A Coupled Remote Sensing and Simplified Surface Energy Balance Approach to Estimate Actual Evapotranspiration from Irrigated Fields

Accurate crop performance monitoring and production estimation are critical for timely assessment of the food balance of several countries in the world. Since 2001, the Famine Early Warning Systems Network (FEWS NET) has been monitoring crop performance and relative production using satellite-derived data and simulation models in Africa, Central America, and Afghanistan where ground-based monitoring is limited because of a scarcity of weather stations. The commonly used crop monitoring models are based on a crop water-balance algorithm with inputs from satellite-derived rainfall estimates. These models are useful to monitor rainfed agriculture, but they are ineffective for irrigated areas. This study focused on Afghanistan, where over 80 percent of agricultural production comes from irrigated lands. We developed and implemented a Simplified Surface Energy Balance (SSEB) model to monitor and assess the performance of irrigated agriculture in Afghanistan using a combination of 1-km thermal data and 250-m Normalized Difference Vegetation Index (NDVI) data, both from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. We estimated seasonal actual evapotranspiration (ETa) over a period of six years (2000-2005) for two major irrigated river basins in Afghanistan, the Kabul and the Helmand, by analyzing up to 19 cloud-free thermal and NDVI images from each year. These seasonal ETa estimates were used as relative indicators of year-to-year production magnitude differences. The temporal water-use pattern of the two irrigated basins was indicative of the cropping patterns specific to each region. Our results were comparable to field reports and to estimates based on watershed-wide crop water-balance model results. For example, both methods found that the 2003 seasonal ETa was the highest of all six years. The method also captured water management scenarios where a unique year-to-year variability was identified in addition to water-use differences between upstream and downstream basins. A major advantage of the energy-balance approach is that it can be used to quantify spatial extent of irrigated fields and their water-use dynamics without reference to source of water as opposed to a water-balance model which requires knowledge of both the magnitude and temporal distribution of rainfall and irrigation applied to fields

Modeling rain-fed maize vulnerability to droughts using the standardized precipitation index from satellite estimated rainfall-Southern Malawi case study

During 1990s, disaster risk reduction emerged as a novel, proactive approach to managing risks from natural hazards. The World Bank, USAlD, and other international donor agencies began making efforts to mainstream disaster risk reduction in countries whose population and economies were heavily dependent on rain-fed agriculture. This approach has more significance in light of the increasing climatic hazard patterns and the climate scenarios projected for different hazard prone countries in the world. The Famine Early Warning System Network (FEWS NET) has been monitoring the food security issues in the sub-Saharan Africa, Asia and in Haiti. FEWS NET monitors the rainfall and moisture availability conditions with the help of NOAA RFE2 data for deriving food security status in Africa. This paper highlights the efforts in using satellite estimated rainfall inputs to develop drought vulnerability models in the drought prone areas in Malawi. The satellite RFE2 based SPI corresponding to the critical tasseling and silking phases (in the months of January, February, and March) were statistically regressed with drought-induced yield losses at the district level. The analysis has shown that the drought conditions in February and early March lead to most damage to maize yields in this region. The district-wise vulnerabilities to drought were up scaled to obtain a regional maize vulnerability model for southern Malawi. The results would help in establishing an early monitoring mechanism for drought impact assessment, give the decision makers additional time to assess seasonal outcomes, and identify potential food-related hazards in Malawi

Evaluating ESA CCI Soil Moisture in East Africa

To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASAs Soil Moisture Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R greater than 0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions

Future Opportunities and Challenges in Remote Sensing of Drought

The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Vegetation Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation, as summarized by Anyamba and Tucker (2012, Chapter 2). Other indices such as the Vegetation Health Index (VHI) (Kogan, 1995) were also developed during this time period and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers like AVHRR could provide for operational drought monitoring through near-daily, synoptic observations of earth’s land surface. However, the advancement of satellite remote sensing for drought monitoring was limited by the relatively few spectral bands on operational global sensors such as AVHRR, along with a relatively short observational record

Warming of the Indian Ocean Threatens Eastern and Southern Africa, but could be Mitigated by Agricultural Development

Since 1980, the number of undernourished people in eastern and southern Africa has more than doubled. Rural development stalled and rural poverty expanded during the 1990s. Population growth remains very high and declining per capita agricultural capacity retards progress towards Millennium Development goals. Analyses of in situ station data and satellite observations of precipitation identify another problematic trend. Main growing season rainfall receipts have diminished by approximately 15% in food insecure countries clustered along the western rim of the Indian Ocean. Occurring during the main growing seasons in poor countries dependent on rain fed agriculture, these declines are societally dangerous. Will they persist or intensify? Tracing moisture deficits upstream to an anthropogenically warming Indian Ocean leads us to conclude that further rainfall declines are likely. We present analyses suggesting that warming in the central Indian Ocean disrupts onshore moisture transports, reducing continental rainfall. Thus late 20th century anthropogenic Indian Ocean warming has probably already produced societally dangerous climate change by creating drought and social disruption in some of the world's most fragile food economies. We quantify the potential impacts of the observed precipitation and agricultural capacity trends by modeling millions of undernourished people as a function of rainfall, population, cultivated area, seed and fertilizer use. Persistence of current tendencies may result in a 50% increase in undernourished people. On the other hand, modest increases in per capita agricultural productivity could more than offset the observed precipitation declines. Investing in agricultural development can help mitigate climate change while decreasing rural poverty and vulnerability

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer's accuracy of 93% and a user's accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of >= 95% for cultivated croplands and >= 76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season

HIV Integration Targeting: A Pathway Involving Transportin-3 and the Nuclear Pore Protein RanBP2

Genome-wide siRNA screens have identified host cell factors important for efficient HIV infection, among which are nuclear pore proteins such as RanBP2/Nup358 and the karyopherin Transportin-3/TNPO3. Analysis of the roles of these proteins in the HIV replication cycle suggested that correct trafficking through the pore may facilitate the subsequent integration step. Here we present data for coupling between these steps by demonstrating that depletion of Transportin-3 or RanBP2 altered the terminal step in early HIV replication, the selection of chromosomal sites for integration. We found that depletion of Transportin-3 and RanBP2 altered integration targeting for HIV. These knockdowns reduced HIV integration frequency in gene-dense regions and near gene-associated features, a pattern that differed from that reported for depletion of the HIV integrase binding cofactor Psip1/Ledgf/p75. MLV integration was not affected by the Transportin-3 knockdown. Using siRNA knockdowns and integration targeting analysis, we also implicated several additional nuclear proteins in proper target site selection. To map viral determinants of integration targeting, we analyzed a chimeric HIV derivative containing MLV gag, and found that the gag replacement phenocopied the Transportin-3 and RanBP2 knockdowns. Thus, our data support a model in which Gag-dependent engagement of the proper transport and nuclear pore machinery mediate trafficking of HIV complexes to sites of integration

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19