Cover crops can positively impact productivity and the environment. While improved estimates of cover crop N benefits could help promote their adoption, little information is currently available at the broad scale. We conducted a multi-site study to determine whether use of satellite images and site factors could fill this gap. Six spectral indices were extracted from Sentinel-2 satellite imagery and used with modeling covariates to estimate cover crop properties and biomass N credits (biomass × N contents). The partial least squares regression (PLSR) models were calibrated and validated with samples from 42 cover crop fields located in the midwestern and northeastern United States collected in 2017-2018. Remote sensing (RS)-derived spectral indices strongly correlated (r > .7) with red clover (Trifolium pratense L.) but not with rye (Secale cereale L.) biomass. Growing degree days (GDDs), cover height, ground cover percentage, and temperature often had high importance (variable importance in projection >1) in PLSR models. Model predictive power was limited for estimates of biomass N credits when data from all validation sites and cover types were used (adjusted [adj] R 2 = .52). However, models for both biomass (adj R 2 = .81) and biomass N credits (adj R 2 = .89) were successful for red clover fields. This suggests N benefits could be more effectively modeled for specific cover crop types. We also found RS-based estimation of C/N ratios performed moderately well when applied to the complete dataset (adj R 2 = .54), suggesting a way to differentiate grass and legume cover crops that can potentially inform biogeochemical models. INTRODUCTIONInterest in cover crops has grown rapidly (Dunn et al.

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Concern is rising that ecologically important, carbon-rich natural lands in the United States are losing ground to agriculture. We investigate how quantitative assessments of historical land-use change (LUC) to address this concern differ in their conclusions depending on the data set used through an examination of LUC between 2006 and 2014 in 20 counties in the Prairie Pothole Region using the Cropland Data Layer, a modifi ed Cropland Data Layer dataset, data from the National Agricultural Imagery Program, and in-person ground-truthing. The Cropland Data Layer analyses overwhelmingly returned the largest amount of LUC with associated error that limits drawing conclusions from it. Analysis with visual imagery estimated a fraction of this LUC. Clearly, analysis technique drives understanding of the measured extent of LUC; different techniques produce vastly different results that would inform land management policy in strikingly different ways. Best practice guidelines are needed.

Eleven spectral vegetation indices that emphasize foliar plant pigments were calculated using airborne hyperspectral imagery and evaluated in 2004 and 2005 for their ability to detect experimental plots of corn manually inoculated with Ostrinia nubilalis (Hübner) neonate larvae. Manual inoculations were timed to simulate infestation of corn, Zea mays L., by first and second flights of adult O. nubilalis. The ability of spectral vegetation indices to detect O. nubilalis-inoculated plots improved as the growing season progressed, with multiple spectral vegetation indices able to identify infested plots in late August and early September. Our findings also indicate that for detecting O. nubilalis-related plant stress in corn, spectral vegetation indices targeting carotenoid and anthocyanin pigments are not as effective as those targeting chlorophyll. Analysis of image data suggests that feeding and stem boring by O. nubilalis larvae may increase the rate of plant senescence causing detectable differences in plant biomass and vigor when compared with control plots. Further, we identified an approximate time frame of 5-6 wk postinoculation, when spectral differences of manually inoculated "second" generation O. nubilalis plots seem to peak.