{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T11:58:59Z","timestamp":1772625539948,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,6]],"date-time":"2017-06-06T00:00:00Z","timestamp":1496707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NASA","award":["NNX11AH98G"],"award-info":[{"award-number":["NNX11AH98G"]}]},{"name":"NASA","award":["NNH15ZDA001N"],"award-info":[{"award-number":["NNH15ZDA001N"]}]},{"DOI":"10.13039\/100006785","name":"Google","doi-asserted-by":"publisher","award":["Earth Engine Research Award"],"award-info":[{"award-number":["Earth Engine Research Award"]}],"id":[{"id":"10.13039\/100006785","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["Graduate Research Fellowship"],"award-info":[{"award-number":["Graduate Research Fellowship"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fine-scale agricultural statistics are an important tool for understanding trends in food production and their associated drivers, yet these data are rarely collected in smallholder systems. These statistics are particularly important for smallholder systems given the large amount of fine-scale heterogeneity in production that occurs in these regions. To overcome the lack of ground data, satellite data are often used to map fine-scale agricultural statistics. However, doing so is challenging for smallholder systems because of (1) complex sub-pixel heterogeneity; (2) little to no available calibration data; and (3) high amounts of cloud cover as most smallholder systems occur in the tropics. We develop an automated method termed the MODIS Scaling Approach (MSA) to map smallholder cropped area across large spatial and temporal scales using MODIS Enhanced Vegetation Index (EVI) satellite data. We use this method to map winter cropped area, a key measure of cropping intensity, across the Indian subcontinent annually from 2000\u20132001 to 2015\u20132016. The MSA defines a pixel as cropped based on winter growing season phenology and scales the percent of cropped area within a single MODIS pixel based on observed EVI values at peak phenology. We validated the result with eleven high-resolution scenes (spatial scale of 5 \u00d7 5 m2 or finer) that we classified into cropped versus non-cropped maps using training data collected by visual inspection of the high-resolution imagery. The MSA had moderate to high accuracies when validated using these eleven scenes across India (R2 ranging between 0.19 and 0.89 with an overall R2 of 0.71 across all sites). This method requires no calibration data, making it easy to implement across large spatial and temporal scales, with 100% spatial coverage due to the compositing of EVI to generate cloud-free data sets. The accuracies found in this study are similar to those of other studies that map crop production using automated methods and use no calibration data. To aid research on agricultural production at fine spatial scales in India, we make our annual winter crop maps from 2000\u20132001 to 2015\u20132016 at 1 \u00d7 1 km2 produced in this study publically available through the NASA Socioeconomic Data and Applications Center (SEDAC) hosted by the Center for International Earth Science Information Network (CIESIN) at Columbia University. We also make our R script available since it is likely that this method can be used to map smallholder agriculture in other regions across the globe given that our method performed well in disparate agro-ecologies across India.<\/jats:p>","DOI":"10.3390\/rs9060566","type":"journal-article","created":{"date-parts":[[2017,6,6]],"date-time":"2017-06-06T10:53:09Z","timestamp":1496746389000},"page":"566","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["An Automated Approach to Map Winter Cropped Area of Smallholder Farms across Large Scales Using MODIS Imagery"],"prefix":"10.3390","volume":"9","author":[{"given":"Meha","family":"Jain","sequence":"first","affiliation":[{"name":"School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"given":"Pinki","family":"Mondal","sequence":"additional","affiliation":[{"name":"Center for International Earth Science Information Network, Columbia University, Palisades, NY 10964, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2192-7385","authenticated-orcid":false,"given":"Gillian","family":"Galford","sequence":"additional","affiliation":[{"name":"Gund Institute of Environment and Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT 05405, USA"}]},{"given":"Greg","family":"Fiske","sequence":"additional","affiliation":[{"name":"Woods Hole Research Center, Falmouth, MA 02540, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3332-4621","authenticated-orcid":false,"given":"Ruth","family":"DeFries","sequence":"additional","affiliation":[{"name":"Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY 10027, USA"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.gfs.2016.07.001","article-title":"Synergies and trade-offs for sustainable agriculture: Nutritional yields and climate-resilience for cereal crops in Central India","volume":"11","author":"DeFries","year":"2016","journal-title":"Glob. Food Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"024004","DOI":"10.1088\/1748-9326\/11\/2\/024004","article-title":"More uneven distributions overturn benefits of higher precipitation for crop yields","volume":"11","author":"Fishman","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1126\/science.1204531","article-title":"Climate trends and global crop production since 1980","volume":"333","author":"Lobell","year":"2011","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1126\/science.1185383","article-title":"Food security: The challenge of feeding 9 billion people","volume":"327","author":"Godfray","year":"2010","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1038\/ncomms2296","article-title":"Recent patterns of crop yield growth and stagnation","volume":"3","author":"Ray","year":"2012","journal-title":"Nat. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"583","DOI":"10.2134\/agronj2001.933583x","article-title":"Use of remote-sensing imagery to estimate corn grain yield","volume":"93","author":"Shanahan","year":"2001","journal-title":"Agron. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1080\/01431169308904332","article-title":"The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction","volume":"14","author":"Quarmby","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.rse.2005.03.015","article-title":"Application of MODIS derived parameters for regional crop yield assessment","volume":"97","author":"Doraiswamy","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.rse.2007.07.019","article-title":"Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains","volume":"112","author":"Wardlow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"19680","DOI":"10.1073\/pnas.0701855104","article-title":"The impact of climate change on smallholder and subsistence agriculture","volume":"104","author":"Morton","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2013.02.029","article-title":"Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors","volume":"134","author":"Jain","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jain, M., Srivastava, A.K., Joon, R.K., McDonald, A., Royal, K., Lisaius, M.C., and Lobell, D.B. (2016). Mapping smallholder wheat yields and sowing dates using micro-satellite data. Remote Sens., 8.","DOI":"10.3390\/rs8100860"},{"key":"ref_13","unstructured":"Carletto, C., Jolliffe, D., and Banerjee, R. (2013). The Emperor Has No Data! Agricultural Statistics in Sub-Saharan Africa, World Bank. Available online: http:\/\/mortenjerven.com\/wp-content\/uploads\/2013\/04\/Panel-3-Carletto.pdf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.rse.2014.10.009","article-title":"Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations","volume":"156","author":"Whitcraft","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1080\/01431160903464179","article-title":"Quantifying the area and spatial distribution of double-and triple-cropping croplands in India with multi-temporal MODIS imagery in 2005","volume":"32","author":"Biradar","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"064010","DOI":"10.1088\/1748-9326\/9\/6\/064010","article-title":"Recent cropping frequency, expansion, and abandonment in Mato Grosso, Brazil had selective land characteristics","volume":"9","author":"Spera","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1038\/nature16467","article-title":"Influence of extreme weather disasters on global crop production","volume":"529","author":"Lesk","year":"2016","journal-title":"Nature"},{"key":"ref_18","unstructured":"Gajbhiye, K.S., and Mandal, C. (2000). Agro-Ecological Zones, Their Soil Resource and Cropping Systems, National Bureau of Soil Survey and Land Use Planning. Available online: http:\/\/www.indiawaterportal.org\/sites\/indiawaterportal.org\/files\/01jan00sfm1.pdf."},{"key":"ref_19","first-page":"5","article-title":"Farm size and productivity: Understanding the strengths of smallholders and improving their livelihoods","volume":"46","author":"Chand","year":"2011","journal-title":"Econ. Political Wkly."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1126\/science.1152339","article-title":"Prioritizing climate change adaptation needs for food security in 2030","volume":"319","author":"Lobell","year":"2008","journal-title":"Science"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s10584-005-9042-x","article-title":"Impact of climate change on Indian agriculture: A review","volume":"78","author":"Mall","year":"2006","journal-title":"Clim. Chang."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.agsy.2010.09.004","article-title":"Livelihoods and agro-ecological gradients: A meso-level analysis in the Indo-Gangetic Plains, India","volume":"104","author":"Erenstein","year":"2011","journal-title":"Agric. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.gloenvcha.2014.12.008","article-title":"Understanding the causes and consequences of differential decision-making in adaptation research: Adapting to a delayed monsoon onset in Gujarat, India","volume":"31","author":"Jain","year":"2015","journal-title":"Glob. Environ. Chang."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.rse.2006.11.021","article-title":"Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains","volume":"108","author":"Wardlow","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2146","DOI":"10.1016\/j.rse.2010.04.019","article-title":"A Two-step filtering approach for detecting maize and soybean phenology with time-series MODIS data","volume":"114","author":"Sakamoto","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_26","unstructured":"(2017, January 10). National Food Security Mission Crop Calendar of NFSM Crops, Available online: http:\/\/nfsm.gov.in\/nfmis\/RPT\/CalenderReport.aspx."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_28","first-page":"403","article-title":"Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China","volume":"10","author":"Ren","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.rse.2010.01.010","article-title":"A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data","volume":"114","author":"Vermote","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.gloenvcha.2004.01.001","article-title":"Mapping vulnerability to multiple stressors: Climate change and globalization in India","volume":"14","author":"Leichenko","year":"2004","journal-title":"Glob. Environ. Chang."},{"key":"ref_31","first-page":"4002","article-title":"Crop per drop of diesel? Energy squeeze on India\u2019s smallholder irrigation","volume":"42","author":"Shah","year":"2007","journal-title":"Econ. Political Wkly."},{"key":"ref_32","unstructured":"Division, A.I. (2017, January 10). District Wise Land Use Statistics, Available online: http:\/\/aps.dac.gov.in\/APY\/Index.htm."},{"key":"ref_33","unstructured":"(2016, September 01). Directorate of Economics and Statistics Area Production Yield Dataset-District 2016, Available online: http:\/\/aps.dac.gov.in\/APY\/Public_Report1.aspx."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1111\/gcb.12660","article-title":"Elucidating the impact of temperature variability and extremes on cereal croplands through remote sensing","volume":"21","author":"Duncan","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2005.10.004","article-title":"Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images","volume":"100","author":"Xiao","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.rse.2015.04.021","article-title":"A scalable satellite-based crop yield mapper","volume":"164","author":"Lobell","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_37","unstructured":"Planet Team (2017, February 01). Planet Application Program Interface: In Space for Life on Earth. Available online: https:\/\/api.planet.com."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/6\/566\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:38:09Z","timestamp":1760207889000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/6\/566"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,6]]},"references-count":37,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2017,6]]}},"alternative-id":["rs9060566"],"URL":"https:\/\/doi.org\/10.3390\/rs9060566","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,6,6]]}}}