{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T06:22:57Z","timestamp":1774938177379,"version":"3.50.1"},"reference-count":87,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T00:00:00Z","timestamp":1654732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007286","name":"California Rice Research Board","doi-asserted-by":"publisher","award":["RR19-7"],"award-info":[{"award-number":["RR19-7"]}],"id":[{"id":"10.13039\/100007286","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Reflectance-based vegetation indices can be valuable for assessing crop nitrogen (N) status and predicting grain yield. While proximal sensors have been widely studied in agriculture, there is increasing interest in utilizing aerial sensors. Given that few studies have compared aerial and proximal sensors, the objective of this study was to quantitatively compare the sensitivity of aerially sensed Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-Edge Index (NDRE) and proximally sensed NDVI for assessing total N uptake at panicle initiation (PI-NUP) and predicting grain yield in rice. Nitrogen response trials were established over a 3-year period (10 site-years) at various locations throughout the Sacramento Valley rice growing region of California. At PI, a multispectral unmanned aircraft system (UAS) was used to measure NDVIUAS and NDREUAS (average ground sampling distance: 3.7 cm pixel\u22121), and a proximal GreenSeeker (GS) sensor was used to record NDVIGS. To enable direct comparisons across the different indices on an equivalent numeric scale, each index was normalized by calculating the Sufficiency-Index (SI) relative to a non-N-limiting plot. Kernel density distributions indicated that NDVIUAS had a narrower range of values that were poorly differentiated compared to NDVIGS and NDREUAS. The critical PI-NUP where yields did not increase with higher PI-NUP averaged 109 kg N ha\u22121 (\u00b14 kg N ha\u22121). The relationship between SI and PI-NUP for the NDVIUAS saturated lower than this critical PI-NUP (96 kg N ha\u22121), whereas NDVIGS and NDREUAS saturated at 111 and 130 kg N ha\u22121, respectively. This indicates that NDVIUAS was less suitable for making N management decisions at this crop stage than NDVIGS and NDREUAS. Linear mixed effects models were developed to evaluate how well each SI measured at PI was able to predict grain yield. The NDVIUAS was least sensitive to variation in yields as reflected by having the highest slope (2.4 Mg ha\u22121 per 0.1 SI). In contrast, the slopes for NDVIGS and NDREUAS were 0.9 and 1.1 Mg ha\u22121 per 0.1 SI, respectively, indicating greater sensitivity to yields. Altogether, these results indicate that the ability of vegetation indices to inform crop management decisions depends on the index and the measurement platform used. Both NDVIGS and NDREUAS produced measurements sensitive enough to inform N fertilizer management in this system, whereas NDVIUAS was more limited.<\/jats:p>","DOI":"10.3390\/rs14122770","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2770","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Comparative Sensitivity of Vegetation Indices Measured via Proximal and Aerial Sensors for Assessing N Status and Predicting Grain Yield in Rice Cropping Systems"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9609-4264","authenticated-orcid":false,"given":"Telha H.","family":"Rehman","sequence":"first","affiliation":[{"name":"Department of Plant Sciences, University of California, Davis, CA 95616, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4043-0841","authenticated-orcid":false,"given":"Mark E.","family":"Lundy","sequence":"additional","affiliation":[{"name":"Department of Plant Sciences, University of California, Davis, CA 95616, USA"},{"name":"Division of Agriculture and Natural Resources, University of California, Davis, CA 95618, USA"}]},{"given":"Bruce A.","family":"Linquist","sequence":"additional","affiliation":[{"name":"Department of Plant Sciences, University of California, Davis, CA 95616, USA"},{"name":"Division of Agriculture and Natural Resources, University of California, Davis, CA 95618, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"117","DOI":"10.2134\/agronj2006.0370c","article-title":"Application of Spectral Remote Sensing for Agronomic Decisions","volume":"100","author":"Hatfield","year":"2008","journal-title":"Agron. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hatfield, J.L., Prueger, J.H., Sauer, T.J., Dold, C., O\u2019Brien, P., and Wacha, K. (2019). Applications of Vegetative Indices from Remote Sensing to Agriculture: Past and Future. Inventions, 4.","DOI":"10.3390\/inventions4040071"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.isprsjprs.2015.10.004","article-title":"Remote Sensing Platforms and Sensors: A Survey","volume":"115","author":"Toth","year":"2016","journal-title":"ISPRS J. Photogramm."},{"key":"ref_5","unstructured":"De Datta, S.K. (1981). Principles and Practices of Rice Production, International Rice Research Institute."},{"key":"ref_6","unstructured":"Williams, J.F. (2010). Rice Nutrient Management in California, University of California Agriculture and Natural Resources Publication."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107714","DOI":"10.1016\/j.agee.2021.107714","article-title":"Quantifying N Leaching Losses as a Function of N Balance: A Path to Sustainable Food Supply Chains","volume":"324","author":"Tamagno","year":"2022","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1111\/gcb.12413","article-title":"Optimizing rice yields while minimizing yield-scaled global warming potential","volume":"20","author":"Pittelkow","year":"2014","journal-title":"Global Change Biol."},{"key":"ref_9","unstructured":"Smith, J., Sutula, M., Bouma-Gregson, K., and Van Dyke, M. (2021). California Water Boards\u2019 Framework and Strategy for Freshwater Harmful Algal Bloom Monitoring: Executive Synthesis, Southern California Coastal Water Research Project Technical Report for California State Water Resources Control Board."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1023\/A:1009744605920","article-title":"On-Farm Adaptation of Knowledge-Intensive Nitrogen Management Technologies for Rice Systems","volume":"53","author":"Balasubramanian","year":"1999","journal-title":"Nutr. Cycl. Agroecosys."},{"key":"ref_12","first-page":"36","article-title":"New Leaf Color Chart for Effective Nitrogen Management in Rice","volume":"89","author":"Witt","year":"2005","journal-title":"Better Crop."},{"key":"ref_13","first-page":"657","article-title":"A Review of Optical Methods for Assessing Nitrogen Contents during Rice Growth","volume":"30","author":"Saberioon","year":"2014","journal-title":"Appl. Eng. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty Five Years of Remote Sensing in Precision Agriculture: Key Advances and Remaining Knowledge Gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and Photographic Infrared Linear Combinations for Monitoring Vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11676-020-01155-1","article-title":"A Commentary Review on the Use of Normalized Difference Vegetation Index (NDVI) in the Era of Popular Remote Sensing","volume":"32","author":"Huang","year":"2021","journal-title":"J. For. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.2134\/agronj2011.0202","article-title":"Estimating Rice Grain Yield Potential Using Normalized Difference Vegetation Index","volume":"103","author":"Harrell","year":"2011","journal-title":"Agron. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2889","DOI":"10.2134\/agronj2019.03.0217","article-title":"Use of Normalized Difference Vegetation Index to Assess N Status and Predict Grain Yield in Rice","volume":"111","author":"Rehman","year":"2019","journal-title":"Agron. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1007\/s13593-012-0094-9","article-title":"Active Canopy Sensor-Based Precision N Management Strategy for Rice","volume":"32","author":"Yao","year":"2012","journal-title":"Agron. Sustain. Dev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.fcr.2010.05.011","article-title":"Estimating the Nitrogen Status of Crops Using a Digital Camera","volume":"118","author":"Li","year":"2010","journal-title":"Field Crop. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.2134\/agronj2006.0103","article-title":"In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index","volume":"98","author":"Teal","year":"2006","journal-title":"Agron. J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xia, T., Miao, Y., Wu, D., Shao, H., Khosla, R., and Mi, G. (2016). Active Optical Sensing of Spring Maize for In-Season Diagnosis of Nitrogen Status Based on Nitrogen Nutrition Index. Remote Sens., 8.","DOI":"10.3390\/rs8070605"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tsouros, D.C., Bibi, S., and Sarigiannidis, P.G. (2019). A Review on UAV-Based Applications for Precision Agriculture. Information, 10.","DOI":"10.3390\/info10110349"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S., and Sun, X. (2021). A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges. Remote Sens., 13.","DOI":"10.3390\/rs13061204"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fu, Y., Yang, G., Song, X., Li, Z., Xu, X., Feng, H., and Zhao, C. (2021). Improved Estimation of Winter Wheat Aboveground Biomass using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13040581"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40538-021-00217-8","article-title":"Drone and Sensor Technology for Sustainable Weed Management: A Review","volume":"8","author":"Esposito","year":"2021","journal-title":"Chem. Biol. Technol. Agric."},{"key":"ref_28","first-page":"48","article-title":"Remote Sensing PI Nitrogen Uptake in Rice","volume":"195","author":"Dunn","year":"2016","journal-title":"IREC Newsl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.fcr.2013.12.018","article-title":"Improving Estimation of Summer Maize Nitrogen Status with Red Edge-Based Spectral Vegetation Indices","volume":"157","author":"Li","year":"2014","journal-title":"Field Crop. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1255\/jnirs.1246","article-title":"Using Field-Derived Hyperspectral Reflectance Measurement to Identify the Essential Wavelengths for Predicting Nitrogen Uptake of Rice at Panicle Initiation","volume":"24","author":"Dunn","year":"2016","journal-title":"J. Near Infrared Spec."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.rse.2013.05.024","article-title":"Multi-Scale Standardized Spectral Mixture Models","volume":"136","author":"Small","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/TGRS.1990.572934","article-title":"Texture Unit, Texture Spectrum, and Texture Analysis","volume":"28","author":"He","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, L., Chen, S., Li, D., Wang, C., Jiang, H., Zheng, Q., and Peng, Z. (2021). Estimation of Paddy Rice Nitrogen Content and Accumulation both at Leaf and Plant Levels from UAV Hyperspectral Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13152956"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zha, H., Miao, Y., Wang, T., Li, Y., Zhang, J., Sun, W., Feng, Z., and Kusnierek, K. (2020). Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning. Remote Sens., 12.","DOI":"10.3390\/rs12020215"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"99","DOI":"10.2747\/1548-1603.48.1.99","article-title":"Small-Scale Unmanned Aerial Vehicles in Environmental Remote Sensing: Challenges and Opportunities","volume":"48","author":"Hardin","year":"2011","journal-title":"GISci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The Application of Small Unmanned Aerial Systems for Precision Agriculture: A Review","volume":"13","author":"Zhang","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s11119-018-9600-7","article-title":"Improved Estimation of Rice Aboveground Biomass Combining Textural and Spectral Analysis of UAV Imagery","volume":"20","author":"Zheng","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"71","DOI":"10.4236\/ars.2018.72006","article-title":"Assessment of UAV Based Vegetation Indices for Nitrogen Concentration Estimation in Spring Wheat","volume":"7","author":"Walsh","year":"2018","journal-title":"Adv. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Becker, T., Nelsen, T.S., Leinfelder-Miles, M., and Lundy, M.E. (2020). Differentiating Between Nitrogen and Water Deficiency in Irrigated Maize Using a UAV-Based Multi-Spectral Camera. Agronomy, 10.","DOI":"10.3390\/agronomy10111671"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"936","DOI":"10.3389\/fpls.2018.00936","article-title":"Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice","volume":"9","author":"Zheng","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2782","DOI":"10.1002\/agj2.20657","article-title":"Ground Versus Aerial Canopy Reflectance of Corn: Red-Edge and Non-Red Edge Vegetation Indices","volume":"113","author":"Sumner","year":"2021","journal-title":"Agron. J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.plantsci.2018.10.022","article-title":"A Rapid Monitoring of NDVI across the Wheat Growth Cycle for Grain Yield Prediction Using a Multi-Spectral UAV Platform","volume":"282","author":"Hassan","year":"2019","journal-title":"Plant Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.fcr.2017.05.025","article-title":"Dynamic Monitoring of NDVI in Wheat Agronomy and Breeding Trials Using an Unmanned Aerial Vehicle","volume":"210","author":"Duan","year":"2017","journal-title":"Field Crop. Res."},{"key":"ref_45","unstructured":"CIMIS (2020, September 01). California Irrigation Management Information System. Internet Resource, Available online: http:\/\/www.cimis.water.ca.gov\/WSNReportCriteria.aspx)."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s10333-005-0026-2","article-title":"The California Rice Cropping System: Agronomic Resource Issues for Long-Term Sustainability","volume":"4","author":"Hill","year":"2006","journal-title":"Paddy Water Environ."},{"key":"ref_47","unstructured":"Sharp, Z. (2017). Principles of Stable Isotope Geochemistry, University of New Mexico Press. [2nd ed.]."},{"key":"ref_48","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium: Section AB. Technical Presentations, Washington, DC, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/1011-1344(93)06963-4","article-title":"Quantitative Estimation of Chlorophyll-A Using Reflectance Spectra: Experiments with Autumn Chestnut and Maple Leaves","volume":"22","author":"Gitelson","year":"1994","journal-title":"J. Photochem. Photobiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-016-0134-6","article-title":"Application of Unmanned Aerial Systems for High Throughput Phenotyping of Large Wheat Breeding Nurseries","volume":"12","author":"Haghighattalab","year":"2016","journal-title":"Plant Methods"},{"key":"ref_51","unstructured":"Nelsen, T., Lundy, M., and Drone Data in Agricultural Research (2019, August 01). GitHub Repository. Available online: https:\/\/github.com\/Grain-Cropping-Systems-Lab\/Drone-Data-in-Agricultural-Research."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.fcr.2018.01.007","article-title":"Do Crop Sensors Promote Improved Nitrogen Management in Grain Crops?","volume":"218","author":"Bramley","year":"2018","journal-title":"Field Crop. Res."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Bijay-Singh, and Ali, A.M. (2020). Using Hand-Held Chlorophyll Meters and Canopy Reflectance Sensors for Fertilizer Nitrogen Management in Cereals in Small Farms in Developing Countries. Sensors, 20.","DOI":"10.3390\/s20041127"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.2134\/agronj2010.0015","article-title":"Derivation of a Variable Rate Nitrogen Application Model for In-Season Fertilization of Corn","volume":"102","author":"Holland","year":"2010","journal-title":"Agron. J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4705","DOI":"10.1002\/agj2.20397","article-title":"Canopy Reflectance Informs In-Season Malting Barley Nitrogen Management: An Ex-Ante Classification Approach","volume":"112","author":"Nelsen","year":"2020","journal-title":"Agron. J."},{"key":"ref_56","first-page":"1","article-title":"Evaluating Different Approaches to Non-Destructive Nitrogen Status Diagnosis of Rice Using Portable Rapidscan Active Canopy Sensor","volume":"7","author":"Lu","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chen, Z., Miao, Y., Lu, J., Zhou, L., Li, Y., Zhang, H., Lou, W., Zhang, Z., Kusnierek, K., and Liu, C. (2019). In-Season Diagnosis of Winter Wheat Nitrogen Status in Smallholder Farmer Fields Across a Village Using Unmanned Aerial Vehicle-Based Remote Sensing. Agronomy, 9.","DOI":"10.3390\/agronomy9100619"},{"key":"ref_58","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis, Springer. Available online: https:\/\/ggplot2.tidyverse.org.","DOI":"10.1007\/978-3-319-24277-4_9"},{"key":"ref_60","unstructured":"Mangiafico, S.S. (2022, April 01). Summary and Analysis of Extension Program Evaluation in R. Available online: https:\/\/rcompanion.org\/handbook."},{"key":"ref_61","unstructured":"Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., and R Core Team (2017, December 15). nlme: Linear and Nonlinear Mixed Effects Models. Available online: https:\/\/CRAN.R-project.org\/package=nlme."},{"key":"ref_62","first-page":"1","article-title":"rcompanion: Functions to Support Extension Education Program Evaluation","volume":"20","author":"Mangiafico","year":"2022","journal-title":"Cran Repos."},{"key":"ref_63","unstructured":"Cox, D.R., and Snell, E.J. (2018). Analysis of Binary Data, Chapman & Hall."},{"key":"ref_64","unstructured":"Barto\u0144, K. (2020, August 01). MuMIn: Multi-Model Inference. Available online: https:\/\/CRAN.R-project.org\/package=MuMIn."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.fcr.2016.04.003","article-title":"Estimating Yield Potential in Temperate High-Yielding, Direct-Seeded US Rice Production Systems","volume":"193","author":"Espe","year":"2016","journal-title":"Field Crop. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"906","DOI":"10.2134\/agronj2008.0230x","article-title":"Assessing the Necessity of Surface Applied Preplant Nitrogen Fertilizer in Rice Systems","volume":"101","author":"Linquist","year":"2009","journal-title":"Agron. J."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0378-4290(95)00101-8","article-title":"Nitrogen-Use Efficiency in Tropical Lowland Rice Systems: Contributions from Indigenous and Applied Nitrogen","volume":"47","author":"Cassman","year":"1996","journal-title":"Field Crop. Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"178","DOI":"10.2134\/agronj1998.00021962009000020010x","article-title":"Upper Thresholds of Nitrogen Uptake Rates and Associated Nitrogen Fertilizer Efficiencies in Irrigated Rice","volume":"90","author":"Peng","year":"1998","journal-title":"Agron. J."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Hatfield, J.L., and Prueger, J.H. (2010). Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages Under Varying Management Practices. Remote Sens., 2.","DOI":"10.3390\/rs2020562"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.2134\/agronj14.0494","article-title":"Algorithm for Variable-Rate Nitrogen Application in Sugarcane Based on Active Crop Canopy Sensor","volume":"107","author":"Amaral","year":"2015","journal-title":"Agron. J."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1078\/0176-1617-01176","article-title":"Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation","volume":"161","author":"Gitelson","year":"2004","journal-title":"J. Plant Physiol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/S0034-4257(99)00067-X","article-title":"Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics","volume":"71","author":"Thenkabail","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.fcr.2017.11.006","article-title":"Characterizing Soybean Vigor and Productivity Using Multiple Crop Canopy Sensor Readings","volume":"216","author":"Miller","year":"2018","journal-title":"Field Crop. Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.compag.2004.06.003","article-title":"Determining Temporal Windows for Crop Discrimination with Remote Sensing: A Case Study in South-Eastern Australia","volume":"45","author":"McVicar","year":"2004","journal-title":"Comput. Electron. Agric."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.2134\/agronj2012.0065","article-title":"2012. Green Leaf Area Index Estimation in Maize and Soybean: Combining Vegetation Indices to Achieve Maximal Sensitivity","volume":"104","author":"Gitelson","year":"2012","journal-title":"Agron. J."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s11119-016-9433-1","article-title":"Evaluation of Red and Red-Edge Reflectance-Based Vegetation Indices for Rice Biomass and Grain Yield Prediction Models in Paddy Fields","volume":"17","author":"Kanke","year":"2016","journal-title":"Precis. Agric."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.fcr.2013.08.005","article-title":"Non-Destructive Estimation of Rice Plant Nitrogen Status with Crop Circle Multispectral Active Canopy Sensor","volume":"154","author":"Cao","year":"2013","journal-title":"Field Crop. Res."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.fcr.2013.09.023","article-title":"2014. Hyperspectral Canopy Sensing of Paddy Rice Aboveground Biomass at Different Growth Stages","volume":"155","author":"Gnyp","year":"2014","journal-title":"Field Crop. Res."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1023\/A:1025592720538","article-title":"Efficient and Flexible Management of Nitrogen for Rainfed Lowland Rice","volume":"67","author":"Linquist","year":"2003","journal-title":"Nutr. Cycl. Agroecosys."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.agee.2016.05.020","article-title":"Alternate Wetting and Drying in High Yielding Direct-Seeded Rice Systems Accomplishes Multiple Environmental and Agronomic Objectives","volume":"229","author":"LaHue","year":"2016","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.fcr.2021.108312","article-title":"Single Midseason Drainage Events Decrease Global Warming Potential Without Sacrificing Grain Yield in Flooded Rice Systems","volume":"276","author":"Perry","year":"2022","journal-title":"Field Crop. Res."},{"key":"ref_82","first-page":"733","article-title":"Prediction of Grain Yield and Nitrogen Uptake by Basmati Rice through In-Season Proximal Sensing with a Canopy Reflectance Sensor","volume":"23","author":"Kaur","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1071\/CP13412","article-title":"Nitrogen Timing and Rate Effects on Growth and Grain Yield of Delayed Permanent-Water Rice in South-Eastern Australia","volume":"65","author":"Dunn","year":"2014","journal-title":"Crop Pasture Sci."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1071\/CP16331","article-title":"Nitrogen Rate and Timing Effects on Growth and Yield of Drill-Sown Rice","volume":"67","author":"Dunn","year":"2016","journal-title":"Crop Pasture Sci."},{"key":"ref_85","unstructured":"Miscellaneous Publication 192, and Hardke, J. (2021). Soil Fertility. Rice Production Handbook, Arkansas Cooperative Extension Service."},{"key":"ref_86","unstructured":"Troldahl, D. (2018). Rice Growing Guide, New South Wales Government Department of Primary Industries."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2017.05.003","article-title":"Predicting Grain Yield in Rice Using Multi-Temporal Vegetation Indices from UAV-Based Multispectral and Digital Imagery","volume":"130","author":"Zhou","year":"2017","journal-title":"ISPRS J. Photogramm."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2770\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:26:52Z","timestamp":1760138812000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,9]]},"references-count":87,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122770"],"URL":"https:\/\/doi.org\/10.3390\/rs14122770","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,9]]}}}