02391nam a2200253 a 450000100080000000500110000800800410001910000190006024501440007926001020022330000170032550000100034252015730035265000110192565000260193665000190196265000290198165000130201065000250202365300280204865300240207670000170210070000200211720737572018-01-15       2017    bl uuuu     u01u1 u    #d1 aBRANDAO, Z. N.  aGeoestatistical tools and spectral measurements from AWiFs data for evaluation of N and P contents in cotton leaves.h[electronic resource]  aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais ... Santos: INPEc2017  ap. 2408-2415  aSBSR.  aSatellite images and geostatistics are useful tools to assess the nutritional status of plants, and thus, understanding the variability of cotton yield in farmers' fields. The resulting kriged maps provide a unique opportunity to overcome both spatial and temporal scaling challenges and understanding the factors that led to crop yield. To support decisions on improving cotton yield, this study combines the conventional statistic analysis, spatial regression modeling of georreferenced data and AWiFs' vegetations indices assessment. The experiments were carried out in a 47.4 ha commercial field of Goiás state, Brazil. Multispectral satellite images at 56 m spatial resolution were collected in a rainfed cotton field in two dates, on 04/01/2011 and 04/10/2012, from AWiFS sensor during the flowering cotton stage. Measures of leaf nitrogen (N) and phosphorus (P) contents were determined over previously georreferenced central points of 70 plots, each one measuring 80X80 m. Data were analyzed using descriptive statistics and geostatistical analyses by building and setting semivariograms and kriging interpolation. Best correlation was found between IVs and nitrogen contents of cotton leaves. Results indicated that NDVI, MSAVI and SAVI were the best indices to estimate P contents at cotton peak flowering. Identifications of spatial differences were possible using geostatistical methods with remote sensing data obtained from medium resolution satellite images, allowing to identify distinct nutritional needs and growth status of canopy to cotton plants.  aCotton  aPrecision agriculture  aRemote sensing  aAgricultura de Precisão  aAlgodão  aSensoriamento remoto  aÌndices de vegetação  aSpatial variability1 aGREGO, C. R.1 aMANJOLIN, R. C.