{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:56:11Z","timestamp":1777510571841,"version":"3.51.4"},"reference-count":79,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T00:00:00Z","timestamp":1587340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["Finance Code 001"],"award-info":[{"award-number":["Finance Code 001"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["48636\/2016-0"],"award-info":[{"award-number":["48636\/2016-0"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>One of the obstacles in monitoring agricultural crops is the difficulty in understanding and mapping rapid changes of these crops. With the purpose of addressing this issue, this study aimed to model and fuse the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) using Landsat-like images to achieve daily high spatial resolution NDVI. The study was performed for the period of 2017 on a commercial farm of irrigated maize-soybean rotation in the western region of the state of Bahia, Brazil. To achieve the objective, the following procedures were performed: (i) Landsat-like images were upscaled to match the Landsat-8 spatial resolution (30 m); (ii) the reflectance of Landsat-like images was intercalibrated using the Landsat-8 as a reference; (iii) Landsat-like reflectance images were upscaled to match the MODIS sensor spatial resolution (250 m); (iv) regression models were trained daily to model MODIS NDVI using the upscaled Landsat-like reflectance images (250 m) of the closest day as the input; and (v) the intercalibrated version of the Landsat-like images (30 m) used in the previous step was used as the input for the trained model, resulting in a downscaled MODIS NDVI (30 m). To determine the best fitting model, we used the following statistical metrics: coefficient of determination (r2), root mean square error (RMSE), Nash\u2013Sutcliffe efficiency index (NSE), mean bias error (MBE), and mean absolute error (MAE). Among the assessed regression models, the Cubist algorithm was sensitive to changes in agriculture and performed best in modeling of the Landsat-like MODIS NDVI. The results obtained in the present research are promising and can enable the monitoring of dynamic phenomena with images available free of charge, changing the way in which decisions are made using satellite images.<\/jats:p>","DOI":"10.3390\/rs12081297","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T04:49:38Z","timestamp":1587444578000},"page":"1297","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0186-8907","authenticated-orcid":false,"given":"Roberto","family":"Filgueiras","sequence":"first","affiliation":[{"name":"Department of Agricultural Engineering, Federal University of Vi\u00e7osa (UFV), Vi\u00e7osa 36570-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8795-8793","authenticated-orcid":false,"given":"Everardo Chartuni","family":"Mantovani","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Federal University of Vi\u00e7osa (UFV), Vi\u00e7osa 36570-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9484-1411","authenticated-orcid":false,"given":"Elp\u00eddio In\u00e1cio","family":"Fernandes-Filho","sequence":"additional","affiliation":[{"name":"Department of Soil and Plant Nutrition, Federal University of Vi\u00e7osa (UFV), Vi\u00e7osa 36570-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1671-1021","authenticated-orcid":false,"given":"Fernando Fran\u00e7a da","family":"Cunha","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Federal University of Vi\u00e7osa (UFV), Vi\u00e7osa 36570-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5390-575X","authenticated-orcid":false,"given":"Daniel","family":"Althoff","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Federal University of Vi\u00e7osa (UFV), Vi\u00e7osa 36570-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7800-5416","authenticated-orcid":false,"given":"Santos Henrique Brant","family":"Dias","sequence":"additional","affiliation":[{"name":"Department of Soils and Agricultural Engineering, State University of Ponta Grossa (UEPG), Ponta Grossa 84030-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.ecolind.2016.11.003","article-title":"Optical trait indicators for remote sensing of plant species composition: Predictive power and seasonal variability","volume":"73","author":"Feilhauer","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3489","DOI":"10.3390\/rs70403489","article-title":"Downscaling 250-m MODIS Growing Season NDVI Based on Multiple-Date Landsat Images and Data Mining Approaches","volume":"7","author":"Gu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1016\/j.rse.2009.05.011","article-title":"Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M., Kuemmerle, T., Meyfroidt, P., and Mitchard, E. (2016). A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring. Remote Sens., 8.","DOI":"10.3390\/rs8010070"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2016.11.001","article-title":"Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields","volume":"123","author":"Xu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lv, Z., Shi, W., Zhou, X., and Benediktsson, J. (2017). Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images. Remote Sens., 9.","DOI":"10.3390\/rs9111112"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.scitotenv.2016.09.032","article-title":"The water productivity score (WPS) at global and regional level: Methodology and first results from remote sensing measurements of wheat, rice and maize","volume":"575","author":"Bastiaanssen","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_9","first-page":"1884","article-title":"Variabilidade espa\u00e7o-temporal da condi\u00e7\u00e3o da vegeta\u00e7\u00e3o na agricultura irrigada por meio de imagens sentinel-2a","volume":"11","author":"Ribeiro","year":"2017","journal-title":"Rev. Bras. Agric. Irrig."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2017.02.004","article-title":"A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration","volume":"126","author":"Mahour","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1109\/JSTARS.2016.2519099","article-title":"Evaluation of Disaggregation Methods for Downscaling MODIS Land Surface Temperature to Landsat Spatial Resolution in Barrax Test Site","volume":"9","author":"Bisquert","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6457","DOI":"10.1080\/01431161.2010.512929","article-title":"Down-scaling of SEBAL derived evapotranspiration maps from MODIS (250 m) to Landsat (30 m) scales","volume":"32","author":"Hong","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1080\/17538947.2011.623189","article-title":"Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation","volume":"6","author":"Meng","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_14","first-page":"102054","article-title":"Spectral unmixing based spatiotemporal downscaling fusion approach","volume":"88","author":"Liu","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_16","unstructured":"Yang, D., Su, H., Yong, Y., and Zhan, J. (July, January 22). MODIS-Landsat Data Fusion for Estimating Vegetation Dynamics\u2014A Case Study for Two Ranches in Southwestern Texas. Proceedings of the 1st International Electronic Conference on Remote Sensing."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"10286","DOI":"10.3390\/rs61110286","article-title":"Landsat-8 operational land imager design, characterization and performance","volume":"6","author":"Knight","year":"2014","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/36.700993","article-title":"Prelaunch characteristics of the moderate resolution imaging spectroradiometer (MODIS) on EOS-AM1","volume":"36","author":"Barnes","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1109\/36.701081","article-title":"Key characteristics of MODIS data products","volume":"36","author":"Masuoka","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"527","DOI":"10.3390\/rs10040527","article-title":"Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions","volume":"10","author":"Zhu","year":"2018","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1016\/j.rse.2011.05.010","article-title":"Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain","volume":"115","author":"Hwang","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1016\/j.asr.2014.04.013","article-title":"A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape","volume":"54","author":"Mukherjee","year":"2014","journal-title":"Adv. Space Res."},{"key":"ref_25","first-page":"515","article-title":"Downscaling Landsat 7 ETM+ thermal imagery using land surface temperature and NDVI images","volume":"18","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"10483","DOI":"10.3390\/rs61110483","article-title":"On the downscaling of actual evapotranspiration maps based on combination of MODIS and Landsat-based actual evapotranspiration estimates","volume":"6","author":"Singh","year":"2014","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2014.02.003","article-title":"Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data","volume":"145","author":"Weng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1080\/15481603.2017.1382065","article-title":"Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA","volume":"55","author":"Boyte","year":"2018","journal-title":"Gisci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gao, F., Anderson, M., Daughtry, C., and Johnson, D. (2018). Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10091489"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4116","DOI":"10.1109\/JSTARS.2017.2701643","article-title":"Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data","volume":"10","author":"Wang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.1038\/s41598-018-20156-z","article-title":"Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion","volume":"8","author":"Wu","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2917","DOI":"10.1007\/s10980-019-00928-2","article-title":"Linear downscaling from MODIS to landsat: Connecting landscape composition with ecosystem functions","volume":"34","author":"Chen","year":"2019","journal-title":"Landsc. Ecol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.ecss.2018.05.031","article-title":"Spatial downscaling of MODIS Chlorophyll-a using Landsat 8 images for complex coastal water monitoring","volume":"209","author":"Fu","year":"2018","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105144","DOI":"10.1016\/j.compag.2019.105144","article-title":"Developing a fused vegetation temperature condition index for drought monitoring at field scales using Sentinel-2 and MODIS imagery","volume":"168","author":"Zhou","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1016\/j.rse.2009.03.007","article-title":"A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1080\/01431161.2014.883106","article-title":"Obtaining crop-specific time profiles of NDVI: The use of unmixing approaches for serving the continuity between SPOT-VGT and PROBA-V time series","volume":"35","author":"Atzberger","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1080\/2150704X.2013.769283","article-title":"Unified fusion of remote-sensing imagery: Generating simultaneously high-resolution synthetic spatial\u2013temporal\u2013spectral earth observations","volume":"4","author":"Huang","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1109\/LGRS.2016.2622726","article-title":"Fast and Accurate Spatiotemporal Fusion Based Upon Extreme Learning Machine","volume":"13","author":"Liu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xie, D., Zhang, J., Zhu, X., Pan, Y., Liu, H., Yuan, Z., and Yun, Y. (2016). An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions. Sensors, 16.","DOI":"10.3390\/s16020207"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/LGRS.2015.2402644","article-title":"Spatial and Temporal Image Fusion via Regularized Spatial Unmixing","volume":"12","author":"Xu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2014.09.012","article-title":"A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion","volume":"156","author":"Gevaert","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Wu, B., Zhang, M., and Zeng, H. (2016). Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products. Sensors, 16.","DOI":"10.3390\/s16122099"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2015.11.016","article-title":"A flexible spatiotemporal method for fusing satellite images with different resolutions","volume":"172","author":"Zhu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4672","DOI":"10.1002\/wrcr.20349","article-title":"A data fusion approach for mapping daily evapotranspiration at field scale: Data Fusion Approach for Mapping Daily ET","volume":"49","author":"Cammalleri","year":"2013","journal-title":"Water Resour. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2017.02.006","article-title":"Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration","volume":"126","author":"Ke","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"39","DOI":"10.5721\/EuJRS20164903","article-title":"A logistic-based method for rice monitoring from multitemporal MODIS-Landsat fusion data","volume":"49","author":"Son","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_47","first-page":"202","article-title":"Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data","volume":"57","author":"Vuolo","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1016\/j.rse.2017.09.031","article-title":"Fractional cover mapping of spruce and pine at 1 ha resolution combining very high and medium spatial resolution satellite imagery","volume":"204","author":"Immitzer","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.3390\/rs5031335","article-title":"Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_50","unstructured":"AIBA (2018). Levantamento Safra Oeste da Bahia 2017\u20132018, Associa\u00e7\u00e3o de Agricultores Irrigantes da Bahia."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.rse.2011.09.022","article-title":"Landsat: Building a strong future","volume":"122","author":"Loveland","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_53","unstructured":"Rudorff, B.F.R. (2007). Sensor Modis e Suas Aplica\u00e7\u00f5es Ambientas no Brasil, Editora Par\u00eantese."},{"key":"ref_54","unstructured":"Ponzoni, F., Shimabukuro, Y., and Kuplich, T. (2012). Sensoriamento Remoto da Vegeta\u00e7\u00e3o. 2a Edi\u00e7\u00e3o Atualizada e Ampliada, Oficina de Textos."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/TGRS.2012.2235447","article-title":"Experimental Evaluation of Sentinel-2 Spectral Response Functions for NDVI Time-Series Continuity","volume":"51","author":"Gonsamo","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1109\/TGRS.2016.2635802","article-title":"A Generalized Model for Intersensor NDVI Calibration and Its Comparison with Regression Approaches","volume":"55","author":"Fan","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","first-page":"528","article-title":"Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China","volume":"18","author":"Mao","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_58","first-page":"91","article-title":"Sensor MODIS: Caracter\u00edsticas gerais e aplica\u00e7\u00f5es","volume":"6","author":"Latorre","year":"2003","journal-title":"Rev. Espa\u00e7o E Geogr."},{"key":"ref_59","unstructured":"Mas, J.-F. (2011). Aplicaciones del Sensor MODIS Para el Monitoreo del Territorio, Primera ed., Secretar\u00eda de Medio Ambiente y Recursos Naturales."},{"key":"ref_60","unstructured":"Rouse, J., Haas, R.H., Schell, J.A., and Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS, NASA Special Publication."},{"key":"ref_61","unstructured":"Formaggio, A.R., and Sanches, I.D. (2017). Sensoriamento Remoto em Agricultura, Oficina de Textos."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.eja.2019.03.001","article-title":"New approach to determining the surface temperature without thermal band of satellites","volume":"106","author":"Filgueiras","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_64","unstructured":"R Core Team (2017). A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models: Part I\u2014A discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.agrformet.2017.04.014","article-title":"Hourly fine fuel moisture model for Pinus halepensis (Mill.) litter","volume":"243","author":"Jazbec","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.rser.2017.07.054","article-title":"Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs. RSM, MARS and M5 model tree","volume":"81","author":"Keshtegar","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3899","DOI":"10.1007\/s11269-016-1397-4","article-title":"Optimized River Stream-Flow Forecasting Model Utilizing High-Order Response Surface Method","volume":"30","author":"Keshtegar","year":"2016","journal-title":"Water Resour. Manag."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.compag.2016.05.018","article-title":"A nonlinear mathematical modeling of daily pan evaporation based on conjugate gradient method","volume":"127","author":"Keshtegar","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2995","DOI":"10.1007\/s00521-017-2917-8","article-title":"Modeling daily dissolved oxygen concentration using modified response surface method and artificial neural network: A comparative study","volume":"30","author":"Keshtegar","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3160","DOI":"10.1002\/hyp.11190","article-title":"Insights on stream temperature processes through development of a coupled hydrologic and stream temperature model for forested coastal headwater catchments: Stream temperature processes in headwater catchments","volume":"31","author":"Leach","year":"2017","journal-title":"Hydrol. Process."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.solener.2017.01.038","article-title":"Optimal interpolation of satellite and ground data for irradiance nowcasting at city scales","volume":"144","author":"Lorenzo","year":"2017","journal-title":"Sol. Energy"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"885","DOI":"10.13031\/2013.23153","article-title":"Model evaluation guidelines for systematic quantification of accuracy in watershed simulations","volume":"50","author":"Moriasi","year":"2007","journal-title":"Trans. ASABE"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","article-title":"Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature","volume":"7","author":"Chai","year":"2014","journal-title":"Geosci. Model. Dev."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1093\/biomet\/78.3.691","article-title":"A note on a general definition of the coefficient of determination","volume":"78","author":"Nagelkerke","year":"1991","journal-title":"Biometrika"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/S0034-4257(01)00248-6","article-title":"Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets","volume":"78","author":"Teillet","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Yan, L., Roy, D., Zhang, H., Li, J., and Huang, H. (2016). An Automated Approach for Sub-Pixel Registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8060520"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Ke, Y., Im, J., Park, S., and Gong, H. (2016). Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches. Remote Sens., 8.","DOI":"10.3390\/rs8030215"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s12237-017-0280-8","article-title":"Potential Salinity and Temperature Futures for the Chesapeake Bay Using a Statistical Downscaling Spatial Disaggregation Framework","volume":"41","author":"Muhling","year":"2018","journal-title":"Estuaries Coasts"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1297\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:09:04Z","timestamp":1760364544000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,20]]},"references-count":79,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12081297"],"URL":"https:\/\/doi.org\/10.3390\/rs12081297","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,20]]}}}