Julian Day Research Articles

Preliminary study of generating a local time scale with NIM 87Sr optical lattice clock

A local time scale can be generated by steering flywheel clocks with state-of-the-art optical lattice clocks. This paper presents our simulations about the influence of the optical lattice clock’s operational strategies and the flywheel clock’s noise characteristics on the performance of the generated time scale. By post-processing the measured frequency difference between the optical lattice clock Sr1 and the hydrogen maser HM50 at the National Institute of Metrology (NIM), during the modified Julian date (MJD) 59029–59059, a local time scale with 0.68 ns time variation referencing to the TT(BIPM20) is demonstrated.

Drivers of Southern Flying Squirrel (Glaucomys volans) Aggregation Size in South Carolina, U.S.A.

Although it is known that southern flying squirrels (Glaucomys volans) nest in larger groups to offset the energetic costs of low temperatures, the influence of other variables on aggregation size remains relatively unknown. Therefore, the influence of environmental variables and individual characteristics of G. volans on nest box aggregation size was studied from 1992–1998 at the Savannah River Site in South Carolina. Over the study period, 5859 occupied boxes were observed, representing 11,238 captures and 2671 unique individuals. The majority of occupied boxes (60.3%) contained solitary individuals, while 26.1% contained aggregations (≥2 individuals) of adult and/or subadult individuals and 13.6% contained litters. Although ambient temperature is widely believed to be the most important variable influencing aggregation size of G. volans, the final model explaining nest aggregation size for G. volans included not only minimum daily ambient temperature, but also Julian date and individual G. volans characteristics of sex, age, and reproductive status. Minimum ambient temperature was inversely related to aggregation size, with larger aggregation sizes associated with winter months, although aggregation size sharply increased between the months of Aug.–Sept. The retention of Julian date in the final model, in addition to minimum ambient temperature, indicates that behavioral shifts due to seasonal changes, independent of temperature, should be considered. Nestlings were associated with larger aggregation sizes, as were inactive and lactating females and inactive males. However, sex alone did not significantly contribute to the final model. This study demonstrates that in addition to temperature, there are a suite of predictors of nest aggregation size for G. volans that should be considered when evaluating nest box occupancy for this species.

Evaluating phenological trends of different vegetation types in response to climate change over the Rajmahal Hills in India during 2001-2019

ABSTRACT The present study employed Moderate Resolution Imaging Spectroradiometer (MODIS) based Enhanced Vegetation Index (EVI) dataset to track vegetation phenological trends in response to climate change activity over the Rajmahal Hills in India during 2001–2019. Two key phenological metrics (i.e., start of the season (SOS), and end of the season (EOS)) were estimated, and their long-term trends were studied for each vegetation type. The study findings revealed that the start of season (SOS) generally occurred during late March (Julian day (JD) 81) to early April (JD 97), whilst the end of season (EOS) has been predominantly observed during early March (JD 65) to end of March (JD 81). Furthermore, the SOS and EOS of most vegetation types (except WSV and MF) showed a delaying trend during 2001–2010 at the rate of 0.009–0.29 day year−1; 0.03–0.33 day year−1, respectively. Conversely, the SOS and EOS of all the vegetation types (except for MF) showed an advancing trend during 2010–2019 at the rate of 0.06–0.24 day year−1; 0.17–0.23 day year−1, respectively. The changes in the trends of SOS and EOS of different vegetation types mainly were perceived due to local climate change activity (changes in precipitation and temperature pattern).

Phytoplankton size-class contributions to new and regenerated production during the EXPORTS Northeast Pacific Ocean field deployment

The NASA EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) program was established to better quantify the pathways of the biological carbon pump in order to gain a more comprehensive understanding of global carbon export efficiency. The summer 2018 field campaign in the vicinity of Ocean Station Papa (Station P; 50°N, 145°W) in the Northeast Pacific Ocean yielded evidence of low phytoplankton biomass and primary productivity dominated by small cells (<5 µm) that are reliant on recycled nutrients. Using combined 13C/15N stable isotope incubations, we calculated an average depth-integrated dissolved inorganic carbon uptake (net primary production) rate of 23.1 mmol C m–2 d–1 throughout the euphotic zone with small cells contributing 88.9% of the total daily DIC uptake. Average depth-integrated NO3– uptake rates were 1.5 mmol N m–2 d–1 with small cells contributing 73.4% of the total daily NO3– uptake. Estimates of new and regenerated production fluctuated, with small cells continuing to dominate both forms of production. The daily mixed-layer f-ratio ranged from 0.17 to 0.38 for the whole community, consistent with previous studies, which indicates a predominance of regenerated production in this region, with small and large cells (≥5 μm) having average f-ratios of 0.28 and 0.82, respectively. Peak phytoplankton biomass, total primary productivity and new production occurred between Julian Days 238 and 242 of our observation period, driven primarily by an increase in carbon and nitrate assimilation rates without apparent substantial shifts in the phytoplankton size-class structure. Our findings demonstrate the importance of small cells in performing the majority of net primary production and new production and the modest productivity fluctuations that occur in this iron-limited region of the Northeast Pacific Ocean, driven by ephemeral increases in new production, which could have significant ramifications for carbon export over broad timescales.

Nesting success of red-winged blackbirds (Agelaius phoeniceus) in marshes in an anthropogenic landscape.

Recent analyses show significant population declines in many abundant avian species, especially marsh-nesting species including the red-winged blackbird (RWBL). Hypothesized causes include reduced nesting success resulting from changing land-use patterns and exposure to contaminants. Our goal was to test the hypothesis that landscape and nest characteristics as well as exposure to polychlorinated biphenyls (PCBs) correlate with nesting success. From 2008 to 2014, we measured clutch size, egg and nestling mass, hatching and fledging success and daily survival of 1293 RWBL nests from 32 marshes in the Hudson River valley of New York. Using generalized linear effect and survival models, we found that: (i) Julian date was negatively related to hatching success and clutch size but positively related to egg mass; (ii) nest height was negatively related to hatching success; (iii) nestling mass decreased with increased nest density and distance to edges; (iv) fledging success was significantly lower in nests closer to the ground that were far from water; and (v) clutch size and daily survival were higher in nests farther from water. Results showed that nesting success was correlated with variables associated with flooding, population density and predation and provided no support for the predicted negative effects of PCB exposure.

Wanted: A Date with Herodotus

Herodotus comes down to us as the father of history and his fifth-century work, the Histories, is recognized as the first in an entirely new literary genre. But mid-fifth-century historiography is missing one of the most convenient of supranational tools—a reliable dating grid, or calendar—and Herodotus simply must make do as best he can without one. Although it was first suggested by a sixth-century Scythian monk, the axis of time along the now familiar BC/AD system is of comparatively recent adoption. Partly because of bitter doctrinal disputes over when Jesus of Nazareth was born—this system is never widely accepted until a seventeenth-century Jesuit scholar suggests that Anno Domini year one is just a convenient convention and by no means an agreement. When reading Herodotus today, particularly in an annotated edition in translation providing scholarly estimates of the Julian dates for the events under discussion, it is only too easy to be blissfully unaware of the author’s extreme dating handicap

Evapotranspiration and Vegetation Cover Classifications Maps Based on Cloud Computing at the Arab Countries Scale

According to the most recent Köppen–Geiger classification, Arab countries are divided into seven climate classes. Ground data availability is limited in developing countries, and ground meteorological data are scarce and concentrated in a few locations, rather than station maintenance capability being adequate for the responsibilities. The current study uses remote sensing and meteorological data to create regional classification maps of reference evapotranspiration (ETo), potential crop evapotranspiration, and vegetation cover in Arab countries from 2005 to 2020. The Stand-alone Remote Sensing Approach to Estimate Reference Evapotranspiration (SARE) was used to estimate ETo using satellite data from 2005 to 2020. The Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were extracted from MODIS satellite data and used in the SARE model, in addition to elevation (E), Julian day (J), and Latitude (Lat). To validate the SARE model results, the FAO-Penman–Monteith model was applied to 35 ground meteorological stations distributed across Arab countries to cover all climate classes based on the most recent Köppen–Geiger climate classification. Google Earth Engine was used to create the classification. The statistical indices produced acceptable results, with average RMSE values ranging from 6.9 to 17.3 (mm/month), while correlation coefficient (r) and index of agreement (d) values are more significant than 0.9. To be included in the ETc calculation, the crop coefficient (Kc) was calculated using NDVI 250 m spatial resolution. The density of the vegetation cover is used to classify it (low to high). The average vegetation cover was calculated to be greater than 31.5 Mha. The minimum vegetation cover was 14.9 Mha, and the maximum vegetation cover was 49.2 Mha. 15.8 Mha can be cultivated without supplementary irrigation for at least one agricultural season, according to the rainfall classification map.

A catchment-scale model of river water quality by Machine Learning

Water quality is a concern in most river basins worldwide due to the widespread release of pollutants which impacts the freshwater ecosystems. Exploring the relationships between drivers and water quality parameters at the regional scale is key in the identification of appropriate actions for the reduction of water pollution. Regional models are the appropriate tool to achieve this, though their development poses relevant challenges because of the complexity and non-linearity of such relationships. Among the available approaches, Machine Learning (ML) is promising because of its capability to detect complex nonlinear relationships and flexibility in the parameterization, which is learned from data. In this work, we developed regional models of water temperature, dissolved oxygen, arsenic, sulfate and chloride concentrations, as well as electrical conductivity, by using two ML algorithms, Random Forest and Deep feed-forward Neural Network, and compared their performances against the standard Linear Regression model. Our results indicate that the two ML algorithms are much more accurate models for such variables than the classical Linear Regression model, with Deep feed-forward Neural Network being the most effective in identifying the reciprocal importance of the drivers and capturing nonlinear relationships between drivers and water quality variables. Our analysis also revealed that the Julian day and year at which the sample was taken surrogate the air temperature in modeling water temperature and dissolved oxygen, with only a slight performance reduction. Arsenic, sulfate, and chloride show more complex behaviors in which geogenic and anthropogenic sources are intertwined. Dilution exerts a role chiefly for arsenic concentration, which suggests a non-uniform, in space, geogenic origin for this variable.

Statistical Analysis for Prediction of Hydrological Events of Budelkani Watershed Area for Planning Rainfed Rice

Statistical analysis of hydrological event like rainfall was carried out for 23 years (1993-2015) for prediction of other hydrological events such as occurrence of effective monsoon (OEM), critical dry spell (CDS) and withdrawal of monsoon (WM). All events predicted by FLOOD software developed by IIT Kharagpur, West Bengal, India for different probability of exceedence (PE) levels from 10-90%. Their performances were indicated by the Chi-square test, mean absolute relative error (MARE), model efficiency (ME), root mean square error (RMSE) and coefficient of determination (CD). At 50% PE level, for OEM and WM, Gumbel (Extreme value minimum) and Pareto distributions were best fitted with 168th and 269th Julian days, representing June 17 and September 26, respectively. Average duration between OEM and WM was found to be 102 days. There were two critical dry spells (CDS) dominated at study area named to be CDS1 and CDS2. These two dry spells were occurred on 213th and 235th Julian days with 12 days duration each with starting date from 1st August and 24th August, respectively. Pareto and Beta distribution function were best fitted for the above CDSs on the aforesaid Julian Days (i.e. on 213th and 235th Julian days), while Gumbel (Extreme value minimum) and Beta distribution function were found to be the best for deciding the occurrences of CDS of 12 days duration at 50% PE level. The study revealed that the short duration rice would face less drought stress than the long duration rice. Hence, short duration rice variety of 100-110 days duration was selected.

Do Invasive and Naturalized Aphid Pest Populations Respond Differently to Climatic and Landscape Factors?

Ongoing environmental change affects pest populations, migration, and propensity to damage crops, but the responses to climatic drivers could vary among newly invasive and already naturalized closely related species. To compare these responses of a newly invasive aphid, Metopolophium festucae cerealium (Stroyan), with its naturalized congeneric [M. dirhodum (Walker)] and confamilial [Sitobian avenae (Fab.)], we conducted annual surveys over four years across a total of 141 winter wheat fields in the inland Pacific Northwest, USA. Key climatic factors (cumulative precipitation for each calendar year to sampling date, cumulative degree days), landscape factors (proportion of wheat and landscape diversity within the sample year), and Julian day were calculated for each sampling event, and aphid abundance by species, total aphid abundance, overall species richness, diversity, and aphid community composition were assessed. Metopolophium f. cerealium, the second most abundant species, was positively associated with precipitation, suggesting a projected increase in precipitation in winter and spring in the region could favor its establishment and expansion. Although M. dirhodum and S. avenae linearly (positively) associated with temperature, M. f. cerealium did not, indicating that continued warming may be detrimental to the species. Despite the weak impacts of landscape factors, our study indicated that more wheat generally facilitates cereal aphid abundance. Metopolophium f. cerealium abundance tended to be higher in earlier (May/early June vs. late June/July) samples when wheat crop could be vulnerable to aphid feeding. This study suggests that the new presence of M. f. cerealium has important pest management implications in the region.

Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito.

Weather Normalized Models (WNMs) are modeling methods used for assessing air contaminants under a business-as-usual (BAU) assumption. Therefore, WNMs are used to assess the impact of many events on urban pollution. Recently, different approaches have been implemented to develop WNMs and quantify the lockdown effects of COVID-19 on air quality, including Machine Learning (ML). However, more advanced methods, such as Deep Learning (DL), have never been applied for developing WNMs. In this study, we proposed WNMs based on DL algorithms, aiming to test five DL architectures and compare their performances to a recent ML approach, namely Gradient Boosting Machine (GBM). The concentrations of five air pollutants (CO, NO2, PM2.5, SO2, and O3) are studied in the city of Quito, Ecuador. The results show that Long-Short Term Memory (LSTM) and Bidirectional Recurrent Neural Network (BiRNN) outperform the other algorithms and, consequently, are recommended as appropriate WNMs to quantify the effects of the lockdowns on air pollution. Furthermore, examining the variable importance in the LSTM and BiRNN models, we identify that the most relevant temporal and meteorological features for predicting air quality are Hours (time of day), Index (1 is the first collected data and increases by one after each instance), Julian Day (day of the year), Relative Humidity, Wind Speed, and Solar Radiation. During the full lockdown, the concentration of most pollutants has decreased drastically: −48.75%, for CO, −45.76%, for SO2, −42.17%, for PM2.5, and −63.98%, for NO2. The reduction of this latter gas has induced an increase of O3 by +26.54%.

Misuses of the terms Day of the Year and Julian Day in agricultural and environmental sciences

The purpose of this short communication is to establish a discussion about the correct use of the terms Julian Day and Day of the Year. The Day of the Year is used for environmental modelling and calculating daily solar radiation and evapotranspiration. The concept of Julian Day is misused in the field of agricultural and environmental sciences. Specifically, the use of the term Julian Day might have been misleading or incorrect for the last ten years in the journal Agricultural Water Management. In this brief communication we intend to clarify the concept of Julian Day, and expose the most commonly used equations, so that the concept might be used with more rigor and precision in the future.

Stand age related differences in forest microclimate

Quantifying spatio-temporal variation in forest microclimate is important to understand the habitat associations of some species, identify potential refugia, and manage forests in a changing climate. We quantified relationships between microclimatic conditions and stand age using a detailed case study of the Mountain Ash (Eucalyptus regnans) forests in south-eastern Australia. We deployed sensor buttons to capture data on day-time and night-time seasonal (Julian Day) temperature profiles in even-aged stands that were 12-years-old, 30–40-years-old, ∼82–95-years-old, and 200+ -years-old. We constructed Bayesian generalised linear models (GLMs) to quantify the main and interacting effects of stand age and topographic factors and foliage cover on seasonal profiles for: median, maximum and variability in day-time and night-time temperature.The most parsimonious model for median day-time temperature included a main effect of midstory and overstory cover, both of which were inversely related to median day-time temperature. The most parsimonious model for median night-time temperature measures included an additive effect of forest age and seasonality. Temperatures for the youngest forests (that were 12-years-old) were different from other age classes and were characterised by the lowest median and minimum night-time temperatures.The most parsimonious model for day-time maximum temperature and day-time interquartile range included an interaction between forest age and Julian day. Day-time maximum temperatures were warmest in 12-year-old forest. The coolest maximum day-time temperatures in summer were generally in old growth stands (200+ -year-old). The greatest variability in maximum day-time temperatures was in 12-year-old forest whereas, in general, old growth stands experienced the least variable day-time temperatures. Our sensor data also showed that old growth forests were characterised by fewer days above 30, 35 and 40 degrees than 12-year old forest. Our empirical data contain compelling evidence that the youngest forests were characterised by the hottest day-time temperatures, the coldest night-time temperatures, and the most variable day-time and night-time temperature regimes. Age class differences in stand structure (e.g. canopy height, prevalence of large old trees and dense wet understorey) may explain stand age related seasonal differences in local microclimatic conditions.

Testing the potential of streamflow data to predict spring migration of ungulate herds.

In mountainous and high latitude regions, migratory animals exploit green waves of emerging vegetation coinciding with rising daily mean temperatures initiating snowmelt across the landscape. Snowmelt also causes rivers and streams draining these regions to swell, a process referred to as to as the 'spring pulse.' Networks of streamgages measuring streamflow in these regions often have long-term and continuous periods of record available in real-time and at the daily time step, and thus produce data with potential to predict temporal migration patterns for species exploiting green waves. We tested the potential of models informed by streamflow data to predict timing of spring migration of mule deer (Odocoileus hemionus) herds in a headwater basin of the Colorado River. Models using streamflow data were compared with those informed by traditional temperature-derived measures of the onset of spring. Non-parametric linear-regression techniques were used to test for temporal stationarity in each variable, and logistic-regression models were used to produce probabilities of migration initiation. Our analysis indicates that models using daily streamflow data can perform as well as those using temperature-derived data to predict past-migration patterns, and nearly as well in potential to forecast future migrations. The best performing model was used to generate probabilities of onset of migration for mule deer herds over the 69-year period-of-record from a streamgage. That model indicated spring migration has been trending toward earlier initiations, with modeled median initiations shifting from a Julian day of 123 in the mid 20th century to Julian day 115 over the most recent two decades. The period of 1960 to 1979 had the latest modeled median initiations with Julian day of 128. The analyses demonstrate promise for merging existing hydrologic and biological data collection platforms in these regions to explore timing of past migration patterns and predict migration onsets in real-time.

Did old Maya observe Mercury?

It is well known that the rich culture of old Maya contained, among other, also a very complicated and complex calendar, in which they recorded not only historical events, but also significant astronomical phenomena. Main source of information is the Dresden Codex, roughly covering the interval between 280 and 1325 AD. The problem of the so-called correlation between Mayan and our calendars (expressing the difference between Long Count of Mayan calendar and Julian date) is very old, there exist about fifty different solutions that mutually differ by up to hundreds of years. Out of these, historians mostly accept the so-called Goodman - Mart?nez - Thompson (GMT) value of 584 283 days, which is based almost entirely on historical events. On the contrary, we stressed very precisely dated astronomical data, demonstrated the contradictions of GMT with them, and derived the so-called B?hm correlation (BB) of 622 261 days, which is in excellent agreement with astronomical phenomena recorded in Dresden Codex. Maya researchers are mostly convinced that Maya did not pay much attention to Mercury. Here we conclude that the truth is opposite; we analyze the data in Dresden Codex and find many records corresponding to visibility of Mercury near its maximum elongations from the Sun, and also to their conjunctions.

Desempenho de fazenda de agricultura intensificada de baixo carbono no Cerrado brasileiro por meio de abordagens univariadas e multivariadas

ABSTRACT The low-carbon agriculture is one of the central themes in the climate agenda. This study aimed to evaluate the performance of a low carbon farm in the Maranhão State, Brazil, using univariate and multidimensional approaches. The experimental design consisted of three replications of five treatments (land uses) (Cerrado as a reference area; no-tillage soybean-off-season corn; no-tillage corn-soybean-off-season corn; no-tillage corn-soybean; no-tillage soybean-off-season corn-Brachiarya brizantha), in five periods (Julian days: 28, 48, 83, 138 and 154), totaling 75 observations. The data were analyzed using univariate and multidimensional approaches. A statistically significant interaction was observed between treatment and period, indicating that the responses to the treatment vary with time. The plot that showed the best performance was the reference area, followed by the no-tillage soybean-off-season corn treatment.

Uso e Cobertura do Solo após Eventos de Queimadas no Município de Floresta em Pernambuco

Queimadas são recorrentes no Brasil para limpeza e aumento de territórios. Análises temporais sobre o tema demonstram que o município de Floresta em Pernambuco se destaca no número de casos no mês de novembro de 2019. Este artigo tem como propósito coletar e quantificar a incidência dos focos de calor e áreas queimadas na região e mês supracitados, além das condições de uso e cobertura do solo baseado nas técnicas do sensoriamento remoto. Utilizou-se informações da Agência Pernambucana de Águas e Clima (APAC) para a climatologia. As bases do Banco de Dados de Queimadas foram empregadas na coleta e quantificação dos focos de calor. Imagens MODIS/TERRA, MODIS/Combinado e Landsat-8/OLI foram aplicadas na caracterização das áreas queimadas, interpretação de imagens e cálculo de índices de vegetação e de queimada. Foram identificadas 35 áreas de fogo, sendo 31 de focos de calor confirmados, totalizando 208. Dessas áreas, 7 coincidiram com o dia 06 de novembro de 2019, dia Juliano 310. Observou-se pelos índices de vegetação WDRVI, IAF e NDVI que o comportamento da vegetação saudável é uniforme, com densidade de baixa a moderada. Após a queimada, o uso e cobertura do solo se regenerou, não havendo modificação. O SAVI e os NBR e NBR2 expõem coeficientes de correlação positivos muito forte e forte, respectivamente, indicando a dependência de uma variável em relação a outra. Desse modo, o sensoriamento remoto se demonstrou eficaz no estudo das condições do uso e cobertura do solo, bem como a identificação das queimadas no município de Floresta.Palavras-Chave: sensoriamento remoto, imagens de satélites, focos de calor, índices de vegetação, índices de queimada. Land use and coverage after fire events in the municipality of Floresta in Pernambuco ABSTRACTFires are recurrent in Brazil to clean up and increase territories. Temporal analyzes on the subject show that the municipality of Floresta in Pernambuco stands out in the number of cases in the month of November 2019. This article aims to collect and quantify the incidence of hotspots and burned areas in the aforementioned region and month, in addition to of land use and cover conditions based on remote sensing techniques. Information from the Pernambuco Water and Climate Agency (APAC) was used for climatology. The databases of the Burning Database were used to collect and quantify hotspots. MODIS/TERRA, MODIS/Combinado and Landsat-8/OLI images were applied in the characterization of burned areas, image interpretation and calculation of vegetation and fire indexes. 35 fire areas were identified, 31 of which were confirmed hotspots, totaling 208. Of these areas, 7 coincided with November 6, 2019, Julian day 310. It was observed by the WDRVI, IAF and NDVI vegetation indices that the Healthy vegetation behavior is uniform, with low to moderate density. After the fire, the use and cover of the soil regenerated, with no change. The SAVI and the NBR and NBR2 exhibit very strong and strong positive correlation coefficients, respectively, indicating the dependence of one variable on the other. In this way, remote sensing proved to be effective in the study of land use and cover conditions, as well as the identification of fires in the municipality of Floresta.Keywords: remote sensing, satellite images, hotspots, vegetation indices, fire índices.

Factors affecting virus prevalence in honey bees in the Pacific-Northwest, USA

Global efforts to assess honey bee health show viruses are major stressors that undermine colony performance. Identifying factors that affect virus incidence, such as management practices and landscape context, could aid in slowing virus transmission. Here we surveyed viruses in honey bees from 86 sites in the Pacific Northwest, USA, and tested effects of regional bee density, movement associated with commercial pollination, julian date, and hive management on virus prevalence. We also explored patterns of virus co-occurrence and spatial autocorrelation to identify whether local transmission was a primary driver of pathogen distribution. Our surveys found widespread prevalence of Deformed wing virus (DWV), Sacbrood virus (SBV), and Black queen cell virus (BQCV). BQCV and SBV were most prolific in commercial apiaries, while Chronic bee paralysis virus (CPBV) was more common in hobbyist apiaries than commercial apiaries. DWV was most common in urban landscapes and was best predicted by mite prevalence and julian date, while the incidence of both SBV and BQCV were best predicted by regional apiary density. We did not find evidence of additional spatial autocorrelation for any viruses, although high co-occurrence suggests parallel transmission patterns. Our results support the importance of mite management in slowing virus spread and suggest that greater bee density increases transmission. Our study provides support that viruses are widespread in honey bees and connects known mechanisms of virus transmission to the distribution of pathogens observed across the Pacific Northwest.

Estimation of Surface and Near-Surface Air Temperatures in Arid Northwest China Using Landsat Satellite Images

Near-surface air (Ta) and land surface (Ts) temperatures are essential parameters for research in the fields of agriculture, hydrology, and ecological changes, which require accurate datasets with different temporal and spatial resolutions. However, the sparse spatial distribution of meteorological stations in Northwest China may not effectively provide high-precision Ta data. And it is not clear whether it is necessary to improve the accuracy of Ts which has the most influence on Ta. In response to this situation, the main objective of this study is to estimate Ta for Northwest China using multiple linear regression models (MLR) and random forest (RF) algorithms, based on Landsat 8 images and auxiliary data collected from 2014 to 2019. Ts, NDVI (Normalized Difference Vegetation Index), surface albedo, elevation, wind speed, and Julian day were variables to be selected, then used to estimate the daily average Ta after analysis and adjustment. Also, the Radiative Transfer Equation (RTE) method for calculating Ts would be corrected by NDVI (RTE-NDVI). The results show that: 1) The accuracy of the surface temperature (Ts) was improved by using RTE-NDVI; 2) Both MLR and RF models are suitable for estimating Ta in areas with few meteorological stations; 3) Analyzing the temporal and spatial distribution of errors, it is found that the MLR model performs well in spring and summer, and is lower in autumn, and the accuracy is higher in plain areas away from mountains than in mountainous areas and nearby areas. This study shows that through appropriate selection and combination of variables, the accuracy of estimating the pixel-scale Ta from satellite remote sensing data can be improved in the area that has less meteorological data.

Profitability of dual‐purpose rye cover crop as influenced by harvesting date

AbstractHarvesting annual winter cereal rye (Secale cereale L.) (WCR) as an emergency forage could offset cover crop establishment costs, facilitate WCR adoption, and still provide multiple ecosystem benefits. A five site‐year trial was conducted in Colorado (CO) and Illinois (IL) to evaluate the effect of harvest date on WCR forage yield, quality, and its economic performance. From March to April, WCR dry matter (DM) yield increased exponentially in CO and linearly in IL. The DM yield at Julian day (DOY) 112–116 in CO was 6.9, 5.0, and 5.2 Mg ha–1 in 2018, 2019, and 2020, respectively, compared with 4.7 and 2.7 Mg ha–1 in IL in 2019 and 2020, respectively. Delayed harvesting increased acid detergent fiber and neutral detergent fiber concentrations and decreased crude, total digestible nutrients, and relative feed quality. Yield–quality trade‐off showed that forage yield increased rapidly but forage quality declined after DOY 105–108. Economic analysis, including cost of nutrient removal and 10% cash crop (corn [Zea mays L.]) yield penalty following WCR production revealed harvesting WCR biomass as forage was economically feasible in four out of five site‐years at hay price over $132 Mg–1. Eliminating corn yield penalty indicated profitability in four site‐years at hay price of ≥$110 Mg–1 and removing nutrient removal costs made all site‐years profitable at hay price of ≥$110 Mg–1. It was concluded that harvesting WCR biomass can be a profitable and effective strategy for sustainable intensification that can offer environmental stewardship and economic benefit.