{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T11:35:04Z","timestamp":1770896104422,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Agricultural droughts impose many economic and social losses on various communities. Most of the effective tools developed for agricultural drought assessment are based on vegetation indices (VIs). The aim of this study is to compare the response of two commonly used VIs to meteorological droughts\u2014Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and Soil Moisture and Ocean Salinity (SMOS) vegetation optical depth (VOD). For this purpose, meteorological droughts are calculated by using a standardized precipitation index over more than 24,000 pixels at 0.25\u00b0 \u00d7 0.25\u00b0 spatial resolution located in central Europe. Then, to evaluate the capability of VIs in the detection of agricultural droughts, the average values of VIs anomalies during dry and wet periods obtained from meteorological droughts are statistically compared to each other. Additionally, to assess the response time of VIs to meteorological droughts, a time lag of one to six months is applied to the anomaly time series of VIs during their comparison. Results show that over 35% of the considered pixels NDVI, over 22% of VOD, and over 8% of both VIs anomalies have a significant response to drought events, while the significance level of these differences and the response time of VIs vary with different land use and climate conditions.<\/jats:p>","DOI":"10.3390\/rs13071251","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T21:09:45Z","timestamp":1616706585000},"page":"1251","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Comparative Evaluation of Microwave L-Band VOD and Optical NDVI for Agriculture Drought Detection over Central Europe"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4411-3299","authenticated-orcid":false,"given":"Mehdi H.","family":"Afshar","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Middle East Technical University, Ankara 06800, Turkey"},{"name":"Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7530-6088","authenticated-orcid":false,"given":"Amen","family":"Al-Yaari","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, UMR 7619 METIS, Case 105, 4 Place Jussieu, F-75005 Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5094-1878","authenticated-orcid":false,"given":"M. Tugrul","family":"Yilmaz","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Middle East Technical University, Ankara 06800, Turkey"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bezak, N., and Miko\u0161, M. (2020). Changes in the Compound Drought and Extreme Heat Occurrence in the 1961\u20132018 Period at the European Scale. Water, 12.","DOI":"10.3390\/w12123543"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Schuldt, B., Buras, A., Arend, M., Vitasse, Y., Beierkuhnlein, C., Damm, A., Gharun, M., Grams, T.E.E., Hauck, M., and Hajek, P. (2020). A first assessment of the impact of the extreme 2018 summer drought on Central European forests. Basic Appl. Ecol.","DOI":"10.1016\/j.baae.2020.04.003"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ahmed, K.R., Paul-Limoges, E., Rascher, U., and Damm, A. (2021). A First Assessment of the 2018 European Drought Impact on Ecosystem Evapotranspiration. Remote Sens., 13.","DOI":"10.3390\/rs13010016"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.jhydrol.2010.07.012","article-title":"A review of drought concepts","volume":"391","author":"Mishra","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1139\/a11-013","article-title":"A review of drought indices","volume":"19","author":"Zargar","year":"2011","journal-title":"Environ. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/0034-4257(81)90037-7","article-title":"Temporal relationships between spectral response and agronomic variables of a corn canopy","volume":"11","author":"Kimes","year":"1981","journal-title":"Remote Sens. Environ."},{"key":"ref_7","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2636","DOI":"10.3390\/s7112636","article-title":"Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: A case study in high-density cypress forest","volume":"7","author":"Matsushita","year":"2007","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.agrformet.2019.01.007","article-title":"Remotely sensed agricultural grassland productivity responses to land use and hydro-climatic drivers under extreme drought and rainfall","volume":"268","author":"Kath","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jaridenv.2019.01.019","article-title":"NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia from 2000 to 2016","volume":"164","author":"Nanzad","year":"2019","journal-title":"J. Arid Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1016\/j.scitotenv.2017.10.253","article-title":"Distinguishing the vegetation dynamics induced by anthropogenic factors using vegetation optical depth and AVHRR NDVI: A cross-border study on the Mongolian Plateau","volume":"616","author":"Zhou","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Afshar, M.H., Foster, T., Higginbottom, T.P., Parkes, B., Hufkens, K., Mansabdar, S., Ceballos, F., and Kramer, B. (2021). Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring. Remote Sens., 13.","DOI":"10.3390\/rs13050924"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Marco-Dos Santos, G., Melendez-Pastor, I., Navarro-Pedre\u00f1o, J., and Koch, M. (2019). Assessing Water Availability in Mediterranean Regions Affected by Water Conflicts through MODIS Data Time Series Analysis. Remote Sens., 11.","DOI":"10.3390\/rs11111355"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ufug.2017.10.010","article-title":"Exploring the impact of green space health on runoff reduction using NDVI","volume":"28","author":"Kim","year":"2017","journal-title":"Urban For. Urban Green."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Natsagdorj, E., Renchin, T., De Maeyer, P., and Darkhijav, B. (2021). Spatial Distribution of Soil Moisture in Mongolia Using SMAP and MODIS Satellite Data: A Time Series Model (2010\u20132025). Remote Sens., 13.","DOI":"10.3390\/rs13030347"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bulut, B., Y\u0131lmaz, M.T., Afshar, M.H., \u015eorman, A.\u00dc., Y\u00fccel, \u0130., Cosh, M.H., and \u015eim\u015fek, O. (2019). Evaluation of Remotely-Sensed and Model-Based Soil Moisture Products According to Different Soil Type, Vegetation Cover and Climate Regime Using Station-Based Observations over Turkey. Remote Sens., 11.","DOI":"10.3390\/rs11161875"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"112238","DOI":"10.1016\/j.rse.2020.112238","article-title":"SMOS-IC data record of soil moisture and L-VOD: Historical development, applications and perspectives","volume":"254","author":"Wigneron","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Frappart, F., Wigneron, J.P., Li, X., Liu, X., Al-Yaari, A., Fan, L., Wang, M., Moisy, C., Le Masson, E., and Lafkih, Z.A. (2020). Global monitoring of the vegetation dynamics from the vegetation optical depth (VOD): A review. Remote Sens., 12.","DOI":"10.3390\/rs12182915"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.rse.2019.03.026","article-title":"Satellite-based vegetation optical depth as an indicator of drought-driven tree mortality","volume":"227","author":"Rao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111451","DOI":"10.1016\/j.rse.2019.111451","article-title":"Can vegetation optical depth reflect changes in leaf water potential during soil moisture dry-down events?","volume":"234","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Al-Yaari, A., Wigneron, J.P., Ciais, P., Reichstein, M., Ballantyne, A., Og\u00e9e, J., Ducharne, A., Swenson, J.J., Frappart, F., and Fan, L. (2020). Asymmetric responses of ecosystem productivity to rainfall anomalies vary inversely with mean annual rainfall over the conterminous United States. Glob. Chang. Biol.","DOI":"10.1111\/gcb.15345"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Brandt, M., Wigneron, J.P., Chave, J., Tagesson, T., Penuelas, J., Ciais, P., Rasmussen, K., Tian, F., Mbow, C., and Al-Yaari, A. (2018). Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat. Ecol. Evol., 2.","DOI":"10.1038\/s41559-018-0530-6"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mialon, A., Rodr\u00edguez-Fern\u00e1ndez, N.J., Santoro, M., Saatchi, S., Mermoz, S., Bousquet, E., and Kerr, Y.H. (2020). Evaluation of the sensitivity of SMOS L-VOD to forest above-ground biomass at global scale. Remote Sens., 12.","DOI":"10.3390\/rs12091450"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.rse.2012.03.025","article-title":"Satellite passive microwave detection of North America start of season","volume":"123","author":"Jones","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3167","DOI":"10.1111\/gcb.12283","article-title":"Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011","volume":"19","author":"Barichivich","year":"2013","journal-title":"Glob. Chang. Biol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1038\/s41559-018-0630-3","article-title":"Coupling of ecosystem-scale plant water storage and leaf phenology observed by satellite","volume":"2","author":"Tian","year":"2018","journal-title":"Nat. Ecol. Evol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.5194\/nhess-18-1535-2018","article-title":"Estimating grassland curing with remotely sensed data","volume":"18","author":"Chaivaranont","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1016\/j.rse.2010.12.015","article-title":"Satellite passive microwave remote sensing for monitoring global land surface phenology","volume":"115","author":"Jones","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, Y.Y., de Jeu, R.A.M., McCabe, M.F., Evans, J.P., and van Dijk, A.I.J.M. (2011). Global long-term passive microwave satellite-based retrievals of vegetation optical depth. Geophys. Res. Lett., 38.","DOI":"10.1029\/2011GL048684"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6657","DOI":"10.5194\/bg-10-6657-2013","article-title":"Global changes in dryland vegetation dynamics (1988\u20132008) assessed by satellite remote sensing: Comparing a new passive microwave vegetation density record with reflective greenness data","volume":"10","author":"Andela","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, Y.Y., Evans, J.P., McCabe, M.F., de Jeu, R.A.M., van Dijk, A.I.J.M., Dolman, A.J., and Saizen, I. (2013). Changing Climate and Overgrazing Are Decimating Mongolian Steppes. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0057599"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1002\/ldr.3342","article-title":"African dryland ecosystem changes controlled by soil water","volume":"30","author":"Wei","year":"2019","journal-title":"Land Degrad. Dev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107703","DOI":"10.1016\/j.agrformet.2019.107703","article-title":"Divergent vegetation responses to extreme spring and summer droughts in Southwestern China","volume":"279","author":"Song","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5716","DOI":"10.1111\/gcb.15215","article-title":"Identifying areas at risk of drought-induced tree mortality across South-Eastern Australia","volume":"26","author":"Medlyn","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mateo-Sanchis, A., Piles, M., Mu\u00f1oz-Mar\u00ed, J., Adsuara, J.E., P\u00e9rez-Suay, A., and Camps-Valls, G. (2019). Synergistic integration of optical and microwave satellite data for crop yield estimation. Remote Sens. Environ., 234.","DOI":"10.1016\/j.rse.2019.111460"},{"key":"ref_36","first-page":"79","article-title":"Assessing the relationship between microwave vegetation optical depth and gross primary production","volume":"65","author":"Teubner","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Fern\u00e1ndez, N.J., Mialon, A., Mermoz, S., Bouvet, A., Richaume, P., Al Bitar, A., Al-Yaari, A., Brandt, M., Kaminski, T., and Le Toan, T. (2018). An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: High sensitivity of L-VOD to above-ground biomass in Africa. Biogeosciences, 15.","DOI":"10.5194\/bg-15-4627-2018"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gruber, A., Dorigo, W.A., Crow, W., and Wagner, W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2017.2734070"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth Syst. Sci. Data Discuss.","DOI":"10.5194\/essd-2019-21"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., and Gruber, A. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2017.07.001"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Fernandez-Moran, R., Al-Yaari, A., Mialon, A., Mahmoodi, A., Al Bitar, A., De Lannoy, G., Rodriguez-Fernandez, N., Lopez-Baeza, E., Kerr, Y., and Wigneron, J.P. (2017). SMOS-IC: An alternative SMOS soil moisture and vegetation optical depth product. Remote Sens., 9.","DOI":"10.20944\/preprints201703.0145.v1"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, X., Al-Yaari, A., Schwank, M., Fan, L., Frappart, F., Swenson, J., and Wigneron, J.P. (2020). Compared performances of SMOS-IC soil moisture and vegetation optical depth retrievals based on Tau-Omega and Two-Stream microwave emission models. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2019.111502"},{"key":"ref_44","unstructured":"Didan, K. (2015). MOD13Q1 MODIS\/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006. NASA EOSDIS Land Process. DAAC."},{"key":"ref_45","unstructured":"Dresden, E.A., Berg, L., and von K\u00f6ppen, W. (1936). Handbuch der Klimatologie in f\u00fcnf B\u00e4nden Das geographische System der Klimate, Borntraeaer Science."},{"key":"ref_46","unstructured":"ESA (2017). Land Cover CCI Product User Guide Version 2, ESA."},{"key":"ref_47","first-page":"179","article-title":"The relationship of drought frequency and duration to time scales","volume":"Volume 17","author":"McKee","year":"1993","journal-title":"Proceedings of the 8th Conference on Applied Climatology"},{"key":"ref_48","unstructured":"Danandeh Mehr, A., Sorman, A.U., Kahya, E., and Afshar, M.H. (2019). Climate change impacts on meteorological drought in Ankara, Turkey. Hydrol. Sci. J., in press."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Afshar, M.H., Sorman, A.U., Tosunoglu, F., Bulut, B., Yilmaz, M.T., and Danandeh Mehr, A. (2020). Climate Change Impact Assessment on Mild and Extreme Drought Events using Copulas over Ankara, Turkey. Theor. Appl. Climatol., Under Review.","DOI":"10.1007\/s00704-020-03257-6"},{"key":"ref_50","first-page":"211","article-title":"A comparative performance analysis of three standardized climatic drought indices in the Chi River basin, Thailand","volume":"50","author":"Homdee","year":"2016","journal-title":"Agric. Nat. Resour."},{"key":"ref_51","unstructured":"Beguer\u00eda, S., and Vicente-Serrano, S.M. (2013). SPEI: Calculation of the Standardised Precipitation-Evapotranspiration Index, R package Version 1.6."},{"key":"ref_52","unstructured":"R Core Team (2018). R: A Language and Environment for Statistical Computing, RC Team."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Maleki, M., Arriga, N., Barrios, J.M., Wieneke, S., Liu, Q., Pe\u00f1uelas, J., Janssens, I.A., and Balzarolo, M. (2020). Estimation of Gross Primary Productivity (GPP) Phenology of a Short-Rotation Plantation Using Remotely Sensed Indices Derived from Sentinel-2 Images. Remote Sens., 12.","DOI":"10.3390\/rs12132104"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.rse.2018.04.049","article-title":"L-band vegetation optical depth seasonal metrics for crop yield assessment","volume":"212","author":"Chaparro","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Wigneron, J.-P., Zribi, M., Albergel, C., Calvet, J.-C., and Fayad, I. (2019). First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields. Remote Sens., 11.","DOI":"10.3390\/rs11232769"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Kerr, Y.H., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B., Leroux, D., Bircher, S., Mahmoodi, A., Mialon, A., and Richaume, P. (2016). Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2016.02.042"},{"key":"ref_58","first-page":"F01002","article-title":"Multisensor historical climatology of satellite-derived global land surface moisture","volume":"113","author":"Owe","year":"2008","journal-title":"J. Geophys. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.rse.2016.11.026","article-title":"The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E","volume":"189","author":"Kerr","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Konings, A.G., Piles, M., Das, N., and Entekhabi, D. (2017). L-band vegetation optical depth and effective scattering albedo estimation from SMAP. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2017.06.037"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Liu, R., Wen, J., Wang, X., Wang, Z., Li, Z., Xie, Y., Zhu, L., and Li, D. (2019). Derivation of Vegetation Optical Depth and Water Content in the Source Region of the Yellow River using the FY-3B Microwave Data. Remote Sens., 11.","DOI":"10.3390\/rs11131536"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1109\/TGRS.2012.2182775","article-title":"SMOS Radio Frequency Interference Scenario: Status and Actions Taken to Improve the RFI Environment in the 1400\u20131427-MHz Passive Band","volume":"50","author":"Oliva","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hesami Afshar, M., Sorman, A., and Yilmaz, M. (2016). Conditional Copula-Based Spatial\u2013Temporal Drought Characteristics Analysis\u2014A Case Study over Turkey. Water, 8.","DOI":"10.3390\/w8100426"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Oliva, R., Daganzo, E., Richaume, P., Kerr, Y., Cabot, F., Soldo, Y., Anterrieu, E., Reul, N., Gutierrez, A., and Barbosa, J. (2016). Status of Radio Frequency Interference (RFI) in the 1400-1427 MHz passive band based on six years of SMOS mission. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2016.01.013"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Hua, L., Wang, H., Sui, H., Wardlow, B., Hayes, M.J., and Wang, J. (2019). Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region. Remote Sens., 11.","DOI":"10.3390\/rs11161873"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1111\/pce.12646","article-title":"Rooting depth, water relations and non-structural carbohydrate dynamics in three woody angiosperms differentially affected by an extreme summer drought","volume":"39","author":"Nardini","year":"2016","journal-title":"Plant Cell Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2423","DOI":"10.1007\/s00477-017-1492-y","article-title":"Time-lag effects of vegetation responses to soil moisture evolution: A case study in the Xijiang basin in South China","volume":"32","author":"Niu","year":"2018","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s40725-020-00119-2","article-title":"Quantifying Growth Responses of Trees to Drought\u2014A Critique of Commonly Used Resilience Indices and Recommendations for Future Studies","volume":"6","author":"Schwarz","year":"2020","journal-title":"Curr. For. Rep."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1111\/gcb.14056","article-title":"Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015\/2016","volume":"24","author":"Yang","year":"2018","journal-title":"Glob. Chang. Biol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1251\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:41:05Z","timestamp":1760161265000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,25]]},"references-count":69,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071251"],"URL":"https:\/\/doi.org\/10.3390\/rs13071251","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,25]]}}}