%0 Journal Article %1 DoraiswamyMODIS2004 %A Doraiswamy, P. C. %A Hatfield, J. L. %A Jackson, T. J. %A Akhmedov, B. %A Prueger, J. %A Stern, A. %B 2002 Soil Moisture Experiment (SMEX02) %D 2004 %J Remote Sensing of Environment %K Crop MODIS Soil application assimilation crops mapping models modis moisture optical reflectance remotesensing satellite uncertainty vegetation %N 4 %P 548--559 %T Crop condition and yield simulations using Landsat and MODIS %U http://www.sciencedirect.com/science/article/B6V6V-4D3B3J8-1/1/787fd89cbcf6981aac93c352693d3145 %V 92 %X Monitoring crop condition and yields at regional scales using imagery from operational satellites remains a challenge because of the problem in scaling local yield simulations to the regional scales. NOAA AVHRR satellite imagery has been traditionally used to monitor vegetation changes that are used indirectly to assess crop condition and yields. Additionally, the 1-km spatial resolution of NOAA AVHRR is not adequate for monitoring crops at the field level. Imagery from the new MODIS sensor onboard the NASA Terra satellite offers an excellent opportunity for daily coverage at 250-m resolution, which is adequate to monitor field sizes are larger than 25 ha. A field study was conducted in the predominantly corn and soybean area of Iowa to evaluate the applicability of the 8-day MODIS composite imagery in operational assessment of crop condition and yields. Ground-based canopy reflectance and leaf area index (LAI) measurements were used to calibrate the models. The MODIS data was used in a radiative transfer model to estimate LAI through the season. LAI was integrated into a climate-based crop simulation model to scale from local simulation of crop development and responses to a regional scale. Simulations of corn and soybean yields at a 1.6�1.6-km2 grid scale were comparable to county yields reported by the USDA-National Agricultural Statistics Service (NASS). Weekly changes in soil moisture for the top 1-m profile were also simulated as part of the crop model as one of the critical parameters influencing crop condition and yields.

@article{DoraiswamyMODIS2004, abstract = {Monitoring crop condition and yields at regional scales using imagery from operational satellites remains a challenge because of the problem in scaling local yield simulations to the regional scales. NOAA AVHRR satellite imagery has been traditionally used to monitor vegetation changes that are used indirectly to assess crop condition and yields. Additionally, the 1-km spatial resolution of NOAA AVHRR is not adequate for monitoring crops at the field level. Imagery from the new MODIS sensor onboard the NASA Terra satellite offers an excellent opportunity for daily coverage at 250-m resolution, which is adequate to monitor field sizes are larger than 25 ha. A field study was conducted in the predominantly corn and soybean area of Iowa to evaluate the applicability of the 8-day MODIS composite imagery in operational assessment of crop condition and yields. Ground-based canopy reflectance and leaf area index (LAI) measurements were used to calibrate the models. The MODIS data was used in a radiative transfer model to estimate LAI through the season. LAI was integrated into a climate-based crop simulation model to scale from local simulation of crop development and responses to a regional scale. Simulations of corn and soybean yields at a 1.6�1.6-km2 grid scale were comparable to county yields reported by the USDA-National Agricultural Statistics Service (NASS). Weekly changes in soil moisture for the top 1-m profile were also simulated as part of the crop model as one of the critical parameters influencing crop condition and yields.}, added-at = {2008-05-15T12:42:42.000+0200}, author = {Doraiswamy, P. C. and Hatfield, J. L. and Jackson, T. J. and Akhmedov, B. and Prueger, J. and Stern, A.}, biburl = {https://www.bibsonomy.org/bibtex/2f77b75e3906f4a4113f3b01b94ddc479/jgomezdans}, booktitle = {2002 Soil Moisture Experiment (SMEX02)}, description = {ScienceDirect - Remote Sensing of Environment : Crop condition and yield simulations using Landsat and MODIS}, interhash = {0b3ec8f23f9a331ef2d1e37f01e81091}, intrahash = {f77b75e3906f4a4113f3b01b94ddc479}, journal = {Remote Sensing of Environment}, keywords = {Crop MODIS Soil application assimilation crops mapping models modis moisture optical reflectance remotesensing satellite uncertainty vegetation}, month = {#sep#}, number = 4, pages = {548--559}, timestamp = {2008-05-15T12:42:43.000+0200}, title = {Crop condition and yield simulations using Landsat and MODIS}, url = {http://www.sciencedirect.com/science/article/B6V6V-4D3B3J8-1/1/787fd89cbcf6981aac93c352693d3145}, volume = 92, year = 2004 }