Molly E. Brown

An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data

Daily daytime Advanced Very High Resolution Radiometer (AVHRR) 4‐km global area coverage data have been processed to produce a Normalized Difference Vegetation Index (NDVI) 8‐km equal‐area dataset from July 1981 through December 2004 for all continents except Antarctica. New features of this dataset include bimonthly composites, NOAA‐9 descending node data from August 1994 to January 1995, volcanic stratospheric aerosol correction for 1982–1984 and 1991–1993, NDVI normalization using empirical mode decomposition/reconstruction to minimize varying solar zenith angle effects introduced by orbital drift, inclusion of data from NOAA‐16 for 2000–2003 and NOAA‐17 for 2003–2004, and a similar dynamic range with the MODIS NDVI. Two NDVI compositing intervals have been produced: a bimonthly global dataset and a 10‐day Africa‐only dataset. Post‐processing review corrected the majority of dropped scan lines, navigation errors, data drop outs, edge‐of‐orbit composite discontinuities, and other artefacts in the composite NDVI data. All data are available from the University of Maryland Global Land Cover Facility (http://glcf.umiacs.umd.edu/data/gimms/).

Food Security Under Climate Change

Food insecurity is likely to increase under climate change, unless early warning systems and development programs are used more effectively.

Warming of the Indian Ocean threatens eastern and southern African food security 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 toward Millennium Development goals. Analyses of in situ station data and satellite observations of precipitation have identified another problematic trend: main growing-season rainfall receipts have diminished by ≈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 worlds 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 by 2030. 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.

Evaluation of the consistency of long-term NDVI time series derived from AVHRR,SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors

This paper evaluates the consistency of the Normalized Difference Vegetation Index (NDVI) records derived from Advanced Very High Resolution Radiometer (AVHRR), SPOT-Vegetation, SeaWiFS, Moderate Resolution Imaging Spectroradiometer, and Landsat ETM+. We used independently derived NDVI from atmospherically corrected ETM+ data at 13 Earth Observation System Land Validation core sites, eight locations of drought, and globally aggregated one-degree data from the four coarse resolution sensors to assess the NDVI records agreement. The objectives of this paper are to: 1) compare the absolute and relative differences of the vegetation signal across these sensors from a user perspective, and, to a lesser degree, 2) evaluate the possibility of merging the AVHRR historical data record with that of the more modern sensors in order to provide historical perspective on current vegetation activities. The statistical and correlation analyses demonstrate that due to the similarity in their overall variance, it is not necessary to choose between the longer time series of AVHRR and the higher quality of the more modern sensors. The long-term AVHRR-NDVI record provides a critical historical perspective on vegetation activities necessary for global change research and, thus, should be the basis of an intercalibrated, sensor-independent NDVI data record. This paper suggests that continuity is achievable given the similarity between these datasets

Declining global per capita agricultural production and warming oceans threaten food security

Despite accelerating globalization, most people still eat food that is grown locally. Developing countries with weak purchasing power tend to import as little food as possible from global markets, suffering consumption deficits during times of high prices or production declines. Local agricultural production, therefore, is critical to both food security and economic development among the rural poor. The level of local agricultural production, in turn, will be determined by the amount and quality of arable land, the amount and quality of agricultural inputs (fertilizer, seeds, pesticides, etc.), as well as farm-related technology, practices and policies. This paper discusses several emerging threats to global and regional food security, including declining yield gains that are failing to keep up with population increases, and warming in the tropical Indian Ocean and its impact on rainfall. If yields continue to grow more slowly than per capita harvested area, parts of Africa, Asia and Central and Southern America will experience substantial declines in per capita cereal production. Global per capita cereal production will potentially decline by 14% between 2008 and 2030. Climate change is likely to further affect food production, particularly in regions that have very low yields due to lack of technology. Drought, caused by anthropogenic warming in the Indian and Pacific Oceans, may also reduce 21st century food availability in some countries by disrupting moisture transports and bringing down dry air over crop growing areas. The impacts of these circulation changes over Asia remain uncertain. For Africa, however, Indian Ocean warming appears to have already reduced rainfall during the main growing season along the eastern edge of tropical Africa, from southern Somalia to northern parts of the Republic of South Africa. Through a combination of quantitative modeling of food balances and an examination of climate change, this study presents an analysis of emerging threats to global food security.

Generating a long-term land data record from the AVHRR and MODIS Instruments

The goal of NASAs land long term data record (LTDR) project is to produce a consistent long term data set from the AVHRR and MODIS instruments for land climate studies. The project will create daily surface reflectance and normalized difference vegetation index (NDVI) products at a resolution of 0.05deg, which is identical to the climate modeling grid (CMG) used for MODIS products from EOS Terra and Aqua. Higher order products such as burned area, land surface temperature, albedo, bidirectional reflectance distribution function (BRDF) correction, leaf area index (LAI), and fraction of photosynthetically active radiation absorbed by vegetation (fPAR), will be created. The LTDR project will reprocess global area coverage (GAC) data from AVHRR sensors onboard NOAA satellites by applying the preprocessing improvements identified in the AVHRR Pathfinder II project and atmospheric and BRDF corrections used in MODIS processing. The preprocessing improvements include radiometric in-flight vicarious calibration for the visible and near infrared channels and inverse navigation to relate an Earth location to each sensor instantaneous field of view (IFOV). Atmospheric corrections for Rayleigh scattering, ozone, and water vapor are undertaken, with aerosol correction being implemented. The LTDR also produces a surface reflectance product for channel 3 (3.75 mum). Quality assessment (QA) is an integral part of the LTDR production system, which is monitoring temporal trends in the AVHRR products using time-series approaches developed for MODIS land product quality assessment. The land surface reflectance products have been evaluated at AERONET sites. The AVHRR data record from LTDR is also being compared to products from the PAL (pathfinder AVHRR land) and GIMMS (global inventory modeling and mapping studies) systems to assess the relative merits of this reprocessing vis-a-vis these existing data products. The LTDR products and associated information can be found at http://ltdr.nascom.nasa.gov/ltdr/ ltdr.html.

Impacts of El Niño Southern Oscillation on the global yields of major crops

The monitoring and prediction of climate-induced variations in crop yields, production and export prices in major food-producing regions have become important to enable national governments in import-dependent countries to ensure supplies of affordable food for consumers. Although the El Niño/Southern Oscillation (ENSO) often affects seasonal temperature and precipitation, and thus crop yields in many regions, the overall impacts of ENSO on global yields are uncertain. Here we present a global map of the impacts of ENSO on the yields of major crops and quantify its impacts on their global-mean yield anomalies. Results show that El Niño likely improves the global-mean soybean yield by 2.1-5.4% but appears to change the yields of maize, rice and wheat by -4.3 to +0.8%. The global-mean yields of all four crops during La Niña years tend to be below normal (-4.5 to 0.0%). Our findings highlight the importance of ENSO to global crop production.

Markets, Climate Change, and Food Security in West Africa

Food price fluctuations, which will be exacerbated by climate change, make West African food security even more tenuous.

NASA's Soil Moisture Active Passive (SMAP) Mission and Opportunities for Applications Users

Water in the soil, both its amount (soil moisture) and its state (freeze/thaw), plays a key role in water and energy cycles, in weather and climate, and in the carbon cycle. Additionally, soil moisture touches upon human lives in a number of ways from the ravages of flooding to the needs for monitoring agricultural and hydrologic droughts. Because of their relevance to weather, climate, science, and society, accurate and timely measurements of soil moisture and freeze/thaw state with global coverage are critically important.

Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS

The long term Advanced Very High Resolution Radiometer (AVHRR)‐Normalized Difference Vegetation Index (NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non‐stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor‐specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at 1° is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product.