Show simple item record

Factors Influencing Remote Sensing Measurements of Winter Cover Crops

dc.contributor.advisorJustice, Christopher Oen_US
dc.contributor.authorPrabhakara, Kusumaen_US
dc.date.accessioned2017-01-24T06:39:35Z
dc.date.available2017-01-24T06:39:35Z
dc.date.issued2016en_US
dc.identifierhttps://doi.org/10.13016/M2MC2S
dc.identifier.urihttp://hdl.handle.net/1903/18970
dc.description.abstractWinter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. In addition to remote sensing imagery, advances have been made in the use of proximal sensors integrated with GPS for on-field measurements, and the comparability of such measurements between platforms, as well as based on processing level is important. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012-2013 winter growing season. There was a strong relationship between the Normalized Difference Vegetation Index (NDVI) and percent groundcover (r2 =0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The Triangular Vegetation Index (TVI) was most accurate in estimating high ranges of biomass (r2=0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). Accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops. Surface reflectance measurements were more correlated with proximal sensors compared to top of atmosphere, with intercepts closer to zero, regression slopes nearer to the 1 to 1 line, and less variance between measured values. NDVI was highly correlated with percent vegetative groundcover, though surface reflectance products did not necessarily improve the relationships. When the Scattering for Arbitrarily Inclined Leaves (SAIL) model was used with measured field variables reflective of realistic winter cover crop scenarios, there were not large differences between NDVI despite differences in residue cover and moisture. At low LAI, NDVI is not capable of differentiating between residue and vegetative cover.en_US
dc.language.isoenen_US
dc.titleFactors Influencing Remote Sensing Measurements of Winter Cover Cropsen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentGeographyen_US
dc.subject.pqcontrolledRemote sensingen_US
dc.subject.pqcontrolledGeographyen_US
dc.subject.pqcontrolledAgricultureen_US
dc.subject.pquncontrolledagricultureen_US
dc.subject.pquncontrolledcover cropsen_US
dc.subject.pquncontrolledgeographical scienceen_US
dc.subject.pquncontrolledproximal sensorsen_US
dc.subject.pquncontrolledremote sensingen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record