High resolution satellite imaging sensors for precision agriculture
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摘要
The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980 s. Various types of remote sensors carried on groundbased platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970 s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications,including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture are discussed.
The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980 s. Various types of remote sensors carried on groundbased platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970 s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications,including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture are discussed.
The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980 s. Various types of remote sensors carried on groundbased platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970 s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications,including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture are discussed.
引文
1.Bramley R G V.Lessons from nearly 20 years of precision agriculture research,development,and adoption as a guide to its appropriate application.Crop&Pasture Science,2009,60(3):197-217
2.Srinivasan A.Handbook of precision agriculture:principles and applications.Boca Raton,Florida:CRC Press,2009
3.Robertson M J,Llewellyn R S,Mandel R,Lawes R,Bramley R GV,Swift L,Metz N,O’Callaghan C.Adoption of variable rate technology in the Australian grains industry:status,issues and prospects.Precision Agriculture,2011,13:181-199
4.Zhang Q.Precision agriculture technology for crop farming.Boca Raton,Florida:CRC Press,2016
5.Schimmelpfennig D,Ebel R.On the doorstep of the information age:recent adoption of precision agriculture.Publication No.EIB-80.Washington,DC:U.S.Department of Agriculture,Economic Research Service,2011
6.Yang C,Sui R,Lee W S.Precision agriculture in large-scale mechanized farming.In:Zhang Q,ed.Precision agriculture technology for crop farming.Boca Raton,Florida:CRC Press,2016,177-211
7.Mulla D J.Twenty five years of remote sensing in precision agriculture-key advances and remaining knowledge gaps.Biosystems Engineering,2013,114(4):358-371
8.Morain S A,Williams D L.Wheat production estimates using satellite images.Agronomy Journal,1975,67(3):361-364
9.Ryerson R A,Dobbins R N,Thibault C.Timely crop area estimates from Landsat.Photogrammetric Engineering and Remote Sensing,1985,51:1735-1743
10.Büttner G,Csillag F.Comparative study of crop and soil mapping using multitemporal and multispectral SPOT and Landsat Thematic Mapper data.Remote Sensing of Environment,1989,29(3):241-249
11.Oettera D R,Cohenb W B,Berterretchea M,Maierspergera T K,Kennedya R E.Land cover mapping in an agricultural setting using multiseasonal Thematic Mapper data.Remote Sensing of Environment,2001,76(2):139-155
12.Murakami T,Ogawa S,Ishitsuka N,Kumagai K,Saito G.Crop discrimination with multitemporal SPOT/HRV data in the Saga Plains,Japan.International Journal of Remote Sensing,2001,22(7):1335-1348
13.Mausel P W,Everitt J H,Escobar D E,King D J.Airborne videography:current status and future perspectives.Photogrammetric Engineering and Remote Sensing,1992,58:1189-1195
14.King D J.Airborne multispectral digital camera and video sensors:a critical review of systems designs and applications.Canadian Journal of Remote Sensing,1995,21(3):245-273
15.Moran M S,Inoue Y,Barnes E M.Opportunities and limitations for image-based remote sensing in precision crop management.Remote Sensing of Environment,1997,61(3):319-346
16.Barnes E M,Sudduth K A,Hummel J W,Lesch S M,Corwin D L,Yang C,Daughtry C S T,Bausch W C.Remote-and ground-based sensor techniques to map soil properties.Photogrammetric Engineering and Remote Sensing,2003,69(6):619-630
17.Varvel G E,Schlemmer M R,Schepers J S.Relationship between spectral data from an aerial image and soil organic matter and phosphorus levels.Precision Agriculture,1999,1(3):291-300
18.Yang C,Everitt J H.Relationships between yield monitor data and airborne multidate multispectral digital imagery for grain sorghum.Precision Agriculture,2002,3(4):373-388
19.Inman D,Khosla R,Reich R,Westfall D G.Normalized difference vegetation index and soil color-based management zones in irrigated maize.Agronomy Journal,2008,100(1):60-66
20.Backoulou G F,Elliott N C,Giles K L,Mirik M.Processed multispectral imagery differentiates wheat crop stress caused by greenbug from other causes.Computers and Electronics in Agriculture,2015,115:34-39
21.Yang C,Odvody G N,Thomasson J A,Isakeit T,Nichols R L.Change detection of cotton root rot infection over 10-year intervals using airborne multispectral imagery.Computers and Electronics in Agriculture,2016,123:154-162
22.Cohen Y,Alchanatis V,Saranga Y,Rosenberg O,Sela E,Bosak A.Mapping water status based on aerial thermal imagery:comparison of methodologies for upscaling from a single leaf to commercial fields.Precision Agriculture,2017,18(5):801-822
23.Bajwa S G,Tian L F.Soil fertility characterization in agricultural fields using hyperspectral remote sensing.Transactions of the ASAE.American Society of Agricultural Engineers,2005,48(6):2399-2406
24.Goel P K,Prasher S O,Landry J A,Patel R M,Viau A A,Miller J R.Estimation of crop biophysical parameters through airborne and field hyperspectral remote sensing.Transactions of the ASAE(American Society of Agricultural Engineers),2003,46(4):1235-1246
25.Zarco-Tejada P J,Ustin S L,Whiting M L.Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery.Agronomy Journal,2005,97(3):641-653
26.Yang C,Everitt J H,Bradford J M.Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield.Precision Agriculture,2007,8(6):279-296
27.Fitzgerald G J,Maas S J,Detar W R.Spidermite detection in cotton using hyperspectral imagery and spectral mixture analysis.Precision Agriculture,2004,5:275-289
28.Kumar A,Lee W S,Ehsani M R,Albrigo L G,Yang C,Mangan R L.Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques.Journal of Applied Remote Sensing,2012,6(1):063542
29.Li H,Lee W S,Wang K,Ehsani R,Yang C.Extended spectral angle mapping(ESAM)for citrus greening disease detection using airborne hyperspectral imaging.Precision Agriculture,2014,15(2):162-183
30.MacDonald S L,Staid M,Staid M,Cooper M L.Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards.Computers and Electronics in Agriculture,2016,130:109-117
31.Johnson L F,Roczen D E,Youkhana S K,Nemani R R,Bosch D F.Mapping vineyard leaf area with multispectral satellite imagery.Computers and Electronics in Agriculture,2003,38(1):33-44
32.Dobermann A,Ping J L.Geostatistical integration of yield monitor data and remote sensing improves yield maps.Agronomy Journal,2004,96(1):285-297
33.Sullivan D G,Shaw J N,Rickman D.IKONOS imagery to estimate surface soil property variability in two Alabama physiographies.Soil Science Society of America Journal,2005,69(6):1789-1798
34.Ping J L,Ferguson R B,Dobermann A.Site-specific nitrogen and plant density management in irrigated maize.Agronomy Journal,2008,100(4):1193-1204
35.Yang C,Everitt J H,Bradford J M.Comparison of QuickBird satellite imagery and airborne imagery for mapping grain sorghum yield patterns.Precision Agriculture,2006,7(1):33-44
36.Yang C,Everitt J H,Bradford J M.Evaluating high resolution QuickBird satellite imagery for estimating cotton yield.Transactions of the ASABE,2006,49(5):1599-1606
37.Yang C,Everitt J H,Fletcher R S,Murden D.Using high resolution QuickBird imagery for crop identification and area estimation.Geocarto International,2007,22(3):219-233
38.Franke J,Menz G.Multi-temporal wheat disease detection by multispectral remote sensing.Precision Agriculture,2007,8(3):161-172
39.Shou L,Jia L,Cui Z,Chen X,Zhang F.Using high-resolution satellite imaging to evaluate nitrogen status of winter wheat.Journal of Plant Nutrition,2007,30(10):1669-1680
40.Bausch W C,Halvorson A D,Cipra J.QuickBird satellite and ground-based multispectral data correlations with agronomic parameters of irrigated maize grown in small plots.Biosystems Engineering,2008,101(3):306-315
41.Song X,Wang J,Huang H,Liu L,Yan G,Pu R.The delineation of agricultural management zones with high resolution remotely sensed data.Precision Agriculture,2009,10(6):471-487
42.Bausch W C,Khosla R.QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize.Precision Agriculture,2010,11(3):274-290
43.Santoso H,Gunawan T,Jatmiko R H,Darmosarkoro W,Minasny B.Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery.Precision Agriculture,2011,12(2):233-248
44.De Castro A I,Lopez-Granados F,Jurado-Exposito M.Broad-scale cruciferous weed patch classification in winter wheat using QuickBird imagery for in-season site-specific control.Precision Agriculture,2013,14(4):392-413
45.Yang C,Everitt J H,Bradford J M.Evaluating high resolution SPOT5 satellite imagery to estimate crop yield.Precision Agriculture,2009,10(4):292-303
46.Yang C,Everitt J H,Murden D.Using high resolution SPOT 5multispectral imagery for crop identification.Computers and Electronics in Agriculture,2011,75:347-354
47.S?derstr?m M,Borjesson T,Pettersson C G,Nissen K,Hagner O.Prediction of protein content in malting barley using proximal and remote sensing.Precision Agriculture,2010,11(6):587-599
48.Ghobadifar F,Aimrun W,Jebur M N.Development of an early warning system for brown planthopper(BPH)(Nilaparvata lugens)in rice farming using multispectral remote sensing.Precision Agriculture,2016,17(4):377-391
49.Yuan L,Pu R,Zhang J,Wang J,Yang H.Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale.Precision Agriculture,2016,17(3):332-348
50.Wagner P,Hank K.Suitability of aerial and satellite data for calculation of site-specific nitrogen fertilisation compared to ground based sensor data.Precision Agriculture,2013,14(2):135-150
51.Magney T S,Eitel J U H,Vierling L A.Mapping wheat nitrogen uptake from RapidEye vegetation indices.Precision Agriculture,2017,18(4):429-451
52.Bu H,Sharma L K,Denton A,Franzen D W.Comparison of satellite imagery and ground-based active optical sensors as yield predictors in sugar beet,spring wheat,corn,and sunflower.Agronomy Journal,2017,109(1):299-308
53.Gomez-Candon D,Lopez-Granados F,Caballero-Novella J J,PenaBarragan J M,García-Torres L.Understanding the errors in input prescription maps based on high spatial resolution remote sensing images.Precision Agriculture,2012,13(5):581-593
54.Caturegli L,Casucci M,Lulli F,Grossi N,Gaetani M,Magni S,Bonari E,Volterrani M.GeoEye-1 satellite versus ground-based multispectral data for estimating nitrogen status of turfgrasses.International Journal of Remote Sensing,2015,36(8):2238-2251
55.Li X,Lee W S,Li M,Ehsani R,Mishra A R,Yang C,Mangan R L.Feasibility study on Huanglongbing(citrus greening)detection based on WorldView-2 satellite imagery.Biosystems Engineering,2015,132:28-38
56.Sharma S A,Bhatt H P,Ajai.Oilseed crop discrimination:selection of optimum bands and role of middle infrared.ISPRS Journal of Photogrammetry and Remote Sensing,1995,50(5):25-30
57.Yang C,Odvody G N,Thomasson J A,Isakeit T,Minzenmayer R R,Drake D R,Norton R,Barnes E M,Nichols R L.Site-specific management of cotton root rot using airborne and satellite imagery and variable rate technology.Cary,North Carolina:Cotton Incorporated,2017
2.Srinivasan A.Handbook of precision agriculture:principles and applications.Boca Raton,Florida:CRC Press,2009
3.Robertson M J,Llewellyn R S,Mandel R,Lawes R,Bramley R GV,Swift L,Metz N,O’Callaghan C.Adoption of variable rate technology in the Australian grains industry:status,issues and prospects.Precision Agriculture,2011,13:181-199
4.Zhang Q.Precision agriculture technology for crop farming.Boca Raton,Florida:CRC Press,2016
5.Schimmelpfennig D,Ebel R.On the doorstep of the information age:recent adoption of precision agriculture.Publication No.EIB-80.Washington,DC:U.S.Department of Agriculture,Economic Research Service,2011
6.Yang C,Sui R,Lee W S.Precision agriculture in large-scale mechanized farming.In:Zhang Q,ed.Precision agriculture technology for crop farming.Boca Raton,Florida:CRC Press,2016,177-211
7.Mulla D J.Twenty five years of remote sensing in precision agriculture-key advances and remaining knowledge gaps.Biosystems Engineering,2013,114(4):358-371
8.Morain S A,Williams D L.Wheat production estimates using satellite images.Agronomy Journal,1975,67(3):361-364
9.Ryerson R A,Dobbins R N,Thibault C.Timely crop area estimates from Landsat.Photogrammetric Engineering and Remote Sensing,1985,51:1735-1743
10.Büttner G,Csillag F.Comparative study of crop and soil mapping using multitemporal and multispectral SPOT and Landsat Thematic Mapper data.Remote Sensing of Environment,1989,29(3):241-249
11.Oettera D R,Cohenb W B,Berterretchea M,Maierspergera T K,Kennedya R E.Land cover mapping in an agricultural setting using multiseasonal Thematic Mapper data.Remote Sensing of Environment,2001,76(2):139-155
12.Murakami T,Ogawa S,Ishitsuka N,Kumagai K,Saito G.Crop discrimination with multitemporal SPOT/HRV data in the Saga Plains,Japan.International Journal of Remote Sensing,2001,22(7):1335-1348
13.Mausel P W,Everitt J H,Escobar D E,King D J.Airborne videography:current status and future perspectives.Photogrammetric Engineering and Remote Sensing,1992,58:1189-1195
14.King D J.Airborne multispectral digital camera and video sensors:a critical review of systems designs and applications.Canadian Journal of Remote Sensing,1995,21(3):245-273
15.Moran M S,Inoue Y,Barnes E M.Opportunities and limitations for image-based remote sensing in precision crop management.Remote Sensing of Environment,1997,61(3):319-346
16.Barnes E M,Sudduth K A,Hummel J W,Lesch S M,Corwin D L,Yang C,Daughtry C S T,Bausch W C.Remote-and ground-based sensor techniques to map soil properties.Photogrammetric Engineering and Remote Sensing,2003,69(6):619-630
17.Varvel G E,Schlemmer M R,Schepers J S.Relationship between spectral data from an aerial image and soil organic matter and phosphorus levels.Precision Agriculture,1999,1(3):291-300
18.Yang C,Everitt J H.Relationships between yield monitor data and airborne multidate multispectral digital imagery for grain sorghum.Precision Agriculture,2002,3(4):373-388
19.Inman D,Khosla R,Reich R,Westfall D G.Normalized difference vegetation index and soil color-based management zones in irrigated maize.Agronomy Journal,2008,100(1):60-66
20.Backoulou G F,Elliott N C,Giles K L,Mirik M.Processed multispectral imagery differentiates wheat crop stress caused by greenbug from other causes.Computers and Electronics in Agriculture,2015,115:34-39
21.Yang C,Odvody G N,Thomasson J A,Isakeit T,Nichols R L.Change detection of cotton root rot infection over 10-year intervals using airborne multispectral imagery.Computers and Electronics in Agriculture,2016,123:154-162
22.Cohen Y,Alchanatis V,Saranga Y,Rosenberg O,Sela E,Bosak A.Mapping water status based on aerial thermal imagery:comparison of methodologies for upscaling from a single leaf to commercial fields.Precision Agriculture,2017,18(5):801-822
23.Bajwa S G,Tian L F.Soil fertility characterization in agricultural fields using hyperspectral remote sensing.Transactions of the ASAE.American Society of Agricultural Engineers,2005,48(6):2399-2406
24.Goel P K,Prasher S O,Landry J A,Patel R M,Viau A A,Miller J R.Estimation of crop biophysical parameters through airborne and field hyperspectral remote sensing.Transactions of the ASAE(American Society of Agricultural Engineers),2003,46(4):1235-1246
25.Zarco-Tejada P J,Ustin S L,Whiting M L.Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery.Agronomy Journal,2005,97(3):641-653
26.Yang C,Everitt J H,Bradford J M.Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield.Precision Agriculture,2007,8(6):279-296
27.Fitzgerald G J,Maas S J,Detar W R.Spidermite detection in cotton using hyperspectral imagery and spectral mixture analysis.Precision Agriculture,2004,5:275-289
28.Kumar A,Lee W S,Ehsani M R,Albrigo L G,Yang C,Mangan R L.Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques.Journal of Applied Remote Sensing,2012,6(1):063542
29.Li H,Lee W S,Wang K,Ehsani R,Yang C.Extended spectral angle mapping(ESAM)for citrus greening disease detection using airborne hyperspectral imaging.Precision Agriculture,2014,15(2):162-183
30.MacDonald S L,Staid M,Staid M,Cooper M L.Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards.Computers and Electronics in Agriculture,2016,130:109-117
31.Johnson L F,Roczen D E,Youkhana S K,Nemani R R,Bosch D F.Mapping vineyard leaf area with multispectral satellite imagery.Computers and Electronics in Agriculture,2003,38(1):33-44
32.Dobermann A,Ping J L.Geostatistical integration of yield monitor data and remote sensing improves yield maps.Agronomy Journal,2004,96(1):285-297
33.Sullivan D G,Shaw J N,Rickman D.IKONOS imagery to estimate surface soil property variability in two Alabama physiographies.Soil Science Society of America Journal,2005,69(6):1789-1798
34.Ping J L,Ferguson R B,Dobermann A.Site-specific nitrogen and plant density management in irrigated maize.Agronomy Journal,2008,100(4):1193-1204
35.Yang C,Everitt J H,Bradford J M.Comparison of QuickBird satellite imagery and airborne imagery for mapping grain sorghum yield patterns.Precision Agriculture,2006,7(1):33-44
36.Yang C,Everitt J H,Bradford J M.Evaluating high resolution QuickBird satellite imagery for estimating cotton yield.Transactions of the ASABE,2006,49(5):1599-1606
37.Yang C,Everitt J H,Fletcher R S,Murden D.Using high resolution QuickBird imagery for crop identification and area estimation.Geocarto International,2007,22(3):219-233
38.Franke J,Menz G.Multi-temporal wheat disease detection by multispectral remote sensing.Precision Agriculture,2007,8(3):161-172
39.Shou L,Jia L,Cui Z,Chen X,Zhang F.Using high-resolution satellite imaging to evaluate nitrogen status of winter wheat.Journal of Plant Nutrition,2007,30(10):1669-1680
40.Bausch W C,Halvorson A D,Cipra J.QuickBird satellite and ground-based multispectral data correlations with agronomic parameters of irrigated maize grown in small plots.Biosystems Engineering,2008,101(3):306-315
41.Song X,Wang J,Huang H,Liu L,Yan G,Pu R.The delineation of agricultural management zones with high resolution remotely sensed data.Precision Agriculture,2009,10(6):471-487
42.Bausch W C,Khosla R.QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize.Precision Agriculture,2010,11(3):274-290
43.Santoso H,Gunawan T,Jatmiko R H,Darmosarkoro W,Minasny B.Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery.Precision Agriculture,2011,12(2):233-248
44.De Castro A I,Lopez-Granados F,Jurado-Exposito M.Broad-scale cruciferous weed patch classification in winter wheat using QuickBird imagery for in-season site-specific control.Precision Agriculture,2013,14(4):392-413
45.Yang C,Everitt J H,Bradford J M.Evaluating high resolution SPOT5 satellite imagery to estimate crop yield.Precision Agriculture,2009,10(4):292-303
46.Yang C,Everitt J H,Murden D.Using high resolution SPOT 5multispectral imagery for crop identification.Computers and Electronics in Agriculture,2011,75:347-354
47.S?derstr?m M,Borjesson T,Pettersson C G,Nissen K,Hagner O.Prediction of protein content in malting barley using proximal and remote sensing.Precision Agriculture,2010,11(6):587-599
48.Ghobadifar F,Aimrun W,Jebur M N.Development of an early warning system for brown planthopper(BPH)(Nilaparvata lugens)in rice farming using multispectral remote sensing.Precision Agriculture,2016,17(4):377-391
49.Yuan L,Pu R,Zhang J,Wang J,Yang H.Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale.Precision Agriculture,2016,17(3):332-348
50.Wagner P,Hank K.Suitability of aerial and satellite data for calculation of site-specific nitrogen fertilisation compared to ground based sensor data.Precision Agriculture,2013,14(2):135-150
51.Magney T S,Eitel J U H,Vierling L A.Mapping wheat nitrogen uptake from RapidEye vegetation indices.Precision Agriculture,2017,18(4):429-451
52.Bu H,Sharma L K,Denton A,Franzen D W.Comparison of satellite imagery and ground-based active optical sensors as yield predictors in sugar beet,spring wheat,corn,and sunflower.Agronomy Journal,2017,109(1):299-308
53.Gomez-Candon D,Lopez-Granados F,Caballero-Novella J J,PenaBarragan J M,García-Torres L.Understanding the errors in input prescription maps based on high spatial resolution remote sensing images.Precision Agriculture,2012,13(5):581-593
54.Caturegli L,Casucci M,Lulli F,Grossi N,Gaetani M,Magni S,Bonari E,Volterrani M.GeoEye-1 satellite versus ground-based multispectral data for estimating nitrogen status of turfgrasses.International Journal of Remote Sensing,2015,36(8):2238-2251
55.Li X,Lee W S,Li M,Ehsani R,Mishra A R,Yang C,Mangan R L.Feasibility study on Huanglongbing(citrus greening)detection based on WorldView-2 satellite imagery.Biosystems Engineering,2015,132:28-38
56.Sharma S A,Bhatt H P,Ajai.Oilseed crop discrimination:selection of optimum bands and role of middle infrared.ISPRS Journal of Photogrammetry and Remote Sensing,1995,50(5):25-30
57.Yang C,Odvody G N,Thomasson J A,Isakeit T,Minzenmayer R R,Drake D R,Norton R,Barnes E M,Nichols R L.Site-specific management of cotton root rot using airborne and satellite imagery and variable rate technology.Cary,North Carolina:Cotton Incorporated,2017