Documenting the distribution of cattle dung across grazed pastures is an important part of understanding nutrient cycling processes in grasslands. However, investigation of distributions at adequate spatial scales and over extended time periods is hindered by the lack of a time- and cost-efficient method for documenting and monitoring dung pat locations. To address this research challenge, an unmanned aerial vehicle and multispectral sensor were used to identify and classify dung pats. Imagery was collected on 12 flights over a subirrigated meadow in the Nebraska Sandhills, in which two different grazing strategies were being evaluated: an ultrahigh stocking density and a low stocking density. The images were classified using supervised classification with a support vector machine algorithm, and post-classification accuracy was assessed using a confusion matrix. In addition, Ripley's K was used to identify high-density dung areas at varying densities and spatial extents. The classification had an overall accuracy of 82.6% and a Kappa coefficient of 0.71. The user's accuracy of dung classification was higher (0.91) than the producer's (0.73). The majority of classification errors were related to the misclassification of dung as vegetation, often in spectrally complex areas where shadowing affected the ability of the classifier to correctly identify dung. Classification accuracy declined precipitously after dung reached 10-14 d of age, both because of the change in spectral reflectance due to drying and because of the regrowth of vegetation. The density-based cluster analysis found no clustering in the low stocking density treatment; dung in the ultra-high stocking density treatment was most frequently found to be clustered near water sources, in corners, and near supplement feeders. This approach to dung identification, mapping, and spatial cluster analysis is a promising alternative to existing methods and deserves further exploration at additional spatial scales and in diverse ecological settings using current technologies. (c) 2024 The Society for Range Management. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The Photochemical Reflectance Index (PRI) and solar induced fluorescence (SIF) provide information on plant photosynthetic activity. PRI and SIF are both strongly influenced by irradiance, but uncertainties related to the interpretation of these light responses at large spatial scales remain, partly due to a shortage of suitable data from aircraft or satellite platforms. The goal of this study was to explore interpretations of the PRI- and SIF-light responses of trees owing to species, functional types (evergreen and deciduous) and season. Using airborne hyperspectral and ultraspectral imagery in a North American urban forest, we derived PRI, SIF, and albedo (an indicator of illumination) at the 1-m pixel level. We then quantified crown-level PRI and SIF light responses of ten different tree species at three time points from late-summer to autumnal senescence using hierarchical models. Our results confirmed that both PRI and SIF were strongly influenced by illumination with PRI decreasing and SIF increasing with illumination. Both slope and intercept of the PRI-albedo relationship changed with season, but the pattern varied among species and functional types. SIF values decreased during autumnal senescence for all species, but evergreen species exhibited less seasonal decline in the slope of SIF-albedo relationship compared to deciduous species. The PRI and SIF light responses derived from the airborne imagery offer complementary information on dynamic photosynthesis responses presumably due to varying canopy structure, pigmentation and photoprotection among species and functional types. From airborne platforms, PRI- and SIF-light responses can be used to explore the contrasting physiological responses of individual tree crowns, providing a spatially and temporally explicit view of dynamic plant traits related to photoregulation and a novel view of functional diversity for entire landscapes.

Semiarid grasslands of the Nebraska Sandhills provide critical ecosystem services and are an important forage resource for the local cattle industry. Over the past decades, warming and climate-related extremes have affected grassland production worldwide, which promotes the initiation of numerous grassland monitoring projects. Despite this, production trends for plant functional groups in the Sandhills regions in recent years have remained unknown. In this study, we analyzed plant biomass production of the Sandhills grasslands with a dataset collected over 15 yr from 2007 to 2021. Ungrazed total biomass and biomass of individual plant functional groups were assessed in grazing exclosures twice a year, in mid-June (for early season) and mid-August (for late season). This first paper reports our findings on total biomass and compositional changes of the three major plant functional groups, as well as trends in precipitation and temperature during the study period. A significant increasing trend ( P < 0.05) was observed in temperature over time during the early season (April to mid-June), with a weak monotonic increasing trend ( P = 0.07) during the full season (April to mid-August), whereas no significant pattern was reported for precipitation during the study, although it displayed complex within- and across-season patterns. The proportion of C3 -grass biomass in total biomass increased ( P < 0.05), while the proportion of C4 -grass biomass decreased ( P < 0.01). We did not observe any significant trends for forbs; however, the drought of 2012 resulted in up to a fivefold increase in the proportion of forb biomass the following year. These findings enhance our understanding of current patterns in grassland production and contribute to regional evidence on the response of plant functional groups to variability and extremes in intra-annual weather variables, which can improve our capability to perform adaptive grazing management in a similar semiarid grassland ecosystem. (c) 2024 The Authors. Published by Elsevier Inc.

The Moderate Solution Imaging Spectroradiometer (MODIS) is a multispectral sensor that has been actively researched in various fields using diverse land and atmospheric products. MODIS was first launched over 20 years ago, and the demand for novel sensors that can produce data comparable to that obtained using MODIS has continuously increased. In this study, land products obtained using the visible infrared imaging radiometer suite (VIIRS) of the Suomi National Polar-orbiting Partnership satellite launched in 2011 were introduced, including land surface temperature and vegetation indices such as the normalized difference vegetation index and enhanced vegetation index. These land products were compared with existing data obtained using MODIS to verify their local applicability in South Korea. Based on spatiotemporal monitoring of an extreme drought period in South Korea and the application of VIIRS land products, our results indicate that VIIRS can effectively replace MODIS multispectral sensors for agricultural drought monitoring.

Cover crops (CCs) offer ecosystem benefits, yet their impact on subsequent crop yields varies with climate, soil, and management practices. Using the Decision Support System for Agrotechnology Transfer (DSSAT) at the University of Nebraska-Lincoln’s Eastern Nebraska Research, Education, and Extension Center (ENREEC), we identified optimal cereal rye management strategies focusing on planting, termination, and the intervals between CC termination and corn planting. Results showed minimal impact of CC management variations on corn yield, underscoring corn’s resilience to management changes. Delayed planting notably decreased CC biomass, nitrogen uptake, and biomass nitrogen content on average by 8.8%, 11%, and 9.2% for every five-day delay from 25 September. Every 5-day increase in the interval between CC termination and corn planting reduced biomass by 19.3%. Conversely, each 5-day delay in CC termination from 10 September to 10 October increased biomass by 30%, enhancing SOC accumulation. SOC changes over the 30-year simulation ranged from 5.8% to 7.7%, peaking with late May terminations. The earliest termination showed the highest nitrogen content in biomass (3.4%), with the lowest (0.69%) in mid-May. Our results demonstrate that strategic CC management supports soil health without negatively impacting corn yield in Eastern Nebraska, providing valuable insights for farmers and practitioners aiming to implement sustainable CC practices while preserving crop productivity.

High interannual variability of forage production in semiarid grasslands leads to uncertainties when livestock producers make decisions, such as buying additional feed, relocating animals, or using flexible stocking. Within-season predictions of annual forage production (i.e., yearly production) can provide specific boundaries for producers to make these decisions with more information and possibly with higher confidence. In this study, we use a recently developed forage production model, ForageAhead, that uses environmental and seasonal climate variables to estimate the annual forage production as approximated by remotely sensed vegetation data. Because, among other variables, this model uses observed summer climate data, the model output cannot be produced early enough in the year (e.g., spring months) to inform within-season management decisions. To address this issue, we developed summer climate scenarios (e.g., extremely warm and dry and moderately cool and wet) that serve as an input in the model in combination with observed winter and spring climate data from a particular year. The summer climate scenarios used historical summer precipitation and temperature data (1950-2018) categorized into three, five, and seven percentile categories. These percentile values were then combined to represent summer climate scenarios, which were further used as the ForageAhead model input. We tested the optimal number of percentile categories to be used as the model input to obtain accurate prediction of forage production while also minimizing the number of possible temperature and precipitation combinations, which increases with the number of percentile categories. For the 19-year period analysis (2000-2018), we also determined the most and least common scenarios that occurred in the western United States. When using five percentile categories for summer precipitation and temperature, we were able to capture the interannual variability in the spatial extent of abnormally low and high biomass production. The ForageAhead predictions captured similar spatial patterns of forage anomalies as another similar model (Grass-Cast). This method can be made available in a user-friendly automated system that can be used by livestock producers and rangeland managers to inform within-season management decisions. This method can be especially valuable for flexible stocking as it provides a range of possible annual forage production scenarios by the end of May.

Drought is a common and costly natural disaster with broad social, economic, and environmental impacts. Machine learning (ML) has been widely applied in scientific research because of its outstanding performance on predictive tasks. However, for practical applications like disaster monitoring and assessment, the cost of the models failure, especially false negative predictions, might significantly affect society. Stakeholders are not satisfied with or do not "trust" the predictions from a so-called black box. The explainability of ML models becomes progressively crucial in studying drought and its impacts. In this work, we propose an explainable ML pipeline using the XGBoost model and SHAP model based on a comprehensive database of drought impacts in the U.S. The XGBoost models significantly outperformed the baseline models in predicting the occurrence of multi-dimensional drought impacts derived from the text-based Drought Impact Reporter, attaining an average F2 score of 0.883 at the national level and 0.942 at the state level. The interpretation of the models at the state scale indicates that the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) contribute significantly to predicting multi-dimensional drought impacts. The time scalar, importance, and relationships of the SPI and STI vary depending on the types of drought impacts and locations. The patterns between the SPI variables and drought impacts indicated by the SHAP values reveal an expected relationship in which negative SPI values positively contribute to complex drought impacts. The explainability based on the SPI variables improves the trustworthiness of the XGBoost models. Overall, this study reveals promising results in accurately predicting complex drought impacts and rendering the relationships between the impacts and indicators more interpretable. This study also reveals the potential of utilizing explainable ML for the general social good to help stakeholders better understand the multi-dimensional drought impacts at the regional level and motivate appropriate responses.

Cover crops (CC) provide numerous ecosystem services such as improving soil health, reducing nutrient loss, increasing productivity, and mitigating greenhouse gas emission. However, adoption of CC has been hindered by perceived negative impacts on main crop productivity and additional production costs. This is partly attributed to the gap in current state of knowledge in CC and its interaction with main crop production under different biophysical conditions. In this study, Decision Support System for Agrotechnology Transfer model was used to evaluate the long-term impact of cereal rye (Secale cereale L.) on corn (Zea mays L.) yield, soil organic carbon (SOC), nitrate leaching, soil water, and drainage for a range of climate, soil, and irrigation management in Eastern and Central Nebraska. A 30-year (1991-2020) simulation showed no difference in corn yield and SOC between CC and no-cover crop treatments at both sites under irrigated and rainfed conditions. However, CC resulted in reduction of N loss by up to 48% at the Eastern Nebraska Research and Extension Center and 24% at South Central Agricultural Laboratory under irrigation. Cereal rye has no significant effect on total soil water but, a significant reduction in cumulative subsurface drainage of 44% was determined at both sites. This study has shown the possible effect of cover crop on corn crop yield and soil properties over different regions in Nebraska. Future research extending the scope and geographic area is needed to test and quantify possible impacts of multiple CC species under diverse management and biophysical conditions.

Woody species encroachment is occurring within the semi-arid grasslands of the Nebraska Sandhills U.S., primarily driven by native Juniperus virginiana and Pinus ponderosa, altering ecosystems and the services they provide. Effective, low cost, and cross-scale monitoring of woody species growth and performance is necessary for integrated grassland and forest management in the face of climate variability and change. In this study, we sought to establish a relationship between remote sensing-derived vegetation indices (VIs), tree dendrochronological (raw and standardized tree ring width) measurements, and the abiotic environment [(precipitation, temperature, Palmer Drought Severity Index (PDSI), and soil water content (0-300 cm depth)], over a 30-year period (1984-2013), to assess the performance of encroaching woody J. virginiana and P. ponderosa within the Nebraska Sandhills. We also investigated whether VIs can be used as an effective alternative tool to replace or complement ground measurements. Our results indicate that precipitation, temperature, and PDSI were significant (p < 0.05) predictors of J. virginiana and P. ponderosa growth based on dendrochronological measurements and VIs, while soil water content from 40 to 300 cm depth was a significant predictor of J. virginiana performance. Out of the six VIs that were investigated, four were significant predictors of tree ring growth. R2 values between grassland VIs and growing season climate were greater than those of J. virginiana or P. ponderosa, while grassland performance was decoupled from soil water content. Additionally, climatic conditions in the previous year were significant determinants of current year growth of tree species but did not affect current year grassland performance. This study provides evidence for the efficacy of remote sensing-based VIs in monitoring interannual variation in the growth of woody species, while determining abiotic factors impacting the growth of grassland vegetation, J. virginiana, and P. ponderosa in the Nebraska Sandhills.

In this study, Eco-hydrometeorological Remote Sensing-based Penman-Monteith algorithm (Eh-RSPM) was developed by implementing the gross primary productivity into the revised Remote Sensing based Penman-Monteith algorithm (RS-PM). Evaluation of Eh-RSPM was conducted through comparison with in-situ measurements as well as model-based products (e.g. MODerate resolution Imaging Spectroradiometer (MODIS) 16 global ET products (MOD16 ET) and Surface Energy Balance System (SEBS)) during two years (2004 and 2012) in Northeast Asia. Comparison of ET from Eh-RSPM algorithm with five flux tower measurement agreed well with the flux tower datasets at the entire validation sites. Especially, Eh-RSPM showed advantages in improving the accuracy of ET at stations with relatively short canopy height (e.g. QHB and KBU site) as well as the forest site (e.g. SMK). Focusing on the forest site, Eh-RSPM exhibited slightly better statistical performance compared to MOD16. Specifically, the temporal mean bias and RMSD showed a slight improvement, decreasing from -15.40 W m-2 to -12.58 W m-2 and from 28.41 W m-2 to 25.26 W m-2, respectively. This is a key finding of this study, demonstrating the applicability of the improved ET algorithm to regions with significant forest cover. Similarly, spatial distribution of Eh-RSPM showed similar patterns with MOD16 and SEBS. Eh-RSPM strongly showed advantages over the land cover types with relatively shorter canopy height (e.g. grassland and alpine meadow) as well as the heterogeneous forest showed significant improvement in Eh-RSPM through considering the actual physiological behavior variation and influence of photosynthesis into ET calculation.

We investigated the synergic use of optical and biophysical traits to characterize Bromus inermis (smooth bromegrass) pasture lands and assess the combined effects of long-term (15-years) rotational grazing and management strategies of (i) no fertilization (C), (ii) mineral nitrogen (N) fertilization (HF), and (iii) supplemented fertilization through dry distiller grains plus soluble (DDGS; SF)- on forage growth, performance, and quality. We found that fertilization improved pasture's biomass, specific leaf area, leaf area index (LAI), as well as forage quality. The use of N fertilization did not offer an advantage (e.g., forage quality, yield) over DDGS under both grazed and ungrazed conditions. Optical, proximal sensing techniques allowed the characterization of pasture lands in a non-invasive and time-efficient manner. We tested established vegetation indices (VIs) for their accuracy in identifying and quantifying important physiological and morphological traits. Results showed that the Normalized Difference Vegetation Index (NDVI) and Vogelmann (VOG) were among the best performing indices. Results contribute to our understanding of the impact of long-term fertilization management on Bromus inermis pastures and validate the use of proximal sensing methods. Proximal sensing methods provide direct, non-invasive, and time efficient tool for assessment of the performance and health of vegetation, keys to successful integrative management strategies.

Fall-planted cover crop (CC) within a continuous corn (Zea mays L.) system offers potential agroecosystem benefits including mitigating the impacts of increased temperature and variability in precipitation patterns. A long-term simulation using the Decision Support System for Agrotechnology Transfer (DSSAT) model was made to assess the effects of cereal rye (Secale cereale L.) on no-till continuous corn yield and soil properties under historical (1991-2020) and projected climate (2041-2070) in eastern Nebraska. Local weather data during the historical period was used, while climate change projections were based on the Canadian Earth System Model 2 (CanESM2) dynamically downscaled using the Canadian Centre for Climate Modelling and Analysis Regional Climate Model 4 (CanRCM4) under two Representative Concentration Pathways (RCP) namely, RCP4.5 and RCP8.5. Simulations results indicated that CC impacts on corn yield were non-significant under historical and climate change conditions. Climate change created favorable conditions for CC growth, resulting in an increase in biomass. Cover crop reduced N leaching under climate change scenarios compared to an average reduction of 60% (7 kg ha-1) during the historical period. Cover crop resulted in a 6% (27 mm) reduction in total water in soil profile (140 cm) (SW) and 22% (27mm) reduction in plant available water (PAW) compared to no cover crop (NCC) during historical period. Cover crop reduced cumulative seasonal surface runoff/soil evaporation and increased rate of SOC buildup. This research provides valuable information on how changes in climate can impact performance of cereal rye CC in continuous corn production and should be scaled to wider locations and CC species.

Increasing intra- and inter-annual precipitation and temperature variability of growing season in grasslands as a result of changing climate alters seasonal plant biomass production. This variability can affect adaptive grazing management decisions designed to align seasonally variable forage supply to meet livestock grazing demands. The long-term effects of this variability on semi-arid grassland biomass have not been well characterized in the Nebraska Sandhills. Therefore, we investigated effects of precipitation and temperature variability on early-season and late-season biomass in the Nebraska Sandhills from 2007 to 2021. Un-grazed biomass of individual plant function group and total un-grazed plant biomass were collected from exclosures in mid-June and mid-August for each year. Normalized Difference Vegetation Index (NDVI) derived from satellite-based observations were used as a proxy of grazed biomass in mid-June and mid-August. We calculated precipitation accumulation (PreAcc) and growing degree days accumulation (GDDAcc) for each growing season to determine variability and relate them to both un-grazed biomass and NDVI with time-lagged analysis. Three effects of precipitation and temperature, respectively on the three main functional groups, were generalized: 1) current-year effects on the dominant C4 grasses, 2) legacy effects of drought on forbs, and 3) long-term effects over years on C3 grasses. C4-grass biomass, total plant biomass, and NDVI were significantly correlated with PreAcc and GDDAcc without time-lagged, while significant lagged correlations were observed for biomass of C3 grasses and forbs with PreAcc and GDDAcc in 1-year (p < 0.05). NDVI and total un-grazed plant biomass were positively correlated (r = 0.57, p < 0.05) in mid-August. Results confirmed the dependence of biomass production on intra- and inter-annual patterns in weather, which can be used to improve annual planning of adaptive grazing management using within-season adjustments based on fast grassland monitoring using satellite-derived NDVI in the face of increasing variability in precipitation and temperature.

A flash drought is a drought that occurs rapidly within a short period of time, owing to changes in weather and environmental factors that occur rapidly due to extreme weather caused by climate change. Drought damage occurs within a relatively short period of time, and extreme vegetation stress can damage crops due to reduced water supply. Thus, different monitoring methods are required for different drought types. In this study, ESI (Evaporative Stress Index) and EDDI (Evaporative Demand Drought Index) were calculated to evaluate the short-term drought and determine the mechanism that causes flash droughts. The ESI and EDDI both indicates faster responses to the 2017 spring drought event than the SPI (Standardized Precipitation Index), confirming their sensitive drought assessment abilities. A flash drought detection standard was established based on the drought category used in USDM (United States Drought Monitor), and seven flash drought events were detected in South Korea over 5 years (2014-2018). A correlation analysis indicates that, among the hydrometeorological factors, the correlation coefficients between soil moisture/maximum temperature and flash drought occurrence exhibited strong positive (negative) correlations (0.9 and -0.9, respectively). The carbon change pattern obtained using GPP (Gross Primary Productivity) differed, exhibiting a -60% average GPP reduction rate compared to three other regions (JB, CN, and CB) where flash droughts occurred in 2018. We focus our discussion on opportunities to extend flash drought detection and mechanism determination using two drought indices that must be addressed to improve flash drought monitoring in South Korea.

Maize Gross primary productivity (GPP) contributes the most to the total cropland GPP, which is an important part of the total global GPP. Thus, it is of great significance to accurately estimation daily maize GPP for global carbon research. However, most existing vegetation index-based GPP models have one of two shortcomings: either too simple consideration of the relationship between GPP and incoming radiation or the weak spatial universality when applied to non-modeled regions. To make up for the foregoing deficiencies, this study proposes an improved practical maize GPP estimation model, the Fraction of vegetation cover-Solar radiation-Leaf nitrogen content (FSL) model, based on the physiological features and phenological information using daily reconstructed Normalized Difference Vegetation Index (NDVI) from MODIS imagery, mean air temperature (DMT) and solar shortwave radiation (SSR) data. The FSL model integrates relatively strict phsical interpretations about canopy light interception, leaf photosynthetic capacity, and light saturation phenomenon. The relationship between GPP and incoming radiation was analyzed in depth in FSL model. The FSL model outperformed the existing vegetation index-based GPP models in comparative experiments within one flux site in Germany, two flux sites in China and four flux sites in America in terms of five evaluation metrics. FSL model delivered strong spatial robustness when applied to four sites (one in Germany, two in China, and one in America) using the model parameters derived from other three sites in America. Based on the high-quality regional NSRDB data and air temperature estimation derived from satellite imageries, this model provides an opportunity to accurately estimate daily maize GPP at regional scale. This study proposes a practical framework that is potential to estimate daily GPP of other crops at regional scale, exploiting the high accuracy and spatial robustness of the FSL model.

The ecosystem performance approach, used in a previously published case study focusing on the Nebraska Sandhills, proved to minimize impacts of non-climatic factors (e.g., overgrazing, fire, pests) on the remotely-sensed signal of seasonal vegetation greenness resulting in a better attribution of its changes to climate variability. The current study validates the applicability of this approach for assessment of seasonal and interannual climate impacts on forage production in the western United States semi-arid grasslands. Using a piecewise regression tree model, we developed the Expected Ecosystem Performance (EEP), a proxy for annual forage production that reflects climatic influences while minimizing impacts of management and disturbances. The EEP model establishes relations between seasonal climate, site-specific growth potential, and long-term growth variability to capture changes in the growing season greenness measured via a time-integrated Normalized Difference Vegetation Index (NDVI) observed using a Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting 19 years of EEP were converted to expected biomass (EB, kg ha−1 year−1) using a newly-developed relation with the Soil Survey Geographic Database range production data (R2 = 0.7). Results were compared to ground-observed biomass datasets collected by the U.S. Department of Agriculture and University of Nebraska-Lincoln (R2 = 0.67). This study illustrated that this approach is transferable to other semi-arid and arid grasslands and can be used for creating timely, post-season forage production assessments. When combined with seasonal climate predictions, it can provide within-season estimates of annual forage production that can serve as a basis for more informed adaptive decision making by livestock producers and land managers.

Hyperspectral airborne imagery can provide rich information on plant physiological and structural properties at a scale intermediate to that of proximal and satellite remote sensing and has broad applications in assessing ecosystem function and biodiversity. A key processing step of airborne hyperspectral data is the atmospheric correction that compensates for path radiance, aerosol effects and gas absorption to derive an accurate surface reflectance that can be compared across time and space. In practice, routine correction procedures are often customized for various platforms without fully reporting or checking the errors systematically in the atmospheric correction. Such errors can have significant effects on downstream analyses such as vegetation indices or trait retrievals, and not all subsequent analyses are equally affected by the accuracy of reflectance retrievals. In this study, we examined the errors in three types of atmospheric correction methods including a radiative transfer model (RTM), empirical line correction (ELC) and a hybrid method that combines elements of the two via Bayesian inference. Our results revealed that the individual correction methods had different effects on the reflectance retrievals that impacted downstream measurements. Including spectral measurements from ground vegetation targets in addition to painted calibration targets improved the performance of the ELC method. The hybrid method yielded reflectance spectra that most closely matched the spectra of the ground validation data. The errors in vegetation indices differed with the methods, and certain indices (such as PRI) were more affected than indices that rely on stable, broader spectral features (e.g., NDVI). Plant pigment retrievals via partial least squares regression were less sensitive to errors in atmospheric correction. These findings demonstrate that obtaining high-quality, field spectral measurements over well-characterized calibration targets and representative land cover types within the scene is critical for accurate surface reflectance and subsequent downstream products, such as vegetation indices or plant traits.

Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.