An examination of thematic research, development, and trends in remote sensing applied to conservation agriculture

Zobaer Ahmed1,2, Aaron Shew3, Lawton Nalley3, Michael Popp3, V. Steven Green4,5, Kristofor Brye6
1Center for Advanced Spatial Technologies, University of Arkansas, Fayetteville, AR, USA
2Environmental Dynamics Program, University of Arkansas, Fayetteville, AR, USA
3Department of Agricultural Economics and Agribusiness, University of Arkansas, Fayetteville, AR, USA
4College of Agriculture, Arkansas State University, Jonesboro, AR, USA
5University of Arkansas System Division of Agriculture, Little Rock, AR, USA
6Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, USA

Tài liệu tham khảo

Aboutalebi, 2019, Estimation of soil moisture at different soil levels using machine learning techniques and unmanned aerial vehicle (UAV) multispectral imagery, 26 Adão, 2017, Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry, Remote Sensing, 9, 10.3390/rs9111110 Adu, 2018, Systematic review of the effects of agricultural interventions on food security in northern Ghana, PLoS One, 13, 10.1371/journal.pone.0203605 Ahmad, 2020, A systematic review of soil erosion control practices on the agricultural land in Asia, International Soil and Water Conservation Research, 8, 103, 10.1016/j.iswcr.2020.04.001 Al-Ali, 2020, A comparative study of remote sensing classification methods for monitoring and assessing desert vegetation using a UAV-based multispectral sensor, Environmental Monitoring and Assessment, 192, 10.1007/s10661-020-08330-1 Al-Juboury, 2021, Integration satellite imagery with fuzzy logic for potential change detection in land use/land cover Ali, 2015, Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data, Remote Sensing, 10.3390/rs71215841 Aoki, 2021, Temporal integration of remote-sensing land cover maps to identify crop rotation patterns in a semiarid region of Argentina, Agronomy Journal, 113, 3232, 10.1002/agj2.20758 Babaeian, 2019, Ground, proximal, and satellite remote sensing of soil moisture, Reviews of Geophysics, 10.1029/2018RG000618 Barnes, 2021, Detecting winter cover crops and crop residues in the midwest us using machine learning classification of thermal and optical imagery, Remote Sensing, 13, 10.3390/rs13101998 Baumgart-Getz, 2012, Why farmers adopt best management practice in the United States: A meta-analysis of the adoption literature, Journal of Environmental Management, 96, 17, 10.1016/j.jenvman.2011.10.006 Beeson, 2016, Multispectral satellite mapping of crop residue cover and tillage intensity in Iowa, Journal of Soil and Water Conservation, 71, 385, 10.2489/jswc.71.5.385 Beeson, 2020, Estimates of conservation tillage practices using landsat archive, Remote Sensing, 12, 10.3390/rs12162665 Belgiu, 2018, Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis, Remote Sensing of Environment, 204, 509, 10.1016/j.rse.2017.10.005 Brisco, 1991, Tillage effects on the radar backscattering coefficient of grain stubble fields, International Journal of Remote Sensing, 12, 2283, 10.1080/01431169108955258 Brooker, 2021, Evaluating high-resolution optical and thermal reflectance of maize interseeded with cover crops across spatial scales using remotely sensed imagery, Agronomy Journal, 113, 2884, 10.1002/agj2.20592 Brye, 2005 Bullock, 1992, 11, 309 Burns, 2022, Determining nitrogen deficiencies for maize using various remote sensing indices, Precision Agriculture, 23, 791, 10.1007/s11119-021-09861-4 Camps-Valls, 2021, A unified vegetation index for quantifying the terrestrial biosphere, Science Advances, 7, 7447, 10.1126/sciadv.abc7447 Candiago, 2015, Evaluating multispectral images and vegetation indices for precision farming applications from UAV images, Remote Sensing, 7, 4026, 10.3390/rs70404026 Chamberlain, 2020, Crop rotation, but not cover crops, influenced soil bacterial community composition in a corn-soybean system in southern Wisconsin, Applied Soil Ecology, 154, 10.1016/j.apsoil.2020.103603 Chao, 2008, Band-reconfigurable multi-UAV-based cooperative remote sensing for real-time water management and di Chen, 2010, Land use and land cover change detection using satellite remote sensing techniques in the mountainous Three Gorges Area, China, International Journal of Remote Sensing, 31, 1519, 10.1080/01431160903475381 Chew, 2020, Deep neural networks and transfer learning for food crop identification in UAV images, Drones, 4, 1, 10.3390/drones4010007 Chi, 2014, Spectral unmixing-based crop residue estimation using hyperspectral remote sensing data: A case study at purdue university, Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 2531, 10.1109/JSTARS.2014.2319585 Chughtai, 2021, A review on change detection method and accuracy assessment for land use land cover, Remote Sensing Applications, 22 Claassen, 2018 Conrad, 2016, Analysing irrigated crop rotation patterns in arid Uzbekistan by the means of remote sensing: A case study on post-soviet agricultural land use, Journal of Arid Environments, 124, 150, 10.1016/j.jaridenv.2015.08.008 Cracknell, 2018, The development of remote sensing in the last 40 years, International Journal of Remote Sensing, 39, 8387, 10.1080/01431161.2018.1550919 Crowley, 2020, Remote sensing's recent and future contributions to landscape ecology, Current Landscape Ecology Reports, 5, 45, 10.1007/s40823-020-00054-9 Cruz-Ramírez, 2012, A multi-objective neural network based method for cover crop identification from remote sensed data, Expert Systems with Applications, 39, 10038, 10.1016/j.eswa.2012.02.046 Daughtry, 2008, Mitigating the effects of soil and residue water contents on remotely sensed estimates of crop residue cover, Remote Sensing of Environment, 112, 1647, 10.1016/j.rse.2007.08.006 Daughtry, 2005, Remote sensing the spatial distribution of crop residues, Agronomy Journal, 97, 864, 10.2134/agronj2003.0291 Daughtry, 2003, Remote sensing of crop residue cover and soil tillage intensity Daughtry, 1996, Measuring crop residue cover using remote sensing techniques, Theoretical and Applied Climatology, 54, 17, 10.1007/BF00863555 Davis, 2012, Increasing cropping system diversity balances productivity, profitability and environmental health, PLoS One, 7, 10.1371/journal.pone.0047149 Delavarpour, 2021, A technical study on UAV characteristics for precision agriculture applications and associated practical challenges, Remote Sensing, 10.3390/rs13061204 Dian Bah, 2018, Deep learning with unsupervised data labeling for weed detection in line crops in UAV images, Remote Sensing, 10 Ding, 2021, Evaluation of three different machine learning methods for object-based artificial terrace mapping—a case study of the loess plateau, China, Remote Sensing, 13, 1, 10.3390/rs13051021 Dvorakova, 2020, Soil organic carbon mapping from remote sensing: The effect of crop residues, Remote Sensing, 12, 10.3390/rs12121913 Estrella, 2021, Quantifying vegetation response to environmental changes on the galapagos islands, Ecuador using the normalized difference vegetation index (NDVI), Environ Res Commun, 3 Feng, 2020, Yield estimation in cotton using UAV-based multi-sensor imagery, Biosystems Engineering, 193, 101, 10.1016/j.biosystemseng.2020.02.014 Fisher, 2018, Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality, Remote Sens Ecol Conserv, 4, 137, 10.1002/rse2.61 Fonji, 2014, Using satellite data to monitor land-use land-cover change in North-eastern Latvia, SpringerPlus, 3, 1, 10.1186/2193-1801-3-61 Franklin, 2001 Galloza, 2013, Crop residue modeling and mapping using landsat, ALI, hyperion and airborne remote sensing data, Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 446, 10.1109/JSTARS.2012.2222355 Gamba, 2010, An efficient neural classification chain of SAR and optical urban images, International Journal of Remote Sensing, 22, 1535, 10.1080/01431160118746 Gao, 2020, Detecting cover crop end-of-season using venμs and sentinel-2 satellite imagery, Remote Sensing, 12, 1, 10.3390/rs12213524 García-Berná, 2020 Gelder, 2009, Estimating mean field residue cover on midwestern soils using satellite imagery, Agronomy Journal, 101, 635, 10.2134/agronj2007.0249 Ge, 2011, Remote sensing of soil properties in precision agriculture: A review, Frontiers of Earth Science, 10.1007/s11707-011-0175-0 Ge, 2019, Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring, PeerJ, 7, 10.7717/peerj.6926 Giovannucci, 2012 Govender, 2008, A comparison of satellite hyperspectral and multispectral remote sensing imagery for improved classification and mapping of vegetation, WaterSA, 34, 147 Gowda, 2008, Remote sensing of contrasting tillage practices in the Texas Panhandle, International Journal of Remote Sensing, 29, 3477, 10.1080/01431160701581810 Guo, 2007, An object-based classification approach in mapping tree mortality using high spatial resolution imagery, GIsci Remote Sens, 44, 24, 10.2747/1548-1603.44.1.24 Hagen, 2020, Mapping conservation management practices and outcomes in the corn belt using the operational tillage information system (Optis) and the denitrification–decomposition (DNDC) model, Land, 9, 1, 10.3390/land9110408 Hartwig, 2002, Cover crops and living mulches, Weed Science, 50, 688, 10.1614/0043-1745(2002)050[0688:AIACCA]2.0.CO;2 Hasituya, 2017, Mapping Plastic-Mulched Farmland with C-band full polarization SAR remote sensing data, Remote Sensing, 9 Hasituya, 2017, Selecting appropriate spatial scale for mapping plastic-mulched farmland with satellite remote sensing imagery, Remote Sensing, 9 Hasituya, 2020, Mapping plastic-mulched farmland by coupling optical and synthetic aperture radar remote sensing, International Journal of Remote Sensing, 41, 7757, 10.1080/01431161.2020.1763510 Hassan-Esfahani, 2015, Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks, Remote Sensing, 7, 2627, 10.3390/rs70302627 Hively, 2015, Remote sensing to monitor cover crop adoption in southeastern Pennsylvania, Journal of Soil and Water Conservation, 70, 340, 10.2489/jswc.70.6.340 Hively, 2018, Mapping crop residue and tillage intensity using WorldView-3 satellite shortwave infrared residue indices, Remote Sensing, 10, 10.3390/rs10101657 Hively, 2009, Using satellite remote sensing to estimate winter cover crop nutrient uptake efficiency, Journal of Soil and Water Conservation, 64, 303, 10.2489/jswc.64.5.303 Hively, 2020, Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling, Journal of Soil and Water Conservation, 75, 362, 10.2489/jswc.75.3.362 Hively, 2019, Mapping crop residue by combining landsat and worldview-3 satellite imagery, Remote Sensing, 11, 10.3390/rs11161857 Hobbs, 2008 Huang, 2021, Characterization of Planetscope-0 Planetscope-1 surface reflectance and normalized difference vegetation index continuity, Science of Remote Sensing, 3, 10.1016/j.srs.2021.100014 Huggins, 2008 Hung, 2014, Feature learning based approach for weed classification using high resolution aerial images from a digital camera mounted on a UAV, Remote Sensing, 6, 12037, 10.3390/rs61212037 Hunt, 2018, What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?, International Journal of Remote Sensing, 39, 5345, 10.1080/01431161.2017.1410300 Hunt, 2011, NIR-green-blue high-resolution digital images for assessment of winter cover crop biomass, GIsci Remote Sens, 48, 86, 10.2747/1548-1603.48.1.86 Jayanth, 2021, Classification of crops and crop rotation using remote sensing and GIS-based approach: A case study of doddakawalande hobli, nanjangudu taluk, Journal of the Indian Society of Remote Sensing Kasischke, 1997, The use of imaging radars for ecological applications - a review, Remote Sensing of Environment, 59, 141, 10.1016/S0034-4257(96)00148-4 Kc, 2021, Assessment of the spatial and temporal patterns of cover crops using remote sensing, Remote Sensing, 13, 10.3390/rs13142689 Khanal, 2017, An overview of current and potential applications of thermal remote sensing in precision agriculture, Computers and Electronics in Agriculture, 10.1016/j.compag.2017.05.001 Koger, 2004, Detection of pitted morningglory (Ipomoea lacunosa) by hyperspectral remote sensing. 1. Effects of tillage and cover crop residue, Source: Weed Science, 52, 222 Koutsos, 2019 Laamrani, 2020, Assessing soil cover levels during the non-growing season using multitemporal satellite imagery and spectral unmixing techniques, Remote Sensing, 12, 10.3390/rs12091397 Lan, 2021, Real-time identification of rice weeds by uav low-altitude remote sensing based on improved semantic segmentation model, Remote Sensing, 13, 10.3390/rs13214370 Lausch, 2019, Linking remote sensing and geodiversity and their traits relevant to biodiversity-Part I: Soil characteristics, Remote Sensing, 10.3390/rs11202356 Leek, 1995, Using remote sensing for monitoring of autumn tillage in Norway, International Journal of Remote Sensing, 16, 447, 10.1080/01431169508954412 Lehman, 2015, Understanding and enhancing soil biological health: The solution for reversing soil degradation, Sustainability, 7, 988, 10.3390/su7010988 Liberati, 2009, The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration, Journal of Clinical Epidemiology, e1, 10.1016/j.jclinepi.2009.06.006 Liu, 2010, Assessing object-based classification: Advantages and limitations, Remote Sensing Letters, 10.1080/01431161003743173 Liu, 2020, A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication, Remote Sensing of Environment, 251, 10.1016/j.rse.2020.112095 Liu, 2018, A phenology-based method to map cropping patterns under a wheat-maize rotation using remotely sensed time-series data, Remote Sensing, 10 Li, 2021, Phenology-based classification of crop species and rotation types using fused MODIS and Landsat data: The comparison of a random-forest-based model and a decision-rule-based model, Soil and Tillage Research, 206, 10.1016/j.still.2020.104838 Lizotte, 2021, 13 Luo, 2019, UAV based soil moisture remote sensing in a karst mountainous catchment, Catena, 174, 478, 10.1016/j.catena.2018.11.017 Lu, 2007, A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, 10.1080/01431160600746456 Maas, 2008, Estimating ground cover of field crops using medium-resolution multispectral satellite imagery, Agronomy Journal, 100, 320, 10.2134/agronj2007.0140 Maes, 2019, Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture, Trends in Plant Science, 10.1016/j.tplants.2018.11.007 Magdoff, 2009 Mahdianpari, 2020, Meta-analysis of wetland classification using remote sensing: A systematic review of a 40-year trend in north America, Remote Sensing, 12, 1882, 10.3390/rs12111882 Manjunath, 2015, Mapping of rice-cropping pattern and cultural type using remote-sensing and ancillary data: A case study for South and southeast asian countries, International Journal of Remote Sensing, 36, 6008, 10.1080/01431161.2015.1110259 Martins, 2021, Digital mapping of structural conservation practices in the Midwest U.S. croplands: Implementation and preliminary analysis, Science of the Total Environment, 772, 10.1016/j.scitotenv.2021.145191 Moher, 2009, Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement, Journal of Clinical Epidemiology, 62, 1006, 10.1016/j.jclinepi.2009.06.005 Möller, 2007, The comparison index: A tool for assessing the accuracy of image segmentation, International Journal of Applied Earth Observation and Geoinformation, 9, 311, 10.1016/j.jag.2006.10.002 Muñoz, 2010, Nonlinear hierarchical models for predicting cover crop biomass using Normalized Difference Vegetation Index, Remote Sensing of Environment, 114, 2833, 10.1016/j.rse.2010.06.011 Najafi, 2021, A comparative approach of fuzzy object based image analysis and machine learning techniques which are applied to crop residue cover mapping by using sentinel-2 satellite and uav imagery, Remote Sensing, 13, 1, 10.3390/rs13050937 Najafi, 2018, Object-based satellite image analysis applied for crop residue estimating using Landsat OLI imagery, International Journal of Remote Sensing, 39, 6117, 10.1080/01431161.2018.1454621 Navarro, 2020, A systematic review of iot solutions for smart farming, Sensors, 10.3390/s20154231 Nevavuori, 2020, Crop yield prediction using multitemporal UAV data and spatio-temporal deep learning models, Remote Sensing, 12, 1, 10.3390/rs12234000 Nevavuori, 2019, Crop yield prediction with deep convolutional neural networks, Computers and Electronics in Agriculture, 163, 10.1016/j.compag.2019.104859 Nowak, 2021, Estimation of winter soil cover by vegetation before spring-sown crops for mainland France using multispectral satellite imagery, Environmental Research Letters, 16, 10.1088/1748-9326/ac007c Obade, 2020, Mapping tillage practices using spatial information techniques, Environmental Management, 66, 722, 10.1007/s00267-020-01335-z Pacheco, 2008, Deriving percent crop cover over agriculture canopies using hyperspectral remote sensing, Canadian Journal of Remote Sensing, 10.5589/m07-064 Pacheco, 2010, Evaluating multispectral remote sensing and spectral unmixing analysis for crop residue mapping, Remote Sensing of Environment, 114, 2219, 10.1016/j.rse.2010.04.024 Page, 2020, The ability of conservation agriculture to conserve soil organic carbon and the subsequent impact on soil physical, chemical, and biological properties and yield, Frontiers in Sustainable Food Systems, 10.3389/fsufs.2020.00031 Page, 2021, The PRISMA 2020 statement: An updated guideline for reporting systematic reviews, BMJ, 372 Panigrahy, 1997, Mapping of crop rotation using multidate Indian Remote Sensing Satellite digital data, ISPRS Journal of Photogrammetry and Remote Sensing, 10.1016/S0924-2716(97)83003-1 Pearlman, 2001, Overview of the Hyperion imaging spectrometer for the NASA EO-1 mission, International Geoscience and Remote Sensing Symposium (IGARSS), 7, 3036 Pittelkow, 2015, Productivity limits and potentials of the principles of conservation agriculture, Nature, 517, 365, 10.1038/nature13809 Prabhakara, 2015, Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States, International Journal of Applied Earth Observation and Geoinformation, 39, 88, 10.1016/j.jag.2015.03.002 Prokopy, 2019, Adoption of agricultural conservation practices in the United States: Evidence from 35 years of quantitative literature, Journal of Soil and Water Conservation, 74, 520, 10.2489/jswc.74.5.520 Radočaj, 2020 Rogan, 2004, Remote sensing technology for mapping and monitoring land-cover and land-use change, Progress in Planning, 61, 301, 10.1016/S0305-9006(03)00066-7 Schreier, 2021, Crop-specific phenomapping by fusing Landsat and Sentinel data with MODIS time series, Eur J Remote Sens, 54, 47, 10.1080/22797254.2020.1831969 Seifert, 2019, Corrigendum: Satellite detection of cover crops and their effects on crop yield in the Midwestern United States, Environmental Research Letters, 14, 10.1088/1748-9326/aaf933 Serbin, 2009, Effect of soil spectral properties on remote sensing of crop residue cover, Soil & Water Management & Conservation, 73, 1545 Serbin, 2009, Effects of soil composition and mineralogy on remote sensing of crop residue cover, Remote Sensing of Environment, 113, 224, 10.1016/j.rse.2008.09.004 Serbin, 2009, An improved ASTER index for remote sensing of crop residue, Remote Sensing, 1, 971, 10.3390/rs1040971 Sharpley, 2015, Arkansas discovery farms: Documenting water quality benefits of on-farm conservation management and empowering farmers, Acta Agric Scand B Soil Plant Sci, 65, 186 Shi, 2019, Decision support system for variable rate irrigation based on UAV multispectral remote sensing, Sensors, 19, 10.3390/s19132880 Smith, 1995, Multi-temporal, multi-sensor remote sensing for monitoring soil conservation farming, Canadian Journal of Remote Sensing, 21, 177, 10.1080/07038992.1995.10874611 Song, 2011, A competitive pixel-object approach for land cover classification, International Journal of Remote Sensing, 26, 4981, 10.1080/01431160500213912 Sonmez, 2016, Measuring intensity of tillage and plant residue cover using remote sensing, Eur J Remote Sens, 49, 121, 10.5721/EuJRS20164907 Sood, 2009, Impact of cropping pattern changes on the exploitation of water resources: A remote sensing and gis approach, J. Indian Soc. Remote Sens., 10.1007/s12524-009-0033-7 South, 2004, Optimal classification methods for mapping agricultural tillage practices, Remote Sensing of Environment, 91, 90, 10.1016/j.rse.2004.03.001 Stroppiana, 2015, Rice yield estimation using multispectral data from UAV: A preliminary experiment in northern Italy, 4664 Sudheer, 2010, Artificial Neural Network approach for mapping contrasting tillage practices, Remote Sensing, 2, 579, 10.3390/rs2020579 Tao, 2020, Estimation of crop growth parameters using UAV- based hyperspectral remote sensing data, Sensors, 20, 10.3390/s20051296 Teasdale, 1996, Contribution of cover crops to weed management in sustainable agricultural systems, Journal of Production Agriculture, 9, 475, 10.2134/jpa1996.0475 Thaler, 2021, The extent of soil loss across the US Corn Belt, Proceedings of the National Academy of Sciences of the United States of America, 118, 1 Thieme, 2020, Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed, Remote Sensing of Environment, 248, 10.1016/j.rse.2020.111943 Uri, 2001, The environmental implications of soil erosion in the United States, Environmental Monitoring and Assessment, 66, 293, 10.1023/A:1006333329653 Ustuner, 2014, Crop type classification using vegetation indices of rapideye imagery, 195 Van Deventer, 1997, Using thematic mapper data to identify contrasting soil plains and tillage practices, Photogrammetric Engineering & Remote Sensing, 63, 87 Viña, 2003 Waldhoff, 2017, Multi-data approach for remote sensing-based regional crop rotation mapping: A case study for the rur catchment, Germany, International Journal of Applied Earth Observation and Geoinformation, 61, 55, 10.1016/j.jag.2017.04.009 Wallace, 2004, Recent developments in analysis of spatial and temporal data for landscape qualities and monitoring, Austral Ecology, 29, 100, 10.1111/j.1442-9993.2004.01356.x Watts, 2011, Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery, Remote Sensing of Environment, 115, 66, 10.1016/j.rse.2010.08.005 Weiss, 2020, Remote sensing for agricultural applications: A meta-review, Remote Sensing of Environment, 236, 10.1016/j.rse.2019.111402 Wulder, 2004, High spatial resolution remotely sensed data for ecosystem characterization, BioScience, 10.1641/0006-3568(2004)054[0511:HSRRSD]2.0.CO;2 Wulder, 2019, Current status of Landsat program, science, and applications, Remote Sensing of Environment, 225, 127, 10.1016/j.rse.2019.02.015 Xiong, 2019, Large scale agricultural plastic mulch detecting and monitoring with multi-source remote sensing data: A case study in Xinjiang, China, Remote Sensing, 11, 10.3390/rs11182088 Xu, 2018, The feasibility of satellite remote sensing and spatial interpolation to estimate cover crop biomass and nitrogen uptake in a small watershed, Journal of Soil and Water Conservation, 73, 682, 10.2489/jswc.73.6.682 Xun, 2021, Mapping cotton cultivated area combining remote sensing with a fused representation-based classification algorithm, Computers and Electronics in Agriculture, 181, 10.1016/j.compag.2020.105940 Yang, 2021, Integration of crop growth model and random forest for winter wheat yield estimation from UAV hyperspectral imagery, Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 6253, 10.1109/JSTARS.2021.3089203 Yang, 2017, Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives, Frontiers of Plant Science Yang, 2020, A near real-time deep learning approach for detecting rice phenology based on UAV images, Agricultural and Forest Meteorology, 287, 10.1016/j.agrformet.2020.107938 Yan, 2021, Large-scale crop mapping from multi-source optical satellite imageries using machine learning with discrete grids, International Journal of Applied Earth Observation and Geoinformation, 103, 10.1016/j.jag.2021.102485 Yue, 2020, Estimating fractional cover of crop, crop residue, and soil in cropland using broadband remote sensing data and machine learning, International Journal of Applied Earth Observation and Geoinformation, 89, 10.1016/j.jag.2020.102089 Zhang, 2020, Assessing the effect of real spatial resolution of in situ UAV multispectral images on seedling rapeseed growth monitoring, Remote Sensing, 12, 1 Zhao, 2012, Effects of crop residue cover resulting from tillage practices on LAI estimation of wheat canopies using remote sensing, International Journal of Applied Earth Observation and Geoinformation, 14, 169, 10.1016/j.jag.2011.09.003 Zhao, 2021, Determination of key phenological phases ofwinter wheat based on the time-weighted dynamic time warping algorithm and MODIS time-series data, Remote Sensing, 13 Zheng, 2012, Remote sensing of crop residue cover using multi-temporal Landsat imagery, Remote Sensing of Environment, 117, 177, 10.1016/j.rse.2011.09.016 Zheng, 2013, Multitemporal remote sensing of crop residue cover and tillage practices: A validation of the minNDTI strategy in the United States, Journal of Soil and Water Conservation, 68, 120, 10.2489/jswc.68.2.120 Zheng, 2014, Remote sensing of crop residue and tillage practices: Present capabilities and future prospects, Soil and Tillage Research, 138, 26, 10.1016/j.still.2013.12.009 Zheng, 2013, Broad-scale monitoring of tillage practices using sequential landsat imagery, Soil Science Society of America Journal, 77, 1755, 10.2136/sssaj2013.03.0108 Zhou, 2018, Assessing the impact of spatial resolution on the estimation of leaf nitrogen concentration over the full season of paddy rice using near-surface imaging spectroscopy data, Frontiers of Plant Science, 9, 10.3389/fpls.2018.00964