The warning classification scheme of ASAP – Anomaly hot Spots of Agricultural Production

Agriculture monitoring, and in particular food security, requires near real time information on crop growing conditions for early detection of possible production deficits. Anomaly maps and time profiles of remote sensing derived indicators related to crop and vegetation conditions can be accessed online thanks to a rapidly growing number of web based portals. However, timely and systematic global analysis and coherent interpretation of such information, as it is needed for example for the United Nation Sustainable Development Goal 2 related monitoring, remains challenging. With the ASAP system (Anomaly hot Spots of Agricultural Production) we propose a two-step analysis to provide timely warning of production deficits in water-limited agricultural systems worldwide every month. The first step is fully automated and aims at classifying each sub-national administrative unit (Gaul 1 level, i.e. first sub-national level) into a number of possible warning levels, ranging from “none” to level 4++. Warnings are triggered only during the crop growing season, as derived from a remote sensing based phenology. The classification system takes into consideration the fraction of the agricultural area for each Gaul 1 unit that is affected by a severe anomaly of two rainfall-based indicators (the Standardized Precipitation Index computed at 1 and 3-month scale), one biophysical indicator (the anomaly of the cumulative Normalized Difference Vegetation Index from the start of the growing season), and the timing during the growing cycle at which the anomaly occurs. The level (i.e. severity) of the warning thus depends on: the timing, the nature and number of indicators for which an anomaly is detected, and the agricultural area affected. Maps and summary information are published on a web GIS. The second step, not described in detail in this manuscript, involves the verification of the automatic warnings by agricultural analysts to identify the countries (national level) with potentially critical conditions that are marked as “hot spots”. This report focusses on the technical description of the automatic warning classification scheme version 1.0.JRC.D.5 - Food Securit

The warning classification scheme of ASAP – Anomaly hot Spots of Agricultural Production, v1.1

Agriculture monitoring, and in particular food security, requires near real time information on crop growing conditions for early detection of possible production deficits. Anomaly maps and time profiles of remote sensing derived indicators related to crop and vegetation conditions can be accessed online thanks to a rapidly growing number of web based portals. However, timely and systematic global analysis and coherent interpretation of such information, as it is needed for example for the United Nation Sustainable Development Goal 2 related monitoring, remains challenging. With the ASAP system (Anomaly hot Spots of Agricultural Production) we propose a two-step analysis to provide timely warning of production deficits in water-limited agricultural systems worldwide every month. The first step is fully automated and aims at classifying each sub-national administrative unit (Gaul 1 level, i.e. first sub-national level) into a set of possible warning levels, ranging from “none” to level 4. Warnings are triggered only during the crop growing season, as derived from a remote sensing based phenology. The classification system takes into consideration the fraction of the agricultural area for each Gaul 1 unit that is affected by a severe anomaly of two rainfall-based indicators (the Standardized Precipitation Index computed at 1 and 3-month scale), one biophysical indicator (the anomaly of the cumulative Normalized Difference Vegetation Index from the start of the growing season), and the timing during the growing cycle at which the anomaly occurs. The level (i.e. severity) of the warning thus depends on: the timing, the nature and number of indicators for which an anomaly is detected, and the agricultural area affected. Maps and summary information are published in the Warning Explorer available at http://mars.jrc.ec.europa.eu/asap. The second step, not described in this manuscript, involves the verification of the automatic warnings by agricultural analysts to identify the countries with potentially critical conditions at the national level that are marked as “hot spots”. This report focuses on the technical description of the automatic warning classification scheme version 1.1.JRC.D.5 - Food Securit

Presence of Anti-Microbial Antibodies in Liver Cirrhosis – A Tell-Tale Sign of Compromised Immunity?

Bacterial translocation plays important role in the complications of liver cirrhosis. Antibody formation against various microbial antigens is common in Crohn's disease and considered to be caused by sustained exposure to gut microflora constituents. We hypothesized that anti-microbial antibodies are present in patients with liver cirrhosis and may be associated with the development of bacterial infections.<0.001, OR:2.02) by Cox-regression analysis.The present study suggests that systemic reactivity to microbial components reflects compromised mucosal immunity in patients with liver cirrhosis, further supporting the possible role of bacterial translocation in the formation of anti-microbial antibodies

The peak-flux of GRB 221009A measured with GRBAlpha

The brightest gamma-ray burst ever observed, long-duration GRB 221009A, was detected by GRBAlpha nano-satellite without saturation. We present light curves of the prompt emission in 13 energy bands, from 80 keV to 950 keV, and perform a spectral analysis to calculate the peak flux and peak isotropic-equivalent luminosity. Since the satellite's attitude information is not available for the time of this GRB, more than 200 incident directions were probed in order to find the median luminosity and its systematic uncertainty. We found that the peak flux in the $80-800$ keV range (observer frame) was $F_{\rm{ph}}^{\rm{p}}=1300_{-200}^{+1200}$ ph cm$^{-2}$s$^{-1}$ or $F_{\rm{erg}}^{\rm{p}}=5.7_{-0.7}^{+3.7}\times10^{-4}$ erg cm$^{-2}$s$^{-1}$ and the fluence in the same energy range of the first GRB episode lasting 300 s, which was observable by GRBAlpha, was $S=2.2_{-0.3}^{+1.4}\times10^{-2}$ erg cm$^{-2}$ or $S^{\rm{bol}}=4.9_{-0.5}^{+0.8}\times10^{-2}$ erg cm$^{-2}$ for the extrapolated range of $0.9-8,690$ keV. We infer the isotropic-equivalent released energy of the first GRB episode to be $E_{\rm{iso}}^{\rm{bol}}=2.8_{-0.5}^{+0.8}\times10^{54}$ erg in the $1-10,000$ keV band (rest frame at $z=0.15$). The peak isotropic-equivalent luminosity in the $92-920$ keV range (rest frame) was $L_{\rm{iso}}^{\rm{p}}=3.7_{-0.5}^{+2.5}\times10^{52}$ erg s$^{-1}$ and the bolometric peak isotropic-equivalent luminosity was $L_{\rm{iso}}^{\rm{p,bol}}=8.4_{-1.5}^{+2.5}\times10^{52}$ erg s$^{-1}$ (4 s scale) in the $1-10,000$ keV range (rest frame). The peak emitted energy is $E_p^\ast=E_p(1+z)=1120\pm470$ keV. Our measurement of $L_{\rm{iso}}^{\rm{p,bol}}$ is consistent with the Yonetoku relation. It is possible that, due to the spectral evolution of this GRB and orientation of GRBAlpha at the peak time, the true values of peak flux, fluence, $L_{\rm{iso}}$, and $E_{\rm{iso}}$ are even higher. [abridged]Comment: 7 pages, 7 figures, 1 table, accepted for publication in Astronomy & Astrophysic

Using global remote sensing and weather data efficiently for agricultural hotspots monitoring anywhere anytime: the ASAP online system

&amp;lt;p&amp;gt;Monitoring agricultural production in vulnerable developing countries is important for food security assessment and requires near real-time (NRT) information on crop growing conditions for early detection of possible production deficits. The public online ASAP system (Anomaly hot Spots of Agricultural Production) is an early warning decision support tool based on weather data and direct observation of crop status as provided by remote sensing. Although decision makers and food security analysts are the main targeted user groups, all the information is fully made available to the public in a simple and well documented online platform. The information further contributes to multi-agency early warning products such as the GEOGLAM Crop Monitor for Early Warning and food security assessments following the IPC-Cadre Harmonis&amp;amp;#233; framework.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Low resolution remote sensing (1 km) and meteorological (5-25 km) data are processed automatically every 10 days and vegetation anomaly warnings are triggered at the first sub-national administrative level. The severity of the warnings is based on the observed land surface phenology and three main derived indicators computed at the 1 km grid level: a proxy of the current season biomass production (the cumulative value of the Normalized Difference Vegetation index from the start of season); an indicator of precipitation deficit (the Standardized Precipitation Index at the 3 month scale); and a water-balance model output (the Water Requirement Satisfaction Index).Warning maps and summary information are published on a web GIS every ten days and then further analyzed by analysts every month. This results in the identification of hotspot countries with potentially critical crop or rangelands production conditions.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In addition to the hotspots analysis and the warning explorer, users can also zoom in to the parcel level thank to the so called High Resolution Viewer, a web interface based on Google Earth Engine that allows to visualize Sentinels (1 and 2) and Landsat imagery, plot temporal profiles and perform basic anomaly operation (e.g. current year NDVI anomaly with respect to a reference year). &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In the near future it is planned to make the anomaly warnings available also at the second sub-national level and to integrate meteorological forecasts in the warning system.&amp;lt;/p&amp;gt; </jats:p

ASAP: a new global early warning system to detect Anomaly hot Spots of Agricultural Production for food security analysis

Monitoring crop and rangeland conditions is highly relevant for early warning and response planning in food insecure areas of the world. Satellite remote sensing offers a unique opportunity to obtain relevant and timely information in such areas where ground data are scattered, non-homogenous, or frequently unavailable. Rainfall estimates provide an outlook of the drivers of vegetation growth, whereas time series of satellite-based biophysical indicators at high temporal resolution provide key information about vegetation status in near real-time and over large areas. The new early warning decision support system ASAP (Anomaly hot Spots of Agricultural Production) builds on the experience of the MARS crop monitoring activities for food insecure areas that have started in the early 2000’s and aims at providing information about possible crop production anomalies. The information made available on the website (https://mars.jrc.ec.europa.eu/asap/) directly supports multi-agency early warning initiatives such as for example the GEOGLAM Crop Monitor for Early Warning and provides inputs to more detailed food security assessments that are the basis for the annual Global Report on Food Crises. ASAP is a two-step analysis framework, with a first fully automated step classifying the first sub-national level administrative units into four agricultural production deficit warning categories. Warnings are based on rainfall and vegetation index anomalies computed over crop and rangeland areas. Warnings take into account the timing during the crop season at which they occur using remote sensing derived phenology per-pixel.. The second step involves the analysis at country level by JRC crop monitoring experts of all the information available, including the automatic warnings, crop production and food security-tailored media analysis, high-resolution imagery (e.g. Landsat 8, Sentinel 1 and 2) processed in Google Earth Engine and ancillary maps, graphs and statistics derived from a set of indicators. Countries with potentially critical conditions are marked as minor or major hotspots and a global overview is provided together with short national level narratives.JRC.D.5 - Food Securit