Synergistic use of Sentinel-1, Sentinel-2, and Landsat 8 in predicting forest variables

Astola, 2019, ‘Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region’, Remote Sens. Environ., 223, 257, 10.1016/j.rse.2019.01.019 Battude, 2016, Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data, Remote Sens. Environ., 184, 668, 10.1016/j.rse.2016.07.030 Belgiu, 2016, ISPRS Journal of Photogrammetry and Remote Sensing Random forest in remote sensing: A review of applications and future directions, Gut, 114, 24 Breiman, 2001, Random forests, Mach. Learn., 5, 10.1023/A:1010933404324 Carrasco, 2019, Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 for land cover mapping with Google Earth Engine, Remote Sens. (Basel), 11, 288, 10.3390/rs11030288 Castillo, 2017, Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery, ISPRS J. Photogramm. Remote Sens., 134, 70, 10.1016/j.isprsjprs.2017.10.016 Chang, J., Shoshany, M. 2016. Mediterranean shrublands biomass estimation using Sentinel-1 and Sentinel-2. In: International Geoscience and Remote Sensing Symposium (IGARSS), 2016-Novem(July), pp. 5300–5303. 10.1109/IGARSS.2016.7730380. Chen, 2017, A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform, ISPRS J. Photogramm. Remote Sens., 131, 104, 10.1016/j.isprsjprs.2017.07.011 Chen, T., Guestrin, C. 2016. XGBoost: A Scalable Tree Boosting System, pp. 785–794. Chen, 2020, Decision-level fusion of Sentinel-1 SAR and Landsat 8 OLI texture features for crop discrimination and classification: case of Masvingo, Zimbabwe, Heliyon, 6, e05358, 10.1016/j.heliyon.2020.e05358 Chrysafis, 2017, Assessing the relationships between growing stock volume and Sentinel-2 imagery in a Mediterranean forest ecosystem, Remote Sensing Letters, 8, 508, 10.1080/2150704X.2017.1295479 Condés, 2017, Updating national forest inventory estimates of growing stock volume using hybrid inference, For. Ecol. Manage., 400, 48, 10.1016/j.foreco.2017.04.046 De Luca, 2022, Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for land cover mapping in a Mediterranean region, Eur. J. Remote Sens., 55, 52, 10.1080/22797254.2021.2018667 Dostálová, 2021, European wide forest classification based on Sentinel-1 data, Remote Sens. (Basel), 13, 337, 10.3390/rs13030337 Fang, 2022, Comparison of variable selection methods among dominant tree species in different regions on forest stock volume estimation, Forests, 13, 787, 10.3390/f13050787 FAO, 2020 Feng, 2022, Multispecies forest plantations outyield monocultures across a broad range of conditions, Science, 376, 865, 10.1126/science.abm6363 Fernández-Manso, 2016, SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity, Int. J. Appl. Earth Obs. Geoinf., 50, 170 Ferrant, 2017, Detection of irrigated crops from Sentinel-1 and Sentinel-2 data to estimate seasonal groundwater use in South India, Remote Sens. (Basel), 9 Foley, 2005, Global consequences of land use, Science, 309, 570, 10.1126/science.1111772 Forkuor, 2020, Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets - A case study, Remote Sens. Environ., 236, 10.1016/j.rse.2019.111496 Fu, 2022, Spatial correlation of nutrients in a typical soil-hickory system of southeastern China and its implication for site-specific fertilizer application, Soil Tillage Res., 217, 10.1016/j.still.2021.105265 Gemmell, 1999, Estimating conifer forest cover with thematic mapper data using reflectance model inversion and two spectral indices in a site with variable background characteristics, Remote Sens. Environ., 69, 105, 10.1016/S0034-4257(99)00004-8 Grabska, E., Socha, J. 2021. Evaluating the effect of stand properties and site conditions on the forest reflectance from Sentinel-2 time series, PLoS ONE, 16(3 March), 1–23. 10.1371/journal.pone.0248459. Greifeneder, 2018, The added value of the VH/VV polarization-ratio for global soil moisture estimations from scatterometer data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 11, 3668, 10.1109/JSTARS.2018.2865185 Halperin, 2016, Canopy cover estimation in miombo woodlands of Zambia: Comparison of Landsat 8 OLI versus RapidEye imagery using parametric, nonparametric, and semiparametric methods, Remote Sens. Environ., 179, 170, 10.1016/j.rse.2016.03.028 Han, 2021, Developing a new method to identify flowering dynamics of rapeseed using landsat 8 and sentinel-1/2, Remote Sens. (Basel), 13, 1 Hassan, N., Hashim, M. 2011. Decomposition of mixed pixels of ASTER satellite data for mapping Chengal (Neobalanocarpus heimii sp.) tree. In: 2011 IEEE International Conference on Control System, Computing and Engineering. IEEE, pp. 74–79. 10.1109/ICCSCE.2011.6190499. Hu, 2020, Estimating forest stock volume in Hunan Province, China, by integrating in situ plot data, Sentinel-2 images, and linear and machine learning regression models, Remote Sens. (Basel), 12, 186, 10.3390/rs12010186 Huete, 1985, Spectral response of a plant canopy with different soil backgrounds, Remote Sens. Environ., 17, 37, 10.1016/0034-4257(85)90111-7 Immitzer, 2016, First experience with Sentinel-2 data for crop and tree species classifications in central Europe, Remote Sens. (Basel), 8 Jennings, 1999, Assessing forest canopies and understorey illumination: canopy closure, canopy cover and other measures, Forestry, 72, 59, 10.1093/forestry/72.1.59 Kankanamge, 2019, Taxi trip travel time prediction with isolated XGBoost regression, 54 Korhonen, 2006, Estimation of forest canopy cover: a comparison of field measurement techniques, Silva Fennica, 40, 10.14214/sf.315 Korhonen, 2017, Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index, Remote Sens. Environ., 195, 259, 10.1016/j.rse.2017.03.021 Korhonen, 2013, Modelling lidar-derived boreal forest canopy cover with SPOT 4 HRVIR data, Int. J. Remote Sens., 34, 8172, 10.1080/01431161.2013.833361 Laurin, 2018, Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data, J. Appl. Remote Sens., 12, 1, 10.1117/1.JRS.12.016008 Li, 2012, A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region, ISPRS J. Photogramm. Remote Sens., 70, 26, 10.1016/j.isprsjprs.2012.03.010 Li, 2020, Estimating the growing stem volume of Chinese pine and larch plantations based on fused optical data using an improved variable screening method and stacking algorithm, Remote Sens. (Basel), 12, 871, 10.3390/rs12050871 Li, 2021, Mapping the growing stem volume of the coniferous plantations in north china using multispectral data from integrated GF-2 and Sentinel-2 images and an optimized feature variable selection method, Remote Sens. (Basel), 13, 2740, 10.3390/rs13142740 Lin’an Government Lobell, 2001, Subpixel canopy cover estimation of coniferous forests in Oregon using SWIR imaging spectrometry, J. Geophys. Res. Atmos., 106, 5151, 10.1029/2000JD900739 Lobert, 2021, Mowing event detection in permanent grasslands: Systematic evaluation of input features from Sentinel-1, Sentinel-2, and Landsat 8 time series, Remote Sens. Environ., 267 Lu, 2016, A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems, Int. J. Digital Earth, 9, 63, 10.1080/17538947.2014.990526 Malhi, 2022, Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India, Adv. Space Res., 69, 1752, 10.1016/j.asr.2021.03.035 Mao, 2020, Spatiotemporal dynamics of bamboo forest net primary productivity with climate variations in Southeast China, Ecol. Ind., 116, 10.1016/j.ecolind.2020.106505 McCombs, 2003, Influence of fusing lidar and multispectral imagery on remotely sensed estimates of stand density and mean tree height in a managed loblolly pine plantation, For. Sci., 49, 457 Mermoz, 2015, Decrease of L-band SAR backscatter with biomass of dense forests, Remote Sens. Environ., 159, 307, 10.1016/j.rse.2014.12.019 Meyer, 2019, Comparison of Landsat-8 and Sentinel-2 data for estimation of leaf area index in temperate forests, Remote Sens. (Basel), 11, 1160, 10.3390/rs11101160 Mohammadi, 2010, Modelling forest stand volume and tree density using landsat ETM+ data, Int. J. Remote Sens., 31, 2959, 10.1080/01431160903140811 Morin, 2019, Estimation and mapping of forest structure parameters from open access satellite images: development of a generic method with a study case on coniferous plantation, Remote Sens. (Basel), 11, 1275, 10.3390/rs11111275 Mura, 2018, Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems, Int. J. Appl. Earth Obs. Geoinf., 66, 126 Mutanga, 2012, International Journal of Applied Earth Observation and Geoinformation High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm, Int. J. Appl. Earth Observ. Geoinf., 18, 399 Narine, 2020, Using ICESat-2 to estimate and map forest aboveground biomass: A first example, Remote Sens. (Basel), 12, 1824, 10.3390/rs12111824 Ndikumana, 2018, Estimation of rice height and biomass using multitemporal SAR Sentinel-1 for Camargue, Southern France, Remote Sens. (Basel), 10, 1 Nuthammachot, 2022, Combined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation, Geocarto Int., 37, 366, 10.1080/10106049.2020.1726507 Oladi, 2005, Developing diameter at breast height (DBH) and a height estimation model from remotely sensed data, J. Agric. Sci. Technol., 7, 95 Patrignani, 2015, Canopeo: A powerful new tool for measuring fractional green canopy cover, Agron. J., 107, 2312, 10.2134/agronj15.0150 Peña, 2012, Constructing satellite-derived hyperspectral indices sensitive to canopy structure variables of a Cordilleran Cypress (Austrocedrus chilensis) forest, ISPRS J. Photogramm. Remote Sens., 74, 1, 10.1016/j.isprsjprs.2012.06.010 Poortinga, 2019, Mapping plantations in Myanmar by fusing Landsat-8, Sentinel-2 and Sentinel-1 data along with systematic error quantification, Remote Sens. (Basel), 11, 1 Ramoelo, 2015, Potential of Sentinel-2 spectral configuration to assess rangeland quality, J. Appl. Remote Sens., 9, 10.1117/1.JRS.9.094096 Sánchez-Ruiz, 2019, Growing stock volume from multi-temporal landsat imagery through google earth engine, Int. J. Appl. Earth Obs. Geoinf., 83 Schepaschenko, 2019, The Forest Observation System, building a global reference dataset for remote sensing of forest biomass, Sci. Data, 6, 1, 10.1038/s41597-019-0196-1 Song, 2021, An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping, Sci. Remote Sens., 3 Spracklen, 2021, Synergistic use of Sentinel-1 and Sentinel-2 to map natural forest and Acacia plantation and stand ages in North-Central Vietnam, Remote Sens. (Basel), 13, 185, 10.3390/rs13020185 Sun, 2020, Red-edge band vegetation indices for leaf area index estimation from Sentinel-2/MSI imagery, IEEE Trans. Geosci. Remote Sens., 58, 826, 10.1109/TGRS.2019.2940826 Sutherland, 2016, Seeing the forest for its multiple ecosystem services: Indicators for cultural services in heterogeneous forests, Ecol. Ind., 71, 123, 10.1016/j.ecolind.2016.06.037 Tian, 2015, A global analysis of soil acidification caused by nitrogen addition, Environ. Res. Lett., 10, 10.1088/1748-9326/10/2/024019 Toan, T. Le et al. 1994. Relating Forest Biomass to SAR Data, 30(2), 403–411. Valero, 2021, Synergy of Sentinel-1 and Sentinel-2 imagery for early seasonal agricultural crop mapping, Remote Sens. (Basel), 13, 4891, 10.3390/rs13234891 Van Tricht, 2018, Synergistic use of radar sentinel-1 and optical sentinel-2 imagery for crop mapping: A case study for Belgium, Remote Sens. (Basel), 10, 1 Veloso, 2017, Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications, Remote Sens. Environ., 199, 415, 10.1016/j.rse.2017.07.015 Verhegghen, 2022, Mapping canopy cover in African dry forests from the combined use of Sentinel-1 and Sentinel-2 data: Application to Tanzania for the year 2018, Remote Sens. (Basel), 14, 1522, 10.3390/rs14061522 Verrelst, 2015, Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison’, ISPRS J. Photogramm. Remote Sens., 108, 260, 10.1016/j.isprsjprs.2015.04.013 Vreugdenhil, 2018, Sensitivity of Sentinel-1 backscatter to vegetation dynamics: An Austrian case study, Remote Sens. (Basel), 10, 1 Vreugdenhil, 2020, Sentinel-1 cross ratio and vegetation optical depth: A comparison over Europe, Remote Sens. (Basel), 12, 3404, 10.3390/rs12203404 Wang, 2019, Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images, ISPRS J. Photogramm. Remote Sens., 154, 189, 10.1016/j.isprsjprs.2019.06.007 Wang, 2022, ‘Assessing Landsat-8 and Sentinel-2 spectral-temporal features for mapping tree species of northern plantation forests in Heilongjiang Province, China, For. Ecosyst., 9, 100032, 10.1016/j.fecs.2022.100032 Wolski, 2017, Keeping it simple: Monitoring flood extent in large data-poor wetlands using MODIS SWIR data, Int. J. Appl. Earth Obs. Geoinf., 57, 224 Yommy, A. S., Liu, R., Wu, A. S. 2015. SAR image despeckling using refined Lee filter. In: 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE, pp. 260–265. 10.1109/IHMSC.2015.236. Yu, 2021, The performance of relative height metrics for estimation of forest above-ground biomass using L - and X -bands TomoSAR data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 14, 1857, 10.1109/JSTARS.2021.3051081