Search

The excessive use of N in agriculture has created various environmental and economic problems. Remote sensing and unmanned aerial vehicles (UAV) are feasible solutions to infer the status of a crop and enable a better management during the growing season. The objective of this study was to correlate experimental N content and wheat (Triticum aestivum L.) crop aboveground biomass data with vegetation indices estimated using UAV images. In this study, the N nutrition index and N dilution curve were used as indicators of the state of plant N; input variables to estimate these indicators were the N content and aboveground biomass. Four flight campaigns were conducted at different phenological stages of a wheat crop and seven N doses were evaluated. A linear relationship of blue normalized difference vegetation index (BNDVI) and green normalized difference vegetation index (GNDVI) with aboveground biomass and N content was identified. BNDVI and biomass demonstrated high R² during boots swollen and end of anthesis growth stages (0.62 and 0.68, respectively), while GNDVI showed the highest R² during the ear half emerged and beginning of anthesis growth stages (0.84 and 0.79, respectively). For N content estimation, GNDVI showed a higher correlation than BNDVI, and the adjustment curve showed an R² up to 0.81 only for the last flight (end of anthesis), BNDVI showed an R² of 0.78. Remote sensing and vegetation indices estimated from UAV images can be reliably used to estimate N content and wheat biomass, contributing to knowing the crop N status. [ABSTRACT FROM AUTHOR]

Traditional estimations of evapotranspiration (ET ) are based on the crop coefficient (Kc). This can be disadvantageous when precise estimations of crop water uptake are required in the Irrigation Districts. Using satellite images, it is possible to estimate vegetation indexes (VI ), such as the Normalized Difference Vegetation Index (NDVI ) and the Soil Adjusted Vegetation Index (SAVI ). These indexes enable monitoring crop development and estimating precisely spatial and temporal Kc variability. The objective of this study was to validate the use of VI to estimate Kc and ET of wheat (Triticum aestivum) in the Río Yaqui Irrigation District, Sonora, Mexico. Validation was carried out with eight images from the sensors TM and ETM+ of the LANDSAT 5 and 7 satellites and measurements of turbulent flux with the Eddy Covariance (EC ) technique for the year 2008. ET estimated from VI and measurement with EC showed a high degree of correspondence. For the eight images, the square root of the Root Mean Square Error (RMSE) was 0.69 mm d-1, the Mean Absolute Error (MAE) 0.62 mm d-1, the index of agreement (d) 0.91 for NDVI and RMSE of 0.64 mm d-1, MAE of 0.57 and an index of agreement of 0.92 for SAVI in daily estimations. It is thus concluded that VI allow to estimate spatial and temporal variability of the Kc and ET with precision in extensive agricultural regions. [ABSTRACT FROM AUTHOR]

Copyright of Agrociencia is the property of Colegio de Postgraduados and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)