B13D-1518
AmericaView – A State-Based Remote Sensing Initiative Integrating Remote Sensing Data Into Geospatial Education and Applications
AmericaView (AV) is a national program created to advance the availability, timely distribution, and widespread use of land remote sensing data, especially among users within the university and government communities. Since the 1970s the federal government and private sector have spent billions of dollars on satellite-based earth observing systems, but distribution of data and development of real-world applications have been tough issues for the government and the academic research communities. It has often been hard for researchers to use or even access the data, particularly at smaller schools or research facilities, hindering applied research and current and future workforce development. Many state and local agencies working with applied research programs have not been able to effectively integrate remote sensing data into their geospatial management or decision-support programs. AV addresses these issues through a partnership between the U.S. Geological Survey and the AmericaView Consortium, which is a 501c3 non-profit comprised of university-led, state-based consortia. AmericaView is the federal government's partner in achieving the program vision and goals, which focus both on making data available in usable, cost-effective formats and on helping the university, secondary-education, and public sectors in each state identify, develop, and implement the kinds of remote sensing applications each state needs most. AV is developing applied remote sensing research programs in each of its thirty StateViews. Partner academic institutions are creating internships programs involving students and faculty with applications development, in cooperation with local, state, and federal government agencies. Education and training outreach programs are improving workforce preparation at K-12, post-secondary, and professional levels. Data distribution and sharing infrastructure that leverages funding and avoids duplication is enabling practical archive expansion and distribution systems. http://www.americaview.org/
B13D-1519
Analyzing Whitebark Pine Distribution in the Northern Rocky Mountains in Support of Grizzly Bear Recovery
Whitebark pine seeds have long been identified as the most significant vegetative food source for grizzly bears in the Greater Yellowstone Ecosystem (GYE) and, hence, a crucial element of suitable grizzly bear habitat. The overall health and status of whitebark pine in the GYE is currently threatened by mountain pine beetle infestations and the spread of whitepine blister rust. Whitebark pine distribution (presence/absence) was mapped for the GYE using Landsat 7 Enhanced Thematic Mapper (ETM+) imagery and topographic data as part of a long-term inter-agency monitoring program. Logistic regression was compared with classification tree analysis (CTA) with and without boosting. Overall comparative classification accuracies for the central portion of the GYE covering three ETM+ images along a single path ranged from 91.6% using logistic regression to 95.8% with See5's CTA algorithm with the maximum 99 boosts. The analysis is being extended to the entire northern Rocky Mountain Ecosystem and extended over decadal time scales. The analysis is being extended to the entire northern Rocky Mountain Ecosystem and extended over decadal time scales.
B13D-1520
Remote sensing of heat fluxes using SEBAL: Comparison between Landsat and MODIS
Instantaneous heat fluxes were estimated using data obtained from Landsat 5 TM (Thematic Mapper), Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) using Surface Energy Balance Algorithm for Land (SEBAL) model for cloud-free days. The modeled results were compared with measurements of net radiation (both incoming and outgoing short and longwave), soil, sensible and latent heat fluxes by two flux towers located in Brookings, SD and Fort Peck, MT. Flux tower data were 30 minutes averages at every half an hour and the contributing area of the air within the period was estimated for each satellite pass by taking into accounts the factors of observation height, atmospheric stability, and surface roughness as well as wind speed and directions (Hsieh et al. 2000). We found that footprints (considering 90% contributing areas) were normally larger than the size of one Landsat pixel (30 m) but smaller than that of one MODIS pixel (1 km). Therefore for Landsat the data were average for pixels within the concurrent footprint and for MODIS the data for the particular pixel covering the flux tower is used. The correlation coefficients between the modeled and the observed net radiation values for Landsat and MODIS were found to be 0.70 and 0.66 respectively. Relatively, comparisons were better at Brookings than at Fort Peck site for both sensors. This could be because the former site has a relatively flat topography and larger fetch than the latter, minimizing the possible effects of terrain heterogeneity on incoming and outgoing solar radiation modeling. Poor correlation was found for soil heat flux between satellites estimate and in-situ observations. In addition, the correlation coefficient for sensible heat flux was found to be 0.62 for Landsat. However, for MODIS, the correlation was only 0.11. On the other hand, the comparisons for latent heat flux showed improvement with correlation coefficients being 0.62 and 0.37 for Landsat and MODIS respectively. In SEBAL, cold pixels are used to estimate air temperature, which is used in computation for both net radiation and sensible heat flux. The uncertainties associated with this assumption cancelled out somehow in deriving latent heat flux. SEBAL performed better in modeling the heat fluxes with Landsat data. It is probably due to the scaling issue in comparison as the footprint areas of the flux towers have always been significantly less than a single MODIS pixel. By simulating MODIS observation using Landsat, we found the correlation coefficients for the aggregated Landsat pixels decreased from 0.62 to 0.25 with an increase of RMSE from 50.5 to 68.3 Wm-2. This suggested that poor performance of MODIS estimate of heat fluxes as compared to the flux tower measurements is due to heterogeneity of the surface within the field of view of MODIS sensor.
B13D-1521
Integrating NASA Satellite-Derived Precipitation and Soil Moisture Data Into the Digital-NGP Decision Support System for Agriculture
The usefulness of NASA satellite-derived data for agricultural decision support systems (DSS) depends on the specific applications and their spatial and temporal resolution requirements. For globally oriented DSS, such as the U.S. Department of Agriculture's Crop Explorer, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has demonstrated the operational usefulness of NASA precipitation data, by providing seamless, dynamic, context-sensitive Web services via the Agricultural Online Visualization and Analysis System (AOVAS). The latter is a component of the GES DISC's Agricultural Information System (AIS), which enables the remote, interoperable, operational access to distributed data (e.g., near-real-time satellite- derived rainfall), by using the GrADS-Data Server (GDS) and the Open Geospatial Consortium (OGC)-compliant MapServer. The latter allows the access of AIS data from any OGC-compliant client, such as the Earth-Sun System Gateway (ESG) or Google Earth. AOVAS is one of a family of "Giovanni" (GES-DISC Interactive Online Visualization ANd aNalysis Infrastructure) instances, which enable users to perform interactive visualization and analysis online without downloading any data. For more regionally or locally oriented DSS, such as the Digital- NGP (Northern Great Plains), an online GIS database system for archiving and distributing remote sensing images developed by the Upper Midwest Aerospace Consortium, the usefulness of NASA data is less clear. For agricultural users from the regional down to the local (including precision farming) levels, answers to two key questions are needed: when and how. The "how" is addressed with spatial distribution (e.g., an image), particularly at the sub-field resolution. An example is the new capability of the Digital-NGP to deliver maps of management zones, using remote sensing images and field data provided by users. "When" is basically a time series question, the answer to which is primarily determined by weather and climate. Phenology of local crops will likely shift in response to global and regional climate changes, therefore it is important to track temporal variations of temperature and moisture for timely decision making. The objective of this study is to determine the extent to which NASA data and services can be usefully integrated into the Digital-NGP, i.e., can integration help to better answer the two key questions. The objective will be approached by availing the Digital-NGP of existing capabilities of Giovanni-AOVAS and AIS, as well as the capabilities of a new Giovanni-Soil Moisture instance.
B13D-1522
He Sapa Bloketu Woecun: Remote Sensing at the Heart of Everything That Is
We are living in a definite time of change. Distinct changes are being experienced in our most sacred and natural environments. This is especially true on Native Tribal lands. Native people have lived for millennia in distinct and unique ways. The knowledge of balancing the needs of people with the needs of our natural environments is paramount in all tribal societies. This inherent knowledge has become the foundation on which to build a "woven" contemporary understanding of western science and geospatial technologies. The Upper Midwest Aerospace Consortium (UMAC) and the Native Tribes of South Dakota and Northern California have recognized the critical need in developing an understanding of and proficiency in the application of geospatial technologies (remote sensing). The presentation will highlight: 1) the current use of remote sensing in Native Tribal communities of the Great Plains and Northern California; 2) successful partnerships between Native communities, Tribal Colleges, public Universities and private organizations; 3) how to address balancing the needs of the people with the needs of the environment (Climate Change); and 4) the critical need for developing strong Native Leaders, experienced in remote sensing, to lead Tribal, local, state and national communities through the challenges presented by Climate Change. Humboldt State University (HSU) and the Upper Midwest Aerospace Consortium (UMAC) have been recognized nationally for their STEM partnerships with Native Tribal communities. Unique collaborations are emerging "bridging" Native Tribal people across geographic areas in developing remote sensing programs/projects which integrate the distinctive Traditional Ecological Knowledge (TEK) of Native Tribal people. Focus will be placed on the currently funded UMAC & NSF OEDG Project "He Sapa Bloketu Woecun" and the application of geospatial technologies (remote sensing) in the sacred sites of the Black Hills as well as Northern California.
B13D-1523
Promoting Sustainable Agricultural Practices Through Remote Sensing Education and Outreach
Ever increasing demand for food and fiber calls for farm management strategies such as effective use of chemicals and efficient water use that will maximize productivity while reducing adverse impacts on the environment. Remotely sensed data collected by satellites are a valuable resource for farmers and ranchers for gaining insights about farm and ranch productivity. While researchers in universities and agencies have made tremendous advances, technology transfer to end-users has lagged, preventing the farmers from taking advantage of this valuable resource. To overcome this barrier, the Upper Midwest Aerospace Consortium (UMAC), a NASA funded program headed by the University of North Dakota, has been working with end-users to promote the use of remote sensing technology for sustainable agricultural practices. We will highlight the UMAC activities in Wyoming aimed at promoting this technology to sugar-beet farmers in the Big Horn Basin. To assist farmers who might not have a computer at home, we provide them to local county Cooperative Extension Offices pre-loaded with relevant imagery. Our targeted outreach activities have resulted in farmers requesting and using new and old Landsat images to identify growth anomalies and trends which have enabled them to develop management zones within their croplands.
B13D-1524
Detecting Weed Infestations in Soybean Using Remote Sensing.
Can weed distribution maps be developed from remote sensed reflectance data? When are the appropriate times to collect these data during the season? What wavebands can be used to distinguish weedy from weed- free areas? This research examined if and when reflectance could be used to distinguish between weed-free and weed-infested (mixed species) areas in soybean and to determine the most useful wavebands to separate crop, weed, and soil reflectance differences. Treatments in the two-year study included no vegetation (bare soil), weed-free soybean, and weed-infested soybean and, in one year, 80% corn residue cover. Reflectance was measured at several sampling times from May through September in 2001 and 2002 using a hand-held multispectral radiometer equipped with band-limited optical interference filters (460 – 1650 nm). Pixel resolution was 0.8-m. Reflectance in the visible spectral range (460 to 700 nm) generally was similar among treatments. In the near-infrared (NIR) range (>700 to 1650 nm), differences among treatments were observed from soybean growth stage V-3 (about 4 weeks after planting) until mid-July to early August depending on crop vigor and canopy closure (76 cm row spacing in 2001 and 19 cm row spacing in 2002). Reflectance rankings in the NIR range when treatments could be differentiated were consistent between years and, from lowest to highest reflectance, were soil < weed-free < weed-infested areas. Increased reflectance from weed-infested areas was most likely due to increased biomass and canopy cover. Residue masked differences between weed-free and weed- infested areas during the early stages of growth due to high reflectance from the residue and reduced weed numbers in these areas. These results suggest that NIR spectral reflectance collected prior to canopy closure can be used to distinguish weed-infested from weed-free areas.
B13D-1525
Towards an Operational Vegetation Health Monitoring System for the Northern Great Plains
Farmers and Rangers in the Northern Great Plains (NGP) of the United States had been devastated by the extremely dry weather conditions in the summer of 2006. The entire state of North Dakota was declared a primary agricultural disaster area in September, 2006 by the US Department of Agriculture. Emergency grazing on CRP lands was extended in several NGP states. On the contrary, the summer of 2005 had been exceptionally wet in certain parts of NGP which ruined crops. The occurrences of these weather extremes severely affect the natural resource based enterprises like farming and ranching, the effects of which ripple through the economies of several states in the region. In order to monitor and assess the impacts of these extreme events and to take mitigation strategies, variety of physical and environmental conditions have to be taken into consideration. Remote sensing based vegetation indices such as normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), climate information and drought indices are employed to assess the growth of vegetation and its vigor, both spatially and temporally. A 25-year history of NDVI and seven-year history of EVI data were used to develop a near real-time vegetation growth monitoring system for the five Northern Great Plains states (ID, MT, ND, SD and WY). The EVI and EVI anomaly, computed based on 2000-2005 averages, are updated twice monthly for the growing season, April through September. In addition, precipitation anomalies based on a 30-year average and Palmer Z-index (a short term moisture availability index) are also updated monthly for the 45 climate divisions within the NGP states. The presentation will highlight how the near real-time monitoring of the combination of vegetation and climate parameters can help to identify the temporal and spatial patterns of vegetation dynamics at different spatial (from individual farms, climate divisions to states and, even, the whole NGP region) and temporal (monthly to growing season) scales. This information can be used by several stakeholders in the region including individual land managers, farmers and ranchers. We will also discuss how this information is disseminated through the Upper Midwest Aerospace Consortium's "learning community" approach. In addition, how additional information like growing degree days can be incorporated in order to provide a better decision support tool to the end users in the region will also be discussed. http://www.umac.org/drought/
B13D-1526
County-Level Crop Yield Prediction Using Remote Sensing Data
Early estimates of crop yield, particularly at a fine scale, can inform precision agriculture efforts. The USDA National Agricultural Statistics Service (NASS) currently provides estimates of yield on a monthly basis for each state. These estimates are based on phone interviews with farmers and in-situ examination of randomly selected plots. We seek to provide predictions at a much higher spatial resolution, on a more frequent basis, using remote sensing observations. We use publicly available data from the MODIS (Moderate Resolution Imaging Spectroradiometer) instruments on the Aqua and Terra spacecraft. These observations have a spatial resolution of 250 m and consist of two spectral bands (red and infra-red) with a repeat period of 8 days. As part of the HARVIST (Heterogeneous Agricultural Research Via Interactive, Scalable Technology) project, we have created statistical crop yield models using historical MODIS data combined with the per-county yield reported by the USDA at the end of the growing season. In our approach, we analyze 100 randomly selected historical pixels from each county to generate a yield prediction for the county as a whole. We construct a time series for each pixel that consists of its NDVI (Normalized Difference Vegetation Index) value observed during each 8-day time period to date. We then cluster all pixels together to identify groups of distinct elements (different crops, bodies of water, urban areas, desert, etc.) and create a regression model for each one. For each crop of interest, the model that best predicts that crop's historical yield is selected. These models can then be applied to data from subsequent years to generate predictions for the future. We applied this approach to data from California and Kansas for corn and wheat. We found that, in general, the yield prediction error decreased as the harvest time approached. In California, distinctly different models were selected to predict corn and wheat, permitting specialization for each crop type. The best models from 2001 predicted yield for 2002 with a 10% (corn) and 23% (wheat) relative error three months before harvest. In Kansas, the 2001 models for corn and wheat were not well distinguished, providing good predictions for wheat (19% error three months before harvest) but poor predictions for corn (55% error three months before harvest). In post-analysis, we found that the 2001 pixel NDVI time series for Kansas are much more homogeneous than those for California, making it difficult to select crop-specific models. We are currently working on incorporating historical data from additional years, which will provide more diversity and potentially better predictions. We are also in the process of applying this technique to additional crops.
B13D-1527
Remote Sensing Approach for Documenting the Conversion of Mangroves to Aquaculture
The loss of mangrove forests to aquaculture, particularly shrimp farming, in coastal Thailand presents serious environmental and societal problems. Shrimp farming is one of the fastest growing aquaculture sectors in many parts of the world, as well as one of the most controversial. In spite of considerable work put into understanding the impacts of shrimp aquaculture, few studies provide detailed assessment of the issue through time. This research compares three change detection techniques (Object-based; Change Vector Analysis (CVA); and Integrated GIS and Remote Sensing) in order to assess the mangrove conversion caused by aquaculture development in Krabi Province, Thailand between 1989, 2001 and 2007 using Landsat TM data. All three methods provide valuable information though each has its own merits. Preliminary results show 40% loss of mangroves between 1989 and 2007, 25% of which is to aquaculture development, 10% to urban, and 5% to agricultural land. This study will help establish a methodology that will aid coastal communities in Southeast Asia in determining sustainable land use management approaches.
B13D-1528
A new flexible airborne multispectral sensor for agriculture and forestry applications
Fragmentation and heterogeneity are typical features of European agricultural landscape, and precision farming applications in this context require high spatial and temporal resolution monitoring devices. Recent studies have demonstrated that vegetation indices utilizing narrow and close to each other wavelengths (like PRI or red edge indices), that are not generally available on satellite sensors, are particularly promising. The high cost associated with the use and the data processing of airborne hyperspectral sensors limits their operational utilization. DISAFRI of Tuscia University and IBIMET CNR, in collaboration with Terrasystem srl, have developed in the last years a suite of sensors that can be installed on small and flexible aerial platforms, allowing airborne remote sensing to be used as an effective operational tool in new environmental monitoring applications. Such sensors include a digital CCD multispectral camera (ASPIS) capable of acquiring images in 4 spectral user selectable bands. This instrument has been used for agricultural applications, that will be illustrated: the estimate of protein content of durum wheat during the pre-harvesting phase and the mapping of chestnut phytopaties in central Italy. The advantages and the limits of this kind of sensors and applications, based on such practical experiences, will be finally highlighted.
B13D-1529
Measuring spectral effects of calcium fertilization in the red spruce foliage
Acidic precipitation has altered biogeochemical cycles in the forests of the Northeastern U.S., and has lead to an interest in the decline symptomology of tree species affected as a result of these changes. For instance, in red spruce (Picea rubens Sarg.) stands, leaching losses of calcium (Ca) may hamper root uptake capacities, wood structural properties, and tolerance of low temperature. The Hubbard Brook Experimental Forest (HBEF) is currently the site of a long-term Ca investigation, where an entire watershed was fertilized with wollastonite (CaSiO3) at the rate of 0.12 kg ha-1 in 1999. Preliminary data confirm that Ca-treated spruce foliage is higher in total foliar Ca as compared to foliage from trees in a reference watershed. Total foliar Ca concentration, as well as that of a bound Ca-oxalate pool, increase with needle age class. In order to test the utility of hyperspectral instruments for differentiating conifer stands of varying Ca availability, we used a Visible/Infrared Intelligent Spectrometer to measure reflectance spectra of fresh red spruce needles from trees at both Ca-amended and reference sites. Needles from Ca-amended sites were characterized by higher percent reflectance of incident radiation. Differences in spectral indices of needle health were apparent mostly in mixed-needle-year boughs (MNY), as opposed to current-year (CY), or third-year (3Y) needle classes. The Ca-amended spectra of MNY boughs had an average green peak of 7.32 ± 0.29 percent, while reference samples had a green peak of 6.37 ± 0.20 percent. The Red-edge Inflection Point (REIP) of MNY boughs was lower in Ca-amended than in reference treatments, occurring at 725.7 ± 0.7 nm and 727.3 ± 0.6 nm, respectively. The ratio of simulated Landsat band measurements (TM 5/4) of Ca-treated MNY needles was 0.440 ± 0.007, while that of reference was 0.421 ± 0.008.
B13D-1530
Enhanced monitoring of the temporal and spatial relationships between water demand and water availability
Better information on evapotranspiration (ET) is essential to better understanding of consumptive use of water by crops. RTi is using NASA Earth-sun System research results and METRIC (Mapping ET at high Resolution with Internalized Calibration) to increase the repeatability and accuracy of consumptive use estimates. METRIC, an image-processing model for calculating ET as a residual of the surface energy balance, utilizes the thermal band on various satellite remote sensors. Calculating actual ET from satellites can avoid many of the assumptions driving other methods of calculating ET over a large area. Because it is physically based and does not rely on explicit knowledge of crop type in the field, a large potential source of error should be eliminated. This paper assesses sources of error in current operational estimates of ET for an area of the South Platte irrigated lands of Colorado, and benchmarks potential improvements in the accuracy of ET estimates gained using METRIC, as well as the processing efficiency of consumptive use demand for large irrigated lands. Examples highlighting how better water planning decisions and water management can be achieved via enhanced monitoring of the temporal and spatial relationships between water demand and water availability are provided.
B13D-1531
Climatological to Near Real Time Global Meteorological Data for Agricultural, Range, and Forestry Applications
Application of Decision Support Systems (DSS) software often requires accurate environmental data on time scales ranging from daily forecasts to long-range climate outlooks. The NASA Science Mission Directorate's Applied Science Energy Management Program provides estimates of many of the required meteorological and solar parameters from a combination of assimilation models and satellite observations. However these data holdings are often in large archives and/or in formats unfamiliar to many potential users. NASA, through its Applications Program, has recognized that many potential data users are either unwilling or lack the resources required to investigate the applicability of these data to their particular application. NASA's Prediction of Worldwide Energy Resource (POWER) is one of NASA's Applications Project that has as one of its objectives the development of user-friendly data products for agricultural applications and to make these products readily accessible to the user community. The POWER project has adapted and reformatted data parameters from NASA Science Directorate sponsored research programs such as the International Satellite Cloud Climatology Project (ISCCP), the Surface Radiation Budget Project (SRB), the Global Precipitation Climatology Project (GPCP), the Tropical Rain Measuring Mission (TRMM) and the meteorological assimilation projects from the Global Modeling and Assimilation Office (GMAO). The POWER project currently provides a database of meteorological parameters and surface solar energy fluxes on a global 1-degree latitude/longitude grid. The agricultural data products currently available through a prototype web based information interface (http://power.larc.nasa.gov), consist of daily integrated surface solar radiation, daily averaged dew point temperature, daily maximum and minimum temperatures, and daily precipitation. The solar data has been inferred from satellite observations that cover the time period from July 1, 1983 through December 31, 2004 (soon to be extended to June 30, 2005) and from July 1, 2006 through current time with a one-month delay; the temperature and dew point parameters are from the Goddard Earth Observing System (GEOS) version 4 analyses, and cover the time period from January 1, 1983 through current time with a one- month delay; and the daily averaged precipitation is based upon a merge of the GPCP and TRMM data files and cover the time period from January 1, 1997 through current time with a two-month delay. Results of validation studies for each of the parameters contained in the POWER web site will be presented that illustrate the strengths and weakness of the satellite/model meteorological and solar parameters. In particular, the POWER parameters have been compared to observations from a range of ground stations with particular emphases on results from automated weather stations that are often found in the US in agricultural, range, and forestry environments. http://power.larc.nasa.gov
B13D-1532
Developing Cropped Area Estimates for Niger From Multi-sensor Satellite Imagery
Estimates of cropped area in developing countries can be critical in determining allocation of food aid. However, these countries frequently lack the resources or infrastructure to perform adequate national assessments. In these instances the use of remote sensing can provide estimates over wide areas which may be difficult to survey in person. This study uses manual interpretation of multi-resolution satellite imagery as the primary inputs to creating a national estimate of cropped area in Niger. A nationwide set of regular grid points at a 2-km interval covering most potential crop-growing areas of Niger provides comprehensive analysis of cropped area. A secondary set of samples consists of points on a regular grid at a 500-m interval for select regions. Spatially comprehensive samples are interpreted using Landsat ETM data from the Landsat7 satellite. Imagery covering late-season and post-harvest periods were selected to maximize contrast between crop and non-crop areas, as well as increase the likelihood of obtaining cloud-free imagery. Nearly 150,000 points were classified using the LCmapper tool developed at EROS Data Center. Secondary sampling units are designed to relate primary samples to actual ground cover. These samples are interpreted using 1m Quickbird or IKONOS satellite imagery and serve as the groundtruth for this study, relating the actual cropped area over small areas. The images were selected to provide a representative sample of the landscape. The goal of the secondary samples is to establish a bias correction. Because the bias may be dependent on farming practices, crop type, crop phenology or many other characteristics it is necessary to select regions which will have a consistent bias. Relating the crop percentage found using the moderate-resolution Landsat data to the percentage using the high-resolution interpretations is the critical piece of this research. This study uses FEWS NET livelihood zones, which are consistent with climatologic gradients, but also incorporate sociological components. Combining this information with physical parameters such as slope and elevation it is possible to unbias the estimates based on Landsat interpretations. National estimates based on the satellite estimates confirm existing estimates of cropped area. Uncertainty in the national estimate is conveyed by the standard error of the modeling phase of this research. Distributing the cropped area according to the remotely sensed data highlights areas with large potential production.
B13D-1533
MODIS NDVI-Based Crop Production Estimates for Zimbabwe in 2006/07
Traditionally, crop production is estimated as the product of yield and cropped area. Satellite sensors tend to convolve these two sources of 'greeness', rendering independent assessments of yield and cropped area difficult. In this study, we derive an analogous metric of crop productivity based on time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. MODIS NDVI data 'cubes' are created and a special temporal filter is used to screen for cloud contamination. Regional NDVI time-series are then composited for cultivated areas, and adjusted in time according to the timing of the onset of rains. This adjustment standardizes the NDVI response vis-a-vis the standard phenological response of maize. A national time-series index is then created by taking the cropped area weighted average of the regional series. This spatio-temporal compositing allows for the identification of NDVI-green up during grain filling in crop growing areas. Cross-validation tests based on Zimbabwe data reveal that this metric is highly correlated with US Department of Agriculture production figures (R2=0.91), possesses desirable linear characteristics, and performs much better than more common indices such as maximum seasonal NDVI (R2=0.52) or seasonally integrated NDVI (R2=0.52). Thus, appropriate agro-phenological filtering of NDVI can improve the utility and accuracy of space-based agricultural monitoring, which in turn can provide an early proxy for national crop production.