0000011258 00000 n Yield maps generated from the image data using the regression equations agreed well with those from the yield monitor data. for use of their fields and harvest equipment. 0000044622 00000 n Two-band hand-held radiometer data from a winter wheat field, collected on 21 dates during the spring growing season, were correlated with within-field final grain yield. high-resolution airborne multispectral and hyperspectral imagery has been used for this purpose. Three types of hyperspectral predictors were tested: optimum multiple narrow band reflectance (OMNBR), narrow band normalized difference vegetation index (NDVI) involving all possible two-band combinations of 490 channels, and the soil-adjusted vegetation indices. 2000. The, surrounding the fields. �ꇆ��n���Q�t�}MA�0�al������S�x ��k�&�^���>�0|>_�'��,�G! blue (450-520 nm), green (520-600 nm), red (630-690 nm), and NIR (760-900 nm). 0000009835 00000 n 0000010306 00000 n Moreover, multi-image combinations generally improved the correlations with yield over single images, and the best three-image combination resulted in the highest overall correlation (r = 0.90) between yield and unconstrained plant abundance. 0000003768 00000 n This regrouping produced similar, results to the four-zone classifications shown in tables 3, munity more choices of remote sensing products. 0000008887 00000 n imagery for mapping plant growth and yield variability in cotton fields. using a FieldSpec HandHeld spectroradiometer (Analytical, 1050 nm portion of the spectrum with a nominal spectral, resolution of 1.4 nm. 0000044860 00000 n As plant canopy was closed, airborne multispectral images of the field were acquired using a 3-CCD MS4100 camera. to yield at original submeter, 2.8 and 8.4m resolutions and the QuickBird imagery was related to yield at 2.8 and 8.4m resolutions. be sufficient and can be used to generate yield maps. Vegetation indices including An alternative vegetation index, the relative nitrogen vegetation index, was not better than NDVI as an indicator of nitrogen stress. Information concerning the spatial variation in crop yield has become necessary for site-specific crop management. acquired from south Texas in the Cotton, and positively correlated to the NIR band and each, tude of the correlation coefficients varied from 0.47 for the, NIR band to 0.66 for NDVI for field 1, and from 0.57 for the, NIR band to 0.77 for BNDVI for field 2. A square white area (a gas well) located in, from the gas well to the northeast corner of the field can be, clearly seen on the images. The rectified, spectral images were converted to reflectance based on three, values to the digital count values from the four tarps. To de- velop management approaches to address this variability, high spatial resolution soil property maps are often needed. �V��)g�B�0�i�W��8#�8wթ��8_�٥ʨQ����Q�j@�&�A)/��g�>'K�� �t�;\�� ӥ$պF�ZUn����(4T�%)뫔�0C&�����Z��i���8��bx��E���B�;�����P���ӓ̹�A�om?�W= 0000003156 00000 n The best of these two-band indices were further tested to see if soil adjustment or nonlinear fitting could improve their predictive accuracy. Spectral unmixing techniques provide an alternative approach to quantifying crop canopy abundance within each image pixel and have the potential for mapping crop yield variability. 0000009141 00000 n Field. 0000000016 00000 n Yield maps generated from the three best images agreed well with a yield map from the yield monitor data. Positioning System (GPS) receiver (Pathfinder Pro XRS, Trimble Navigation, Ltd., Sunnyvale, Cal.). The coefficient of determination between yield and the imagery increased with pixel size because of the smoothing effect. A QuickBird 2.8-m four-band image covering 0000050442 00000 n (PCs) for QuickBird and airborne MegaPlus imagery for two cotton fields in south Texas in 2003. models and all PCs remaining in the models were significant at the 0.0001 level. Color-infrared (CIR) digital images were acquired from three grain sorghum fields on five different dates during the 1998 growing season. Further research is needed to evaluate the, advantages and disadvantages of QuickBird imagery for, yield estimation as compared with other types of remote, We thank Rene Davis and Fred Gomez for acquiring the, assistance in image processing. When cotton was harvested in late July, monitor was calibrated using five calibration loads. Barnes, E. M., K. A. Sudduth, J. W. Hummel, S. M. Lesch, D. L. Remote- and ground-based sensor techniques to map soil, Cavazos. Manipulation of high spatial resolution aircraft. sorghum fields as compared with airborne multispectral image data. a strong linear relation between RVI derived from in situ and RVI derived from satellite data (R = 0.75; p = 0.000). Wiegand, C. L., A. J. Richardson, D. E. Escobar, and A. H. identify spatial plant growth variability for grain. In practice, this reduced resolution may be, sufficient, since areas smaller than one or two, bands of the QuickBird imagery for the whole field and for. 0000004590 00000 n Correlation analyses showed grain yield was significantly related to the individual near-infrared (NIR), red, and green bands of the CIR images and the normalized difference vegetation index (NDVI) for the five dates. Much research has focused on the use of intensive grid soil sampling and yield monitors to identify within-field spatial variability in precision farming. 0000018830 00000 n 1995. The clustered images were geo-referenced, and spatially analyzed using a GIS package. [35] compared QuickBird imagery and airborne imagery for mapping grain sorghum yield patterns. 0000146156 00000 n remotely sensed reflectance data and cotton growth and yield. Digital aerial imaging proves to be a promising tool for obtaining spatial in-field variability in the crop field for site-specific management and yield prediction. 2003 growing season. Model and statistical methods show promise to integrate these ground- and image-based data sources to maximize the information from each source for soil property mapping. USING IN SITU HYPERSPECTRAL MEASUREMENTS AND HIGH RESOLUTION SATELLITE IMAGERY TO DETECT STRESS IN WHEAT IN EGYPT, High resolution satellite imaging sensors for precision agriculture, The use of unmanned aerial systems (UASs) in precision agriculture, Assessing flue-cured tobacco crop growth and biomass response to nitrogen application levels using canopy reflectance, Hyperspectral Imagery for Mapping Crop Yield for Precision Agriculture, Estimation of cotton yield with varied irrigation and nitrogen treatments using aerial multispectral imagery, In-field variability detection and spatial yield modeling for corn using digital aerial imaging, Remote and Ground-Based Sensor Techniques to Map Soil Properties, Relationship of spectral data to grain-yield variation, Mapping grain sorghum growth and yield variations using airborne multispectral digital imagery, Relationships between remotely sensed reflectance data and cotton growth and yield, Airborne multispectral imagery for mapping variable growing conditions and yields of cotton, grain sorghum, and corn, Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics, Relationships Between Yield Monitor Data and Airborne Multidate Multispectral Digital Imagery for Grain Sorghum, Mapping Grain Sorghum Yield Variability Using Airborne Digital Videography, Airborne Videography to Identify Spatial Plant Growth Variability for Grain Sorghum, Cotton Disease Identification & Delineation Based on Remote Sensing, Image debating based on improved dark channel prior method for agriculture, Evaluating high resolution SPOT 5 satellite imagery to estimate crop yield, Airborne Hyperspectral Imagery for Mapping Crop Yield Variability, Using High Resolution QuickBird Satellite Imagery for Cotton Yield Estimation, Comparison of Airborne Multispectral and Hyperspectral Imagery for Estimating Grain Sorghum Yield.

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