{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:44:01Z","timestamp":1775069041989,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2016,8,23]],"date-time":"2016-08-23T00:00:00Z","timestamp":1471910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41401416"],"award-info":[{"award-number":["41401416"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Agriculture Science and Technology Innovation Fund","award":["CX(14)5073"],"award-info":[{"award-number":["CX(14)5073"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper works on the agricultural drought forecasting in the Guanzhong Plain of China using Autoregressive Integrated Moving Average (ARIMA) models based on the time series of drought monitoring results of Vegetation Temperature Condition Index (VTCI). About 90 VTCI images derived from Advanced Very High Resolution Radiometer (AVHRR) data were selected to develop the ARIMA models from the erecting stage to the maturity stage of winter wheat (early March to late May in each year at a ten-day interval) of the years from 2000 to 2009. We take the study area overlying on the administration map around the study area, and divide the study area into 17 parts where at least one weather station is located in each part. The pixels where the 17 weather stations are located are firstly chosen and studied for their fitting models, and then the best models for all pixels of the whole area are determined. According to the procedures for the models\u2019 development, the selected best models for the 17 pixels are identified and the forecast is done with three steps. The forecasting results of the ARIMA models were compared with the monitoring ones. The results show that with reference to the categorized VTCI drought monitoring results, the categorized forecasting results of the ARIMA models are in good agreement with the monitoring ones. The categorized drought forecasting results of the ARIMA models are more severity in the northeast of the Plain in April 2009, which are in good agreements with the monitoring ones. The absolute errors of the AR(1) models are lower than the SARIMA models, both in the frequency distributions and in the statistic results. However, the ability of SARIMA models to detect the changes of the drought situation is better than the AR(1) models. These results indicate that the ARIMA models can better forecast the category and extent of droughts and can be applied to forecast droughts in the Plain.<\/jats:p>","DOI":"10.3390\/rs8090690","type":"journal-article","created":{"date-parts":[[2016,8,23]],"date-time":"2016-08-23T10:18:55Z","timestamp":1471947535000},"page":"690","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Drought Forecasting with Vegetation Temperature Condition Index Using ARIMA Models in the Guanzhong Plain"],"prefix":"10.3390","volume":"8","author":[{"given":"Miao","family":"Tian","sequence":"first","affiliation":[{"name":"Institute of Agricultural Economics and Information, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China"}]},{"given":"Pengxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geographic Information Engineering, College of Information and Electrical Engineering, China Agricultural University, East Campus, Beijing 100083, China"}]},{"given":"Jahangir","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Geographic Information Engineering, College of Information and Electrical Engineering, China Agricultural University, East Campus, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1002\/met.1533","article-title":"Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms","volume":"23","author":"Valipour","year":"2015","journal-title":"Meteorol. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"204","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_3","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/0022-1694(91)90055-M","article-title":"On the physics of drought. I. A conceptual framework","volume":"129","author":"Bravar","year":"1991","journal-title":"J. Hydrol."},{"key":"ref_4","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 Eighth Conference on Applied Climatology, Anaheim, CA, USA."},{"key":"ref_5","unstructured":"Palmer, W.C. (1965). Meteorological Drought."},{"key":"ref_6","first-page":"1","article-title":"Estimation of actual evapotranspiration by using MODIS images (a case study: Tajan catchment)","volume":"61","author":"Rahimi","year":"2014","journal-title":"Arch. Agron. Soil Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8337","DOI":"10.3390\/rs6098337","article-title":"NDVI-based long-term vegetation dynamics and its response to climatic change in Mongolian Plateau","volume":"6","author":"Bao","year":"2014","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/0034-4257(95)00209-X","article-title":"The effect of microphytes on the spectral reflectance of vegetation in semiarid regions","volume":"57","author":"Karnieli","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2006.06.003","article-title":"A comparative study of NOAA-AVHRR derived drought indices using change vector analysis","volume":"105","author":"Bayarjargal","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1080\/01431168908903997","article-title":"Comparing SMMR and AVHRR data for drought monitoring","volume":"10","author":"Tucker","year":"1989","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3471","DOI":"10.1080\/01431169308904458","article-title":"Environmental monitoring and crop forecasting in the Sahel through the use of NOAA NDVI data. A case study: Niger 1986\u201389","volume":"14","author":"Maselli","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1007\/s40333-014-0080-y","article-title":"Agricultural irrigation requirements under future climate scenarios in China","volume":"7","author":"Zhu","year":"2015","journal-title":"J. Arid Land"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1006\/jare.1999.0607","article-title":"Remote sensing of the seasonal variability of vegetation in a semi-arid environment","volume":"45","author":"Schmidt","year":"2000","journal-title":"J. Arid Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1080\/01431161.2014.916444","article-title":"Satellites-based detection of water surface variation in China\u2019s largest freshwater lake in response to hydro-climatic drought","volume":"35","author":"Wu","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2446","DOI":"10.1175\/JHM-D-13-0132.1","article-title":"Assimilation of remotely sensed soil moisture and snow depth retrievals for drought estimation","volume":"15","author":"Kumar","year":"2014","journal-title":"J. Hydrometeorol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1080\/01431168608948914","article-title":"Global vegetation dynamics: Satellite observations over Asia","volume":"7","author":"Malingreau","year":"1986","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/0034-4257(87)90040-X","article-title":"Satellite remote-sensing of drought conditions","volume":"23","author":"Tucker","year":"1987","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2931","DOI":"10.1080\/014311697217134","article-title":"Interlinkages of NOAA\/AVHRR derived integrated NDVI to seasonal precipitation and transpiration in dryland tropics","volume":"18","author":"Srivastava","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3483","DOI":"10.1080\/01431160010006430","article-title":"ENSO drought onset prediction in northeast Brazil using NDVI","volume":"22","author":"Liu","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S0034-4257(03)00174-3","article-title":"Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices","volume":"87","author":"Ji","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1080\/01431169008955102","article-title":"Remote sensing of weather impacts on vegetation in non-homogeneous areas","volume":"11","author":"Kogan","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","first-page":"71","article-title":"Drought monitoring with NDVI-based standardized vegetation index","volume":"68","author":"Peters","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1175\/1520-0442(1990)003<0282:TMDFS>2.0.CO;2","article-title":"Towards monitoring droughts from space","volume":"3","author":"Gutman","year":"1990","journal-title":"J. Clim."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/0143116031000115328","article-title":"Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA","volume":"25","author":"Wan","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5133","DOI":"10.1080\/01431160802036557","article-title":"Using the vegetation temperature condition index for time series drought occurrence monitoring in the Guanzhong Plain, PR China","volume":"29","author":"Sun","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/014311697219286","article-title":"Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site","volume":"18","author":"Goetz","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1175\/1520-0450(1989)028<0276:EORSRT>2.0.CO;2","article-title":"Developing satellite derived estimates of surface moisture status","volume":"28","author":"Nemani","year":"1993","journal-title":"J. Appl. Meteorol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1109\/36.58983","article-title":"Using spatial context in satellite data to infer regional scale evapotranspiration","volume":"28","author":"Price","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/02757259409532220","article-title":"A method to make use of thermal infrared temperature and NDVI measurements to infer soil water content and fractional vegetation cover","volume":"9","author":"Carlson","year":"1994","journal-title":"Remote Sens. Reviews."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1175\/1520-0450(1995)034<0745:TRSOSS>2.0.CO;2","article-title":"Thermal remote sensing of surface soil water content with partial vegetation cover for incorporating into climate models","volume":"34","author":"Gillies","year":"1995","journal-title":"J. Appl. Meteorol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3145","DOI":"10.1080\/014311697217026","article-title":"A verification of the \u201ctriangle\u201d method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature","volume":"18","author":"Gillies","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/0034-4257(94)90020-5","article-title":"Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index","volume":"49","author":"Moran","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1080\/014311697219196","article-title":"Estimating local sugarcane evapotranspiration using Landsat TM image and a VITT concept","volume":"18","author":"Yang","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1029\/WR017i004p01133","article-title":"Canopy temperature as a crop water stress indicator","volume":"17","author":"Jackson","year":"1981","journal-title":"Water Resour. Res."},{"key":"ref_35","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_36","first-page":"412","article-title":"Vegetation temperature condition index and its application for drought monitoring","volume":"26","author":"Wang","year":"2001","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_37","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 the surface moisture status","volume":"79","author":"Sandholt","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_38","first-page":"186","article-title":"Applicability of vegetation temperature index for drought monitoring at different time scales","volume":"33","author":"Lin","year":"2016","journal-title":"Arid Zone Res."},{"key":"ref_39","first-page":"600","article-title":"Classification of drought grades based on vegetation temperature condition index","volume":"27","author":"Zhang","year":"2010","journal-title":"Arid Zone Res."},{"key":"ref_40","first-page":"446","article-title":"Estimating soil water in northern China based on vegetation temperature condition index (VTCI)","volume":"35","author":"Wang","year":"2012","journal-title":"Arid Land Geogr."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1111\/j.1752-1688.1997.tb03560.x","article-title":"An early warning system for drought management using the Palmer drought index","volume":"33","author":"Lohani","year":"1997","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/0022-1694(84)90212-9","article-title":"Analysis and modeling of Palmer Drought Index series","volume":"68","author":"Rao","year":"1984","journal-title":"J. Hydrol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/0022-1694(90)90149-R","article-title":"Critical drought analysis by 2nd-order Markov-chain","volume":"120","author":"Sen","year":"1990","journal-title":"J. Hydrol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1061\/(ASCE)1084-0699(2003)8:6(319)","article-title":"Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks","volume":"8","author":"Kim","year":"2003","journal-title":"J. Hydrol. Eng."},{"key":"ref_45","unstructured":"Box, G.E.P., Jenkins, G.M., and Reinsel, G.C. (1970). Time Series Analysis. Forecasting and Control, Prentice Hall."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/S0965-8564(99)00032-4","article-title":"Adaptive estimation of daily demands with complex calendar effects for freight transportation","volume":"34","author":"Godfrey","year":"2000","journal-title":"Transp. Res. B Method"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1002\/met.1491","article-title":"Long-term runoff study using SARIMA and ARIMA models in the United States","volume":"22","author":"Valipour","year":"2015","journal-title":"Meteorol. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.jhydrol.2012.11.017","article-title":"Comparison of ARMA, ARIMA and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir","volume":"476","author":"Valipour","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1631\/jzus.A0820042","article-title":"Adaptive load forecasting of the Hellenic electric grid","volume":"9","author":"Pappas","year":"2008","journal-title":"J. Zhejiang Univ. Sci. A"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1016\/j.mcm.2009.10.031","article-title":"Drought forecasting based on the remote sensing data using ARIMA models","volume":"51","author":"Han","year":"2010","journal-title":"Math. Comput. Model."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1111\/j.1467-9892.1982.tb00349.x","article-title":"An approach to time series smoothing and forecasting using the EM algorithm","volume":"3","author":"Shumway","year":"1982","journal-title":"J. Time Ser. Anal."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Trans. Autom. Control."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1109\/JSTARS.2011.2179637","article-title":"Addressing the effects of canopy structure on the remote sensing of foliar chemistry of a 3-Dimensional, radiometrically porous surface","volume":"5","author":"Niemann","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","unstructured":"Boor, C.D. (1978). A Practical Guide to Splines, Springer-Verlag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/9\/690\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:29:00Z","timestamp":1760210940000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/9\/690"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,8,23]]},"references-count":54,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2016,9]]}},"alternative-id":["rs8090690"],"URL":"https:\/\/doi.org\/10.3390\/rs8090690","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,8,23]]}}}