The Use of Landsat 8 and Sentinel-2 Data and Meterological Observations for Winter Wheat Yield Assessment

This study focuses on winter wheat yield assessment from NASA's Harmonized Landsat Sentinel-2 (HLS) product and meteorological observations through phenological fitting. Vegetation indices (VIs), namely difference vegetation index (DVI), normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI2), extracted from satellite optical data, are fitted per pixel against accumulated growing degree days (AGDD) using a quadratic function. Accumulated VIs are correlated against winter wheat yields. Results show a better performance from DVI compared to NDVI and EVI2

The CACAO Method for Smoothing, Gap Filling, and Characterizing Seasonal Anomalies in Satellite Time Series

Consistent, continuous, and long time series of global biophysical variables derived from satellite data are required for global change research. A novel climatology fitting approach called CACAO (Consistent Adjustment of the Climatology to Actual Observations) is proposed to reduce noise and fill gaps in time series by scaling and shifting the seasonal climatological patterns to the actual observations. The shift and scale CACAO parameters adjusted for each season allow quantifying shifts in the timing of seasonal phenology and inter-annual variations in magnitude as compared to the average climatology. CACAO was assessed first over simulated daily Leaf Area Index (LAI) time series with varying fractions of missing data and noise. Then, performances were analyzed over actual satellite LAI products derived from AVHRR Long-Term Data Record for the 1981-2000 period over the BELMANIP2 globally representative sample of sites. Comparison with two widely used temporal filtering methods-the asymmetric Gaussian (AG) model and the Savitzky-Golay (SG) filter as implemented in TIMESAT-revealed that CACAO achieved better performances for smoothing AVHRR time series characterized by high level of noise and frequent missing observations. The resulting smoothed time series captures well the vegetation dynamics and shows no gaps as compared to the 50-60% of still missing data after AG or SG reconstructions. Results of simulation experiments as well as confrontation with actual AVHRR time series indicate that the proposed CACAO method is more robust to noise and missing data than AG and SG methods for phenology extraction

Enhancing Remote Sensing Based Yield Forecasting: Application to Winter Wheat in United States

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. In Becker-Reshef et al. (2010) and Franch et al. (2015) we developed an empirical generalized model for forecasting winter wheat yield. In this study we present a new model based on the extrapolation of the pure wheat signal (100 percent of wheat within the pixel) from MODIS (Moderate-resolution Imaging Spectroradiometer) data at 1-kilometer resolution and using the Difference Vegetation Index (DVI). The model has been applied to monitor the national and state level yield of winter wheat in the United States from 2001 to 2016

Evaluation of Near-Surface Air Temperature from Reanalysis over the United States and Ukraine: Application to Winter Wheat Yield Forecasting

In this work we evaluate the near-surface air temperature datasets from the ERA-Interim, JRA55, MERRA2, NCEP1, and NCEP2 reanalysis projects. Reanalysis data were first compared to observations from weather stations located on wheat areas of the United States and Ukraine, and then evaluated in the context of a winter wheat yield forecast model. Results from the comparison with weather station data showed that all datasets performed well (r2>0.95) and that more modern reanalysis such as ERAI had lower errors (RMSD ~ 0.9) than the older, lower resolution datasets like NCEP1 (RMSD ~ 2.4). We also analyze the impact of using surface air temperature data from different reanalysis products on the estimations made by a winter wheat yield forecast model. The forecast model uses information of the accumulated Growing Degree Day (GDD) during the growing season to estimate the peak NDVI signal. When the temperature data from the different reanalysis projects were used in the yield model to compute the accumulated GDD and forecast the winter wheat yield, the results showed smaller variations between obtained values, with differences in yield forecast error of around 2% in the most extreme case. These results suggest that the impact of temperature discrepancies between datasets in the yield forecast model get diminished as the values are accumulated through the growing season

Portosystemic shunts in dogs and cats: definition, epidemiology and clinical signs of congenital portosystemic shunts

SAMENVATTING Congenitale portosystemische shunts (CPSS) zijn hepatische bloedvatafwijkingen die bij elk honden-of kattenras kunnen voorkomen. Extrahepatische CPSS komen vooral voor bij kleine honden en katten, terwijl intrahepatische CPSS vooral grote hondenrassen aantasten. Voor sommige hondenrassen is een erfelijke basis vastgesteld. Aangetaste dieren worden meestal op jonge leeftijd aangeboden met variërende neurologische, gastro-intestinale, urinaire of andere klachten. Symptomen te wijten aan hepatische encefalopathie nemen dikwijls de overhand. De pathogenese van dit syndroom is tot nu toe nog niet volledig gekend en is vermoedelijk multifactorieel. De onderliggende oorzaak is vermoedelijk de invloed op de hersenen van één of meerdere toxinen die normaal gezien door de lever ontgiftigd zouden moeten worden. Katten met CPSS vertonen zeer vaak ptyalisme

Fractional snow cover in the Colorado and Rio Grande basins, 1995-2002

A cloud-masked fractional snow-covered area (SCA) product gridded at 1 km was developed from the advanced very high resolution radiometer for the Colorado River and upper Rio Grande basins for 1995-2002. Cloud cover limited SCA retrievals on any given 1-km2 pixel to on average once per week. There were sufficient cloud-free scenes to map SCA over at least part of the basins up to 21 days per month, with 3 months having only two scenes sufficiently cloud free to process. In the upper Colorado and upper Grande, SCA peaked in February-March. Maxima were 1-2 months earlier in the lower Colorado. Averaged over a month, as much as 32% of the upper Colorado and 5.5% of the lower Colorado were snow covered. Snow cover persisted longest at higher elevations for both wet and dry years. Interannual variability in snow cover persistence reflected wet-dry year differences. Compared with an operational (binary) SCA product produced by the National Operational Hydrologic Remote Sensing Center, the current products classify a lower fraction of pixels as having detectable snow and being cloud covered (5.5% for SCA and 6% for cloud), with greatest differences in January and June in complex, forested terrain. This satellite-derived subpixel determination of snow cover provides the potential for enhanced hydrologic forecast abilities in areas of complex, snow-dominated terrain. As an example, we merged the SCA product with interpolated ground-based snow water equivalent (SWE) to develop a SWE time series. This interpolated, masked SWE peaked in April, after SCA peaked and after some of the lower-elevation snow cover had melted. Copyright 2008 by the American Geophysical Union

Optical Properties of Boreal Region Biomass Burning Aerosols in Central Alaska and Seasonal Variation of Aerosol Optical Depth at an Arctic Coastal Site

Long-term monitoring of aerosol optical properties at a boreal forest AERONET site in interior Alaska was performed from 1994 through 2008 (excluding winter). Large interannual variability was observed, with some years showing near background aerosol optical depth (AOD) levels (<0.1 at 500 nm) while 2004 and 2005 had August monthly means similar in magnitude to peak months at major tropical biomass burning regions. Single scattering albedo (omega (sub 0); 440 nm) at the boreal forest site ranged from approximately 0.91 to 0.99 with an average of approximately 0.96 for observations in 2004 and 2005. This suggests a significant amount of smoldering combustion of woody fuels and peat/soil layers that would result in relatively low black carbon mass fractions for smoke particles. The fine mode particle volume median radius during the heavy burning years was quite large, averaging approximately 0.17 micron at AOD(440 nm) = 0.1 and increasing to approximately 0.25 micron at AOD(440 nm) = 3.0. This large particle size for biomass burning aerosols results in a greater relative scattering component of extinction and, therefore, also contributes to higher omega (sub 0). Additionally, monitoring at an Arctic Ocean coastal site (Barrow, Alaska) suggested transport of smoke to the Arctic in summer resulting in individual events with much higher AOD than that occurring during typical spring Arctic haze. However, the springtime mean AOD(500 nm) is higher during late March through late May (approximately 0.150) than during summer months (approximately 0.085) at Barrow partly due to very few days with low background AOD levels in spring compared with many days with clean background conditions in summer