Can a Combination of UAV-Derived Vegetation Indices with Biophysical Variables Improve Yield Variability Assessment in Smallholder Farms?
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[1] J. Hanway. How a corn plant develops , 1966 .
[2] C. Tucker. Red and photographic infrared linear combinations for monitoring vegetation , 1979 .
[3] S. K. Kim,et al. MAIZE YIELD DETERMINANTS IN FARMER-MANAGED TRIALS IN THE NIGERIAN NORTHERN GUINEA SAVANNA , 1998, Experimental Agriculture.
[4] A. Huete,et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .
[5] A. Viña,et al. Remote estimation of canopy chlorophyll content in crops , 2005 .
[6] K. Giller,et al. Estimating yields of tropical maize genotypes from non-destructive, on-farm plant morphological measurements , 2005 .
[7] W. Raun,et al. In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index , 2006 .
[8] B. Herbert. Land use efficiency under maize-based croping system in Zaria, Nigeria , 2006 .
[9] L. Olarinde,et al. Attitudes towards risk among maize farmers in the dry savanna zone of Nigeria: some prospective policies for improving food production , 2007 .
[10] J. Diels,et al. Assessment of nutrient deficiencies in maize in nutrient omission trials and long-term field experiments in the West African Savanna , 2008, Plant and Soil.
[11] John H. Prueger,et al. Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices , 2010, Remote. Sens..
[12] A. Viña,et al. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops , 2011 .
[13] C. Pacini,et al. Communicating complexity: Integrated assessment of trade-offs concerning soil fertility management within African farming systems to support innovation and development , 2011 .
[14] Ramón Isla Climente,et al. Utilización de imágenes aéreas multiespectrales para evaluar la disponibilidad de nitrógeno en maíz , 2011 .
[15] A. Viña,et al. Green leaf area index estimation in maize and soybean: Combining vegetation indices to achieve maximal sensitivity , 2012 .
[16] B. Wylie,et al. NDVI saturation adjustment: A new approach for improving cropland performance estimates in the Greater Platte River Basin, USA , 2013 .
[17] Bruno Basso,et al. Assessing the Robustness of Vegetation Indices to Estimate Wheat N in Mediterranean Environments , 2014, Remote. Sens..
[18] J. Kovacs,et al. Applications of Low Altitude Remote Sensing in Agriculture upon Farmers' Requests– A Case Study in Northeastern Ontario, Canada , 2014, PloS one.
[19] David B. Lobell,et al. Testing Remote Sensing Approaches for Assessing Yield Variability among Maize Fields , 2014 .
[20] A. Gitelson,et al. Near real-time prediction of U.S. corn yields based on time-series MODIS data , 2014 .
[21] Johanna Link,et al. Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System , 2014, Remote. Sens..
[22] B. Sponholz,et al. Soil Assessment along Toposequences in Rural Northern Nigeria: A Geomedical Approach , 2014 .
[23] Enric Pastor,et al. UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas , 2014, Remote. Sens..
[24] F. A. Vega,et al. Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop , 2015 .
[25] T. Ochsner,et al. Canopeo: A Powerful New Tool for Measuring Fractional Green Canopy Cover , 2015, Agronomy Journal.
[26] C. Carletto,et al. From Tragedy to Renaissance: Improving Agricultural Data for Better Policies , 2015 .
[27] Jaume Lloveras,et al. Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service , 2016, Remote. Sens..
[28] D. Goodin,et al. Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries , 2016, Plant Methods.
[29] Onuk,et al. A Comparative Study of Production Efficiencies Under Cowpea-Maize and Groundnut-Millet Intercropping Systems In The North Central Zone , Nigeria , 2016 .
[30] K. Giller,et al. BEYOND AVERAGES: NEW APPROACHES TO UNDERSTAND HETEROGENEITY AND RISK OF TECHNOLOGY SUCCESS OR FAILURE IN SMALLHOLDER FARMING , 2016, Experimental Agriculture.
[31] Ola Hall,et al. The challenge of comparing crop imagery over space and time , 2016 .
[32] Yang Song. Evaluation of the UAV-Based Multispectral Imagery and Its Application for Crop Intra-Field Nitrogen Monitoring and Yield Prediction in Ontario , 2016 .
[33] S. Nebiker,et al. LIGHT-WEIGHT MULTISPECTRAL UAV SENSORS AND THEIR CAPABILITIES FOR PREDICTING GRAIN YIELD AND DETECTING PLANT DISEASES , 2016 .
[34] F. Tei,et al. RELIABILITY OF NDVI DERIVED BY HIGH RESOLUTION SATELLITE AND UAV COMPARED TO IN-FIELD METHODS FOR THE EVALUATION OF EARLY CROP N STATUS AND GRAIN YIELD IN WHEAT , 2017, Experimental Agriculture.
[35] A. Tagarakis,et al. Proximal Sensing to Estimate Yield of Brown Midrib Forage Sorghum , 2017 .
[36] Wei Guo,et al. High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling , 2017, Front. Plant Sci..
[37] A. Tagarakis,et al. In-Season Estimation of Corn Yield Potential Using Proximal Sensing , 2017 .
[38] Hao Yang,et al. Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives , 2017, Front. Plant Sci..
[39] Baofeng Su,et al. Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications , 2017, J. Sensors.
[40] D. Lobell,et al. Satellite-based assessment of yield variation and its determinants in smallholder African systems , 2017, Proceedings of the National Academy of Sciences.
[41] J. M. Jibrin,et al. Quantifying Variability in Maize Yield Response to Nutrient Applications in the Northern Nigerian Savanna , 2018 .
[42] A. Schut,et al. Assessing yield and fertilizer response in heterogeneous smallholder fields with UAVs and satellites , 2018 .
[43] Jill E. Cairns,et al. High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging , 2018, Remote. Sens..
[44] Ola Hall,et al. Remote Sensing of Yields: Application of UAV Imagery-Derived NDVI for Estimating Maize Vigor and Yields in Complex Farming Systems in Sub-Saharan Africa , 2018, Drones.
[45] Lin Liu,et al. Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models , 2018, Remote. Sens..
[46] Timothy L. Hawthorne,et al. Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data , 2019, Drones.