{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T11:26:20Z","timestamp":1774437980950,"version":"3.50.1"},"reference-count":83,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation Project of China","award":["32130066"],"award-info":[{"award-number":["32130066"]}]},{"name":"Henan Provincial Department of Science and Technology Research Project","award":["212102310019"],"award-info":[{"award-number":["212102310019"]}]},{"name":"Natural Science Foundation of Henan","award":["202300410531"],"award-info":[{"award-number":["202300410531"]}]},{"name":"Youth Science Foundation Program of Henan Natural Science Foundation","award":["202300410077"],"award-info":[{"award-number":["202300410077"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R2 = 0.9606, p &lt; 0.05): During the period 2003\u20132020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R2 = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann\u2013Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (p &gt; 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and p values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging \u22120.4~\u22120.2 and p values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future.<\/jats:p>","DOI":"10.3390\/rs14071570","type":"journal-article","created":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T23:31:43Z","timestamp":1648164703000},"page":"1570","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Spatiotemporal Comparison of Drought in Shaanxi\u2013Gansu\u2013Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9146-3494","authenticated-orcid":false,"given":"Xiaoyang","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0106-6709","authenticated-orcid":false,"given":"Haoming","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China"}]},{"given":"Baoying","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Chemistry and Chemical Engineering, Henan University, Kaifeng 475004, China"}]},{"given":"Wenzhe","family":"Jiao","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.agwat.2018.04.001","article-title":"Responses of gross primary production of grasslands and croplands under drought, pluvial, and irrigation conditions during 2010\u20132016, Oklahoma, USA","volume":"204","author":"Doughty","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1007\/s00477-014-0982-4","article-title":"Drought assessment using a multivariate drought index in the Luanhe River basin of Northern China","volume":"29","author":"Li","year":"2015","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_3","first-page":"61","article-title":"Drought assessment using a multivariate drought index in the Huaihe River basin of Eastern China","volume":"369","author":"Li","year":"2015","journal-title":"Proc. Int. Assoc. Hydrol. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.wse.2015.11.004","article-title":"Analysis of spatio-temporal evolution of droughts in Luanhe River Basin using different drought indices","volume":"8","author":"Wang","year":"2015","journal-title":"Water Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.rse.2016.12.010","article-title":"Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices","volume":"190","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Keyantash, J.A., and Dracup, J.A. (2004). An aggregate drought index: Assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resour. Res., 40.","DOI":"10.1029\/2003WR002610"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1023\/A:1013388814894","article-title":"Assessing vulnerability to agricultural drought: A Nebraska case study","volume":"25","author":"Wilhelmi","year":"2002","journal-title":"Nat. Hazards"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1080\/01431161.2011.600349","article-title":"Comparison of remotely sensed and meteorological data-derived drought indices in mid-eastern China","volume":"33","author":"Zhou","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0168-1923(03)00072-8","article-title":"An evaluation of agricultural drought indices for the Canadian prairies","volume":"118","author":"Quiring","year":"2003","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1029\/WR016i002p00289","article-title":"On the statistical characteristics of drought events","volume":"16","author":"Dracup","year":"1980","journal-title":"Water Resour. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112124","DOI":"10.1016\/j.rse.2020.112124","article-title":"Soil moisture-based index for agricultural drought assessment: SMADI application in Pernambuco State-Brazil","volume":"252","author":"Souza","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2013.02.023","article-title":"Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data","volume":"134","author":"Zhang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.scitotenv.2018.02.200","article-title":"Multi-scale assessments of droughts: A case study in Xinjiang, China","volume":"630","author":"Yao","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100393","DOI":"10.1016\/j.wace.2021.100393","article-title":"Seasonal and aridity influences on the relationships between drought indices and hydrological variables over China","volume":"34","author":"Xu","year":"2021","journal-title":"Weather. Clim. Extrem."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep37747","article-title":"Drought rapidly diminishes the large net CO2 uptake in 2011 over semi-arid Australia","volume":"6","author":"Ma","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_16","first-page":"102418","article-title":"Drought sensitivity of vegetation photosynthesis along the aridity gradient in northern China","volume":"102","author":"Xu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","unstructured":"Palmer, W.C. (1965). Meteorological Drought."},{"key":"ref_18","unstructured":"McKee, T.B., Doesken, N.J., and Kleist, J. (1993, January 17\u201322). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, Anaheim, CA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1175\/1520-0477(1995)076<0655:DOTLIT>2.0.CO;2","article-title":"Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data","volume":"76","author":"Kogan","year":"1995","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/0273-1177(95)00079-T","article-title":"Application of vegetation index and brightness temperature for drought detection","volume":"15","author":"Kogan","year":"1995","journal-title":"Adv. Space Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1029\/2002EO000382","article-title":"World droughts in the new millennium from AVHRR\u2014Based vegetation health indices. Eos","volume":"83","author":"Kogan","year":"2002","journal-title":"Trans. Am. Geophys. Union"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(01)00274-7","article-title":"A simple interpretation of the surface temperature\/vegetation index space for assessment of surface moisture status","volume":"79","author":"Sandholt","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.rse.2017.05.041","article-title":"The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations","volume":"198","author":"Sadeghi","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3748","DOI":"10.3390\/rs13183748","article-title":"Drought monitoring over Yellow River basin from 2003\u20132019 using reconstructed MODIS land surface temperature in Google Earth Engine","volume":"13","author":"Zhao","year":"2021","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1175\/2009JCLI2909.1","article-title":"A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index","volume":"23","year":"2010","journal-title":"J. Clim."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2875","DOI":"10.1016\/j.rse.2010.07.005","article-title":"Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data","volume":"114","author":"Rhee","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4998","DOI":"10.3390\/rs6064998","article-title":"Characterization of drought development through remote sensing: A case study in Central Yunnan, China","volume":"6","author":"Abbas","year":"2014","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"287","DOI":"10.3390\/rs8040287","article-title":"A new Soil Moisture Agricultural Drought Index (SMADI) integrating MODIS and SMOS products: A case of study over the Iberian Peninsula","volume":"8","author":"Piles","year":"2016","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/nclimate2067","article-title":"Global warming and changes in drought","volume":"4","author":"Trenberth","year":"2014","journal-title":"Nat. Clim. Change"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1038\/nature11575","article-title":"Little change in global drought over the past 60 years","volume":"491","author":"Sheffield","year":"2012","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1038\/nclimate1633","article-title":"Increasing drought under global warming in observations and models","volume":"3","author":"Dai","year":"2013","journal-title":"Nat. Clim. Change"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4781","DOI":"10.1002\/joc.6489","article-title":"Spatiotemporal variability of standardized precipitation evapotranspiration index in mainland China over 1961\u20132016","volume":"40","author":"Wu","year":"2020","journal-title":"Int. J. Climatol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.scitotenv.2016.11.098","article-title":"Does drought in China show a significant decreasing trend from 1961 to 2009?","volume":"579","author":"Wang","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s11069-013-0909-2","article-title":"Spatial and temporal analysis of drought risk during the crop-growing season over northeast China","volume":"71","author":"Yu","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-17810-3","article-title":"Spatial-temporal variation of drought in China from 1982 to 2010 based on a modified temperature vegetation drought index (mTVDI)","volume":"7","author":"Zhao","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_36","first-page":"50","article-title":"Evolution characteristics of seasonal drought in the south of China during the past 58 years based on standardized precipitation index","volume":"26","author":"Huang","year":"2010","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1016\/j.scitotenv.2016.07.096","article-title":"Combined use of meteorological drought indices at multi-time scales for improving hydrological drought detection","volume":"571","author":"Zhu","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s40808-021-01102-x","article-title":"Analyzing the extent of drought in the Rajasthan state of India using vegetation condition index and standardized precipitation index","volume":"8","author":"Mishra","year":"2022","journal-title":"Modeling Earth Syst. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"20408","DOI":"10.1007\/s11356-020-12120-0","article-title":"Comparative evaluation of drought indices for monitoring drought based on remote sensing data","volume":"28","author":"Wei","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"21085","DOI":"10.1007\/s11356-020-12124-w","article-title":"Remote sensing strategies to characterization of drought, vegetation dynamics in relation to climate change from 1983 to 2016 in Tibet and Xinjiang Province, China","volume":"28","author":"Zhang","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"21910","DOI":"10.1007\/s11356-020-12146-4","article-title":"Monitoring drought events and vegetation dynamics in relation to climate change over mainland China from 1983 to 2016","volume":"28","author":"Ali","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6487","DOI":"10.1109\/JSTARS.2021.3084849","article-title":"The added-value of remotely-sensed soil moisture data for agricultural drought detection in Argentina","volume":"14","author":"Salvia","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"150066","DOI":"10.1038\/sdata.2015.66","article-title":"The climate hazards infrared precipitation with stations\u2014A new environmental record for monitoring extremes","volume":"2","author":"Funk","year":"2015","journal-title":"Sci. Data"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1029\/2018JD030007","article-title":"Spatiotemporal variability in land surface temperature over the mountainous region affected by the 2008 Wenchuan earthquake from 2000 to 2017","volume":"124","author":"Zhao","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"111931","DOI":"10.1016\/j.rse.2020.111931","article-title":"Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS\/Terra land products and MSG geostationary satellite data","volume":"247","author":"Zhao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.3390\/rs13132510","article-title":"Mapping winter crops using a phenology algorithm, time-series Sentinel-2 and Landsat-7\/8 images, and Google Earth Engine","volume":"13","author":"Pan","year":"2021","journal-title":"Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.3390\/rs14041004","article-title":"Mapping the northern limit of double cropping using a phenology-based algorithm and Google Earth Engine","volume":"14","author":"Guo","year":"2022","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s41748-021-00213-w","article-title":"Investigating decadal changes of multiple hydrological products and land-cover changes in the Mediterranean Region for 2009\u20132018","volume":"5","author":"Li","year":"2021","journal-title":"Earth Syst. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2017.12","article-title":"A land data assimilation system for sub-Saharan Africa food and water security applications","volume":"4","author":"McNally","year":"2017","journal-title":"Sci. Data"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2017.191","article-title":"TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958\u20132015","volume":"5","author":"Abatzoglou","year":"2018","journal-title":"Sci. Data"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2019.03.015","article-title":"Estimating reservoir evaporation losses for the United States: Fusing remote sensing and modeling approaches","volume":"226","author":"Zhao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111395","DOI":"10.1016\/j.rse.2019.111395","article-title":"Divergent shifts in peak photosynthesis timing of temperate and alpine grasslands in China","volume":"233","author":"Yang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"111290","DOI":"10.1016\/j.rse.2019.111290","article-title":"Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China","volume":"232","author":"Fang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"237199","DOI":"10.22161\/ijaers.4.6.12","article-title":"Proposing a popular method for meteorological drought monitoring in the Kabul River Basin, Afghanistan","volume":"4","author":"Alami","year":"2017","journal-title":"Int. J. Adv. Eng. Res. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.rse.2018.04.001","article-title":"A fusion-based methodology for meteorological drought estimation using remote sensing data","volume":"211","author":"Alizadeh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"143530","DOI":"10.1016\/j.scitotenv.2020.143530","article-title":"Performance and relationship of four different agricultural drought indices for drought monitoring in China\u2019s mainland using remote sensing data","volume":"759","author":"Javed","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.agrformet.2019.01.008","article-title":"A new multi-sensor integrated index for drought monitoring","volume":"268","author":"Jiao","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"140701","DOI":"10.1016\/j.scitotenv.2020.140701","article-title":"Copula-based Joint Drought Index using SPI and EDDI and its application to climate change","volume":"744","author":"Won","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_59","first-page":"1571","article-title":"A drought climatology for Europe","volume":"22","author":"Saunders","year":"2002","journal-title":"Int. J. Climatol. A J. R. Meteorol. Soc."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1175\/1520-0477(2001)082<1949:OSTFGV>2.3.CO;2","article-title":"Operational space technology for global vegetation assessment","volume":"82","author":"Kogan","year":"2001","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1175\/1520-0477(1997)078<0621:GDWFS>2.0.CO;2","article-title":"Global drought watch from space","volume":"78","author":"Kogan","year":"1997","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"147803","DOI":"10.1016\/j.scitotenv.2021.147803","article-title":"Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China","volume":"789","author":"Liu","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.ecoinf.2017.03.005","article-title":"Characterization of droughts during 2001\u20132014 based on remote sensing: A case study of Northeast China","volume":"39","author":"Cong","year":"2017","journal-title":"Ecol. Inform."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.agrformet.2018.04.022","article-title":"Integrated remote sensing approach to global agricultural drought monitoring","volume":"259","author":"Piles","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.3390\/s7081612","article-title":"An overview of the \u201ctriangle method\u201d for estimating surface evapotranspiration and soil moisture from satellite imagery","volume":"7","author":"Carlson","year":"2007","journal-title":"Sensors"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.gloplacha.2012.10.014","article-title":"Analysis of changes in meteorological variables using Mann-Kendall and Sen\u2019s slope estimator statistical tests in Serbia","volume":"100","author":"Gocic","year":"2013","journal-title":"Glob. Planet. Change"},{"key":"ref_67","first-page":"245","article-title":"Nonparametric tests against trend","volume":"13","author":"Mann","year":"1945","journal-title":"Econom. J. Econom. Soc."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1080\/01621459.1968.10480934","article-title":"Estimates of the regression coefficient based on Kendall\u2019s tau","volume":"63","author":"Sen","year":"1968","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_69","first-page":"110","article-title":"Trend analysis using Mann-Kendall, Sen\u2019s slope estimator test and innovative trend analysis method in Yangtze river basin, China","volume":"8","author":"Ali","year":"2019","journal-title":"Int. J. Eng. Technol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.3390\/w11091855","article-title":"Long-term trends and seasonality detection of the observed flow in Yangtze River using Mann-Kendall and Sen\u2019s innovative trend method","volume":"11","author":"Ali","year":"2019","journal-title":"Water"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"6945","DOI":"10.5194\/amt-13-6945-2020","article-title":"Effects of the prewhitening method, the time granularity, and the time segmentation on the Mann\u2013Kendall trend detection and the associated Sen\u2019s slope","volume":"13","author":"Andrews","year":"2020","journal-title":"Atmos. Meas. Tech."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s00382-016-3110-y","article-title":"Spatio-statistical analysis of temperature fluctuation using Mann\u2013Kendall and Sen\u2019s slope approach","volume":"48","author":"Dawood","year":"2017","journal-title":"Clim. Dyn."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1007\/s11069-015-1644-7","article-title":"Rainfall and river flow trends using Mann\u2013Kendall and Sen\u2019s slope estimator statistical tests in the Cobres River basin","volume":"77","author":"Santos","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"205","DOI":"10.54302\/mausam.v68i2.604","article-title":"Analysis of rainfall by using Mann-Kendall trend, Sen\u2019s slope and variability at five districts of south Gujarat, India","volume":"68","author":"Kumar","year":"2017","journal-title":"Mausam"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"115011","DOI":"10.1088\/2515-7620\/ac39f7","article-title":"The trend towards a warmer and wetter climate observed in arid and semi-arid areas of northwest China from 1959 to 2019","volume":"3","author":"Zheng","year":"2021","journal-title":"Environ. Res. Commun."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"103348","DOI":"10.1016\/j.earscirev.2020.103348","article-title":"Challenges for drought assessment in the Mediterranean region under future climate scenarios. Earth-Science Reviews","volume":"210","author":"Tramblay","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.atmosres.2015.08.017","article-title":"Why does precipitation in northwest China show a significant increasing trend from 1960 to 2010?","volume":"167","author":"Li","year":"2016","journal-title":"Atmos. Res."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1007\/s10113-014-0723-8","article-title":"Relationships between drought disasters and crop production during ENSO episodes across the North China Plain","volume":"15","author":"Liu","year":"2015","journal-title":"Reg. Environ. Change"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2886","DOI":"10.1002\/joc.4526","article-title":"Temporal dynamics and spatial patterns of drought and the relation to ENSO: A case study in Northwest China","volume":"36","author":"Liu","year":"2016","journal-title":"Int. J. Climatol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"4020","DOI":"10.1002\/2014GL060130","article-title":"Increasing autumn drought over southern China associated with ENSO regime shift","volume":"41","author":"Zhang","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.3390\/w13192761","article-title":"Study on the Spatial and Temporal Characteristics of Mesoscale Drought in China under Future Climate Change Scenarios","volume":"13","author":"Gong","year":"2021","journal-title":"Water"},{"key":"ref_82","first-page":"1","article-title":"A statistical tool to generate potential future climate scenarios for hydrology applications","volume":"2020","year":"2020","journal-title":"Sci. Program."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.jhydrol.2015.05.003","article-title":"A review of water scarcity and drought indexes in water resources planning and management","volume":"527","author":"Solera","year":"2015","journal-title":"J. Hydrol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1570\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:42:39Z","timestamp":1760136159000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1570"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,24]]},"references-count":83,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14071570"],"URL":"https:\/\/doi.org\/10.3390\/rs14071570","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,24]]}}}