrating Multitemporal Remote Sensing and Minimum Field Data Agron. J., September 11, 2007; 99(5): 1317 - 1326. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Ko, S. J. Maas, S. Mauget, G. Piccinni, and D. Wanjura Modeling Water-Stressed Cotton Growth Using Within-Season Remote Sensing Data Agron. J., October 31, 2006; 98(6): 1600 - 1609. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. D. Baez-Gonzalez, J. R. Kiniry, S. J. Maas, M. L. Tiscareno, J. Macias C., J. L. Mendoza, C. W. Richardson, J. Salinas G., and J. R. Manjarrez Large-Area Maize Yield Forecasting Using Leaf Area Index Based Yield Model Agron. J., March 1, 2005; 97(2): 418 - 425. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. B. Lobell, J. I. Ortiz-Monasterio, G. P. Asner, R. L. Naylor, and W. P. Falcon Combining Field Surveys, Remote Sensing, and Regression Trees to Understand Yield Variations in an Irrigated Wheat Landscape Agron. J., January 1, 2005; 97(1): 241 - 249. [Abstract] [Full Text] [PDF]
|
|
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| The SCI Journals | Agronomy Journal | Vadose Zone Journal | |||
| Journal of Natural Resources and Life Sciences Education |
Soil Science Society of America Journal | ||||
| Journal of Plant Registrations | Journal of Environmental Quality |
The Plant Genome | |||
