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THIS IS PARTICULARLY TRUE IN INDIA  WHERE CLIMATE CHANGE IMPACTS ARE EXPECTED TO BE ESPECIALLY LARGE  WITH UP TO A 30% LOSS IN YIELD FOR SOME STAPLE CROPS BY MID-CENTURY (LOBELL ET AL. 2008). FURTHERMORE  OVER 40% OF AGRICULTURAL PRODUCTION RELIES ON GROUNDWATER IRRIGATION  HOWEVER GROUNDWATER RESERVES ARE BECOMING RAPIDLY DEPLETED  WITH SOME STUDIES ESTIMATING THAT A LARGE PROPORTION OF DEEP WELLS WILL DRY UP BY MID-CENTURY (SHAH ET AL. 2009). WHILE THE IMPACTS OF ENVIRONMENTAL CHANGE ON PRODUCTION HAVE BEEN WELL ESTABLISHED  THERE IS LITTLE UNDERSTANDING OF HOW FARMERS RESPOND TO THIS CHANGE. YET  IT IS IMPORTANT TO ACCOUNT FOR FARMER BEHAVIOR AS FARMERS MAY BE ABLE TO REDUCE OR ELIMINATE THE NEGATIVE IMPACTS OF ENVIRONMENTAL CHANGE BY ADAPTING THEIR CROPPING PRACTICES. FOR EXAMPLE  FARMERS MAY BE ABLE TO REDUCE THE IMPACT OF WARMING TEMPERATURES BY SWITCHING TO NEW HYBRID CROP VARIETIES THAT ARE MORE HEAT-TOLERANT. UNDERSTANDING HOW  WHY  AND HOW EFFECTIVELY FARMERS MAY ADAPT THEIR CROPPING STRATEGIES TO ENVIRONMENTAL CHANGE WILL BETTER IDENTIFY WHETHER INDIA WILL BE ABLE TO PRODUCE ENOUGH FOOD OVER THE UPCOMING DECADES.THIS PROPOSAL WILL EXAMINE LAND USE AND LAND COVER CHANGE (LCLUC) OF AGRICULTURAL SYSTEMS  AND ATTRIBUTE THESE CHANGES TO LONGTERM ENVIRONMENTAL DRIVERS  LIKE CLIMATE CHANGE AND GROUNDWATER DEPLETION. THIS WILL ALLOW US TO UNDERSTAND HOW EFFECTIVELY FARMERS HAVE ADAPTED TO ENVIRONMENTAL CHANGE  AND HOW VULNERABLE CURRENT AGRICULTURAL SYSTEMS STILL ARE TO FUTURE CHANGE. SPECIFICALLY  WE WILL DERIVE NOVEL REMOTE SENSING PRODUCTS THAT QUANTIFY SMALLHOLDER CROP PRODUCTION  INCLUDING CROPPED AREA AND YIELD FROM 1995 TO THE PRESENT. TO DATE  MAPPING THE PRODUCTION OF SMALLHOLDER FARMS HAS BEEN DIFFICULT FOR SEVERAL REASONS. FIRST  THE SIZE OF INDIVIDUAL FARMS IS SMALLER THAN THE RESOLUTION OF READILY-AVAILABLE SATELLITE IMAGERY  LIKE LANDSAT AND MODIS  LEADING TO ISSUES WITH MIXED PIXELS.FURTHERMORE  GROUND DATA RARELY EXIST TO CALIBRATE MODELS THAT TRANSLATE SATELLITE VEGETATION INDICES TO PRODUCTION MEASURES LIKE YIELD.WE PROPOSE TO DEVELOP UNIQUE METHODS TO OVERCOME THESE PROBLEMS THAT BUILD ON PREVIOUS WORK BY THE PIS (JAIN ET AL. 2013  LOBELL ET AL. 2015). WE WILL ALSO USE REMOTE SENSING TO QUANTIFY ADAPTATION DECISIONS  INCLUDING SHIFTING SOW DATE  SWITCHING CROP VARIETY  AND INCREASING IRRIGATION. WE WILL LINK THESE REMOTE SENSING DATASETS WITH GRIDDED WEATHER  GROUNDWATER DEPTH  AND PANEL HOUSEHOLD DATASETS TO EXAMINE HOW FARMERS ARE RESPONDING TO CLIMATE CHANGE AND GROUNDWATER DEPLETION. THIS STUDY WILL BE ONE OF THE FIRST TO IDENTIFY SPECIFIC ADAPTATION STRATEGIES FARMERS ADOPT IN RESPONSE TO MEDIUM TO LONG-TERM ENVIRONMENTAL CHANGE. WE WILL ALSO EVALUATE HOW EFFECTIVE THESE STRATEGIES ARE IN BOLSTERING FUTURE FOOD SECURITY.","funder_award_id":"NNX17AH97G","funder_id":"https://openalex.org/F4320306101","funder_display_name":"National Aeronautics and Space Administration"}],"funders":[{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3160732124.pdf"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1489564417","https://openalex.org/W1964050442","https://openalex.org/W1974714523","https://openalex.org/W1986738039","https://openalex.org/W1994378716","https://openalex.org/W2000181981","https://openalex.org/W2009542758","https://openalex.org/W2023336635","https://openalex.org/W2059523177","https://openalex.org/W2066122301","https://openalex.org/W2073861886","https://openalex.org/W2086418613","https://openalex.org/W2100967854","https://openalex.org/W2125763679","https://openalex.org/W2149813070","https://openalex.org/W2184568779","https://openalex.org/W2262752710","https://openalex.org/W2283002322","https://openalex.org/W2532003389","https://openalex.org/W2550139446","https://openalex.org/W2561368607","https://openalex.org/W2582743722","https://openalex.org/W2725897987","https://openalex.org/W2746044348","https://openalex.org/W2767166343","https://openalex.org/W2793603191","https://openalex.org/W2803901105","https://openalex.org/W2890942070","https://openalex.org/W2895854890","https://openalex.org/W2943472941","https://openalex.org/W2954664657","https://openalex.org/W2959328963","https://openalex.org/W2977267463","https://openalex.org/W3047450819","https://openalex.org/W3086184474","https://openalex.org/W3125947825","https://openalex.org/W3130550871","https://openalex.org/W6629272072"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W2355956201","https://openalex.org/W148178222","https://openalex.org/W2104657898","https://openalex.org/W1948992892","https://openalex.org/W1886884218","https://openalex.org/W3201212491","https://openalex.org/W4380767732","https://openalex.org/W4247744717","https://openalex.org/W3161605631"],"abstract_inverted_index":{"Remote":[0],"sensing":[1],"offers":[2],"a":[3,83],"way":[4],"to":[5,61,93,157,174,204],"map":[6,175,205],"crop":[7,18,65,110,176,206],"types":[8,19,66,111,177,207],"across":[9],"large":[10],"spatio-temporal":[11],"scales":[12],"at":[13,178],"low":[14],"costs.":[15],"However,":[16],"mapping":[17],"is":[20],"challenging":[21],"in":[22,30,72,134,184,208],"heterogeneous,":[23],"smallholder":[24,135,186,209],"farming":[25],"systems,":[26],"such":[27],"as":[28],"those":[29],"India,":[31],"where":[32],"field":[33],"sizes":[34],"are":[35],"often":[36],"smaller":[37],"than":[38],"the":[39,50,94,100,113,140,146,179,197,200],"resolution":[40,125],"of":[41,52,102,142,199],"historically":[42],"available":[43,166],"imagery.":[44],"In":[45],"this":[46,191],"study,":[47],"we":[48,150,162],"examined":[49,139],"potential":[51],"relatively":[53],"new,":[54],"high-resolution":[55],"imagery":[56,171],"(Sentinel-1,":[57],"Sentinel-2,":[58,168],"and":[59,70,99,149,169],"PlanetScope)":[60],"identify":[62],"four":[63],"major":[64],"(maize,":[67],"mustard,":[68],"tobacco,":[69],"wheat)":[71],"eastern":[73],"India":[74],"using":[75],"support":[76],"vector":[77],"machine":[78],"(SVM).":[79],"We":[80,137],"found":[81,151,163],"that":[82,87,152,164],"trained":[84],"SVM":[85],"model":[86],"included":[88],"all":[89],"three":[90],"sensors":[91],"led":[92],"highest":[95],"classification":[96,147],"accuracy":[97,183],"(85%),":[98],"inclusion":[101],"Planet":[103,170],"data":[104],"was":[105,119],"particularly":[106],"helpful":[107],"for":[108,112,131,196],"classifying":[109],"smallest":[114],"farms":[115],"(&lt;600":[116],"m2).":[117],"This":[118],"likely":[120],"because":[121],"its":[122],"higher":[123],"spatial":[124],"(3":[126],"m)":[127],"could":[128],"better":[129],"account":[130],"field-level":[132],"variations":[133],"systems.":[136,187,210],"also":[138],"impact":[141],"image":[143],"timing":[144],"on":[145],"accuracy,":[148],"early-season":[153],"images":[154],"did":[155],"little":[156],"improve":[158],"our":[159],"models.":[160],"Overall,":[161],"readily":[165],"Sentinel-1,":[167],"were":[172],"able":[173],"field-scale":[180],"with":[181],"high":[182],"Indian":[185],"The":[188],"findings":[189],"from":[190],"study":[192],"have":[193],"important":[194],"implications":[195],"identification":[198],"most":[201],"effective":[202],"ways":[203]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":4}],"updated_date":"2026-05-22T06:13:13.366637","created_date":"2025-10-10T00:00:00"}
