{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T20:57:47Z","timestamp":1772053067582,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T00:00:00Z","timestamp":1641427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071303"],"award-info":[{"award-number":["42071303"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Foundation for Science and Technology Basic Resource Investigation Program of China","award":["2019FY101304"],"award-info":[{"award-number":["2019FY101304"]}]},{"name":"Joint program of Beijing Municipal Education Commission and Beijing Municipal Natural Science Foundation","award":["KZ202110028044"],"award-info":[{"award-number":["KZ202110028044"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimating the carbon (C), nitrogen (N), and phosphorus (P) contents of a large-span grassland transect is essential for evaluating ecosystem functioning and monitoring biogeochemical cycles. However, the field measurements are scattered, such that they cannot indicate the continuous gradient change in the grassland transect. Although remote sensing methods have been applied for the estimation of nutrient elements at the local scale in recent years, few studies have considered the effective estimation of C, N, and P contents over large-span grassland transects with complex environment including a variety of grassland types (i.e., meadow, typical grassland, and desert grassland). In this paper, an information enhancement algorithm (involving spectral enhancement, regional enhancement, and feature enhancement) is used to extract the weak information related to C, N, and P. First, the spectral simulation algorithm is used to enhance the spectral information of Sentinel-2 imagery. Then, the enhanced spectra and meteorological data are fused to express regional characteristics and the fractional differential (FD) algorithm is used to extract sensitive spectral features related to C, N, and P, in order to construct a partial least-squares regression (PLSR) model. Finally, the C, N, and P contents are estimated over a West\u2013East grassland transect in Inner Mongolia, China. The results demonstrate that: (i) the contents of C, N, and P in large-span transects can be effectively estimated through use of the information enhancement method involving spectral enhancement, regional feature enhancement, and information enhancement, for which the estimation accuracies (R2) were 0.88, 0.78, and 0.85, respectively. Compared with the estimation results of raw Sentinel-2 imagery, the RMSE was reduced by 3.42 g\/m2, 0.14 g\/m2, and 13.73 mg\/m2, respectively; and (ii) the continuous change trend and spatial distribution characteristics of C, N, and P contents in the west\u2013east transect of the Inner Mongolia Plateau were obtained, which showed decreasing trends in C, N, and P contents from east to west and the characteristics of meadow &gt; typical grassland &gt; desert grassland. Thus, the information enhancement algorithm can help to improve estimates of C, N, and P contents when considering large-span grassland transects.<\/jats:p>","DOI":"10.3390\/rs14020242","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"242","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Estimating Carbon, Nitrogen, and Phosphorus Contents of West\u2013East Grassland Transect in Inner Mongolia Based on Sentinel-2 and Meteorological Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Haiyang","family":"Pang","sequence":"first","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Engineering Research Center of Spatial Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China"}]},{"given":"Aiwu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Engineering Research Center of Spatial Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China"}]},{"given":"Shengnan","family":"Yin","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Engineering Research Center of Spatial Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China"}]},{"given":"Jiaxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Engineering Research Center of Spatial Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China"}]},{"given":"Gang","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Life Science, Shanxi University, Taiyuan 030006, China"}]},{"given":"Nianpeng","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Wenxuan","family":"Qin","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Engineering Research Center of Spatial Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China"}]},{"given":"Dandan","family":"Wei","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resource, Beijing 100048, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cui, L., Dou, Z., Liu, Z., Zuo, X., Lei, Y., Li, J., Zhao, X., Zhai, X., Pan, X., and Li, W. (2020). Hyperspectral Inversion of Phragmites Communis Carbon, Nitrogen, and Phosphorus Stoichiometry Using Three Models. Remote Sens., 12.","DOI":"10.3390\/rs12121998"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"147201","DOI":"10.1016\/j.scitotenv.2021.147201","article-title":"Patterns and drivers of carbon, nitrogen and phosphorus stoichiometry in Southern China\u2019s grasslands","volume":"785","author":"Wang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1111\/nph.16671","article-title":"Legacy effects of nitrogen and phosphorus additions on vegetation and carbon stocks of upland heaths","volume":"228","author":"Paassen","year":"2020","journal-title":"New Phytol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1890\/02-0251","article-title":"Carbon sequestration in ecosystems: The role of stoichiometry","volume":"85","author":"Hessen","year":"2004","journal-title":"Ecology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.3389\/fpls.2018.01883","article-title":"Estimation of Dynamic Canopy Variables Using Hyperspectral Derived Vegetation Indices Under Varying N Rates at Diverse Phenological Stages of Rice","volume":"9","author":"Din","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, H., Crabbe, M.J.C., Xu, F., Wang, W., Niu, R., Gao, X., Zhang, P., and Chen, H. (2017). Seasonal Variations in Carbon, Nitrogen and Phosphorus Concentrations and C:N:P Stoichiometry in the Leaves of Differently Aged Larix principis-rupprechtii Mayr. Plantations. Forests, 8.","DOI":"10.3390\/f8100373"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.isprsjprs.2020.03.017","article-title":"Potential of hyperspectral data and machine learning algorithms to estimate the forage carbon-nitrogen ratio in an alpine grassland ecosystem of the Tibetan Plateau","volume":"163","author":"Gao","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1111\/j.1469-8137.2009.03106.x","article-title":"Leaf nitrogen to phosphorus ratios of tropical trees: Experimental assessment of physiological and environmental controls","volume":"185","author":"Cernusak","year":"2010","journal-title":"New Phytol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s11104-013-1823-9","article-title":"C:N:P stoichiometry in Australian soils with respect to vegetation and environmental factors","volume":"373","author":"Bui","year":"2013","journal-title":"Plant Soil"},{"key":"ref_10","first-page":"298","article-title":"IGBP\/GCTE terrestrial transects: Dynamics of terrestrial ecosystems under environmental change","volume":"13","author":"Canadell","year":"2002","journal-title":"J. Veg. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107267","DOI":"10.1016\/j.ecolind.2020.107267","article-title":"Hyperspectral retrieval of leaf physiological traits and their links to ecosystem productivity in grassland monocultures","volume":"122","author":"Zhao","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"47","DOI":"10.33584\/jnzg.2015.77.482","article-title":"Mapping nutrient concentration in pasture using hyperspectral imaging","volume":"77","author":"Yule","year":"2015","journal-title":"J. N. Z. Grassl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s11119-014-9348-7","article-title":"Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.)","volume":"15","author":"Mahajan","year":"2014","journal-title":"Precis. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.compag.2018.06.029","article-title":"A non-destructive determination of peroxide values, total nitrogen and mineral nutrients in an edible tree nut using hyperspectral imaging","volume":"151","author":"Baia","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecocom.2013.06.003","article-title":"Review of optical-based remote sensing for plant trait mapping","volume":"15","author":"Clevers","year":"2013","journal-title":"Ecol. Complex."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.agrformet.2018.02.010","article-title":"Mapping forest canopy nitrogen content by inversion of coupled leaf-canopy radiative transfer models from airborne hyperspectral imagery","volume":"253","author":"Wang","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4361","DOI":"10.1038\/s41598-020-61294-7","article-title":"Estimation of leaf nutrition status in degraded vegetation based on field survey and hyperspectral data","volume":"10","author":"Peng","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"112003","DOI":"10.1016\/j.rse.2020.112003","article-title":"Monitoring biochemical limitations to photosynthesis in N and P-limited radiata pine using plant functional traits quantified from hyperspectral imagery","volume":"248","author":"Watt","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2091","DOI":"10.1007\/s11368-017-1751-z","article-title":"The potential of hyperspectral images and partial least square regression for predicting total carbon, total nitrogen and their isotope composition in forest litterfall samples","volume":"17","author":"Tahmasbian","year":"2017","journal-title":"J. Soils Sediments"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.rse.2003.11.001","article-title":"Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features","volume":"89","author":"Mutanga","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.3389\/fpls.2019.01047","article-title":"Estimation of Corn Canopy Chlorophyll Content Using Derivative Spectra in the O2-A Absorption Band","volume":"10","author":"Zhang","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_22","first-page":"84","article-title":"Canopy foliar nitrogen retrieved from airborne hyperspectral imagery by correcting for canopy structure effects","volume":"54","author":"Wang","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.isprsjprs.2011.01.008","article-title":"Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations","volume":"66","author":"Ramoelo","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"114228","DOI":"10.1016\/j.geoderma.2020.114228","article-title":"Exploring the potential of airborne hyperspectral image for estimating topsoil organic carbon: Effects of fractional-order derivative and optimal band combination algorithm","volume":"365","author":"Hong","year":"2020","journal-title":"Geoderma"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104257","DOI":"10.1016\/j.catena.2019.104257","article-title":"Prediction of soil organic matter in northwestern China using fractional-order derivative spectroscopy and modified normalized difference indices","volume":"185","author":"Zhang","year":"2020","journal-title":"Catena"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wei, L., Yu, M., Zhong, Y., Zhao, J., Liang, Y., and Hu, X. (2019). Spatial\u2013Spectral Fusion Based on Conditional Random Fields for the Fine Classification of Crops in UAV-Borne Hyperspectral Remote Sensing Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11070780"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.3389\/fpls.2018.01195","article-title":"Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease","volume":"9","author":"Yu","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.envpol.2015.05.041","article-title":"Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images","volume":"205","author":"Arellano","year":"2015","journal-title":"Environ. Pollut."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111416","DOI":"10.1016\/j.rse.2019.111416","article-title":"Satellite-ground integrated destriping network: A new perspective for EO-1 Hyperion and Chinese hyperspectral satellite datasets","volume":"237","author":"Zhong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pang, H., Zhang, A., Kang, X., He, N., and Dong, G. (2020). Estimation of the Grassland Aboveground Biomass of the Inner Mongolia Plateau Using the Simulated Spectra of Sentinel-2 Images. Remote Sens., 12.","DOI":"10.3390\/rs12244155"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MGRS.2016.2637824","article-title":"Hyperspectral and Multispectral Data Fusion: A Comparative Review","volume":"5","author":"Yokoya","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1080\/15481603.2021.1877435","article-title":"Investigating different versions of PROSPECT and PROSAIL for estimating spectral and biophysical properties of photosynthetic and non-photosynthetic vegetation in mixed grasslands","volume":"58","author":"Lu","year":"2021","journal-title":"GISci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jaridenv.2019.01.004","article-title":"Spatiotemporal variations and its influencing factors of grassland net primary productivity in Inner Mongolia, China during the period 2000\u20132014","volume":"165","author":"Zhao","year":"2019","journal-title":"J. Arid Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.3389\/fpls.2020.01244","article-title":"Spatial Variation of Leaf Chlorophyll in Northern Hemisphere Grasslands","volume":"11","author":"Zhang","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_35","first-page":"69","article-title":"RS-Based Monitoring of NDVI Spatial Variations: A Case Study of Typical Grasslands on Mongolian Plateau","volume":"116","author":"Tangkesi","year":"2019","journal-title":"Nat. Inn. Asia"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1111\/geb.12427","article-title":"Coordinated pattern of multi-element variability in leaves and roots across Chinese forest biomes","volume":"25","author":"Zhao","year":"2016","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7159","DOI":"10.1109\/JSTARS.2021.3089655","article-title":"Performance Assessment of Optical Satellite-Based Operational Snow Cover Monitoring Algorithms in Forested Landscapes","volume":"14","author":"Muhuri","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TGRS.2006.886176","article-title":"Target Scattering Decomposition in Terms of Roll-Invariant Target Parameters","volume":"45","author":"Touzi","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","unstructured":"Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. (1998). Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56, FAO."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8017614","DOI":"10.1155\/2018\/8017614","article-title":"Application of Fractional Differential Calculation in Pretreatment of Saline Soil Hyperspectral Reflectance Data","volume":"2018","author":"Tian","year":"2018","journal-title":"J. Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Fu, C., Xiong, H., and Tian, A. (2018). Fractional Modeling for Quantitative Inversion of Soil-Available Phosphorus Content. Mathematics, 6.","DOI":"10.3390\/math6120330"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"115412","DOI":"10.1016\/j.envpol.2020.115412","article-title":"Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China","volume":"266","author":"Wang","year":"2020","journal-title":"Environ. Pollut."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bhadra, S., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Newcomb, M., Shakoor, N., and Mockler, T.C. (2020). Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning. Remote Sens., 12.","DOI":"10.3390\/rs12132082"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1236329","DOI":"10.1155\/2017\/1236329","article-title":"Influence of Fractional Differential on Correlation Coefficient between EC1:5 and Reflectance Spectra of Saline Soil","volume":"2017","author":"Xia","year":"2017","journal-title":"J. Spectrosc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"142661","DOI":"10.1016\/j.scitotenv.2020.142661","article-title":"Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images","volume":"755","author":"Zhou","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1038\/nclimate2549","article-title":"Decoupling of nitrogen and phosphorus in terrestrial plants associated with global changes","volume":"5","author":"Yuan","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"106043","DOI":"10.1016\/j.agwat.2020.106043","article-title":"Similarity and difference of potential evapotranspiration and reference crop evapotranspiration\u2014A review","volume":"232","author":"Xiang","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2005.12.011","article-title":"A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method","volume":"101","author":"Cho","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"105275","DOI":"10.1016\/j.compag.2020.105275","article-title":"A possible fractional order derivative and optimized spectral indices for assessing total nitrogen content in cotton","volume":"171","author":"Abulaiti","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.isprsjprs.2018.11.015","article-title":"Modeling alpine grassland forage phosphorus based on hyperspectral remote sensing and a multi-factor machine learning algorithm in the east of Tibetan Plateau, China","volume":"147","author":"Gao","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.biosystemseng.2021.03.006","article-title":"Generalisation of tea moisture content models bsased on VNIR spectra subjected to fractional differential treatment","volume":"205","author":"Wei","year":"2021","journal-title":"Biosyst. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lin, X., Su, Y.-C., Shang, J., Sha, J., Li, X., Sun, Y.-Y., Ji, J., and Jin, B. (2019). Geographically Weighted Regression Effects on Soil Zinc Content Hyperspectral Modeling by Applying the Fractional-Order Differential. Remote Sens., 11.","DOI":"10.3390\/rs11060636"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1016\/j.scitotenv.2019.02.125","article-title":"Current and emerging methodologies for estimating carbon sequestration in agricultural soils: A review","volume":"665","author":"Nayak","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.scitotenv.2019.03.151","article-title":"Mapping dynamics of soil organic matter in croplands with MODIS data and machine learning algorithms","volume":"669","author":"Chen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"291","DOI":"10.2307\/2404751","article-title":"Nitrogen and Phosphorus Cycling in Grazed and Ungrazed Plots in a Temperate Subhumid Grassland in Argentina","volume":"33","author":"Chaneton","year":"1996","journal-title":"J. Appl. Ecol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s00442-007-0912-y","article-title":"Leaf nitrogen:phosphorus stoichiometry across Chinese grassland biomes","volume":"155","author":"He","year":"2008","journal-title":"Oecologia"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.chnaes.2017.06.005","article-title":"Foliar carbon, nitrogen, and phosphorus stoichiometry in a grassland ecosystem along the Chinese Grassland Transect","volume":"37","author":"Hailing","year":"2017","journal-title":"Acta Ecol. Sin."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2015.02.013","article-title":"Evaluation of spectral unmixing techniques using MODIS in a structurally complex savanna environment for retrieval of green vegetation, nonphotosynthetic vegetation, and soil fractional cover","volume":"161","author":"Meyer","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Rumpel, C., and Chabbi, A. (2019). Plant-Soil Interactions Control CNP Coupling and Decoupling Processes in Agroecosystems with Perennial Vegetation. Agroecosystem Diversity, Academic Press.","DOI":"10.1016\/B978-0-12-811050-8.00001-7"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/242\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:12:56Z","timestamp":1760364776000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/242"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,6]]},"references-count":60,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14020242"],"URL":"https:\/\/doi.org\/10.3390\/rs14020242","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,6]]}}}