Browsing by Author "Kimball, John S."

Now showing 1 - 5 of 5

Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations
(2006-07) Heinsch, Faith A.; Zhao, Maosheng; Running, Steven W.; Kimball, John S.; Nemani, Ramakrishna R.; Davis, Kenneth J.; Cook, Bruce D.; Desai, Ankur R.; Ricciuto, Daniel M.; Law, Beverly E.; Oechel, Walter C.; Kwon, Hyojung; Wofsy, Steven C.; Dunn, Allison L.; Munger, J. William; Baldocchi, Dennis D.; Xu, Liukang; Hollinger, David Y.; Richardson, Andrew D.; Stoy, Paul C.; Siqueira, Mario B. S.; Monson, Russell K.; Burns, Sean P.; Flanagan, Lawrence B.; Bolstad, Paul V.; Luo, Hongyan
The Moderate Resolution Spectroradiometer (MODIS) sensor has provided near real-time estimates of gross primary production (GPP) since March 2000. We compare four years (2000 to 2003) of satellite-based calculations of GPP with tower eddy CO2 flux-based estimates across diverse land cover types and climate regimes. We examine the potential error contributions from meteorology, leaf area index (LAI)/fPAR, and land cover. The error between annual GPP computed from NASA's Data Assimilation Office's (DAO) and tower-based meteorology is 28%, indicating that NASA's DAO global meteorology plays an important role in the accuracy of the GPP algorithm. Approximately 62% of MOD15-based estimates of LAI were within the estimates based on field optical measurements, although remaining values overestimated site values. Land cover presented the fewest errors, with most errors within the forest classes, reducing potential error. Tower-based and MODIS estimates of annual GPP compare favorably for most biomes, although MODIS GPP overestimates tower-based calculations by 20%-30%. Seasonally, summer estimates of MODIS GPP are closest to tower data, and spring estimates are the worst, most likely the result of the relatively rapid onset of leaf-out. The results of this study indicate, however, that the current MODIS GPP algorithm shows reasonable spatial patterns and temporal variability across a diverse range of biomes and climate regimes. So, while continued efforts are needed to isolate particular problems in specific biomes, we are optimistic about the general quality of these data, and continuation of the MOD17 GPP product will likely provide a key component of global terrestrial ecosystem analysis, providing continuous weekly measurements of global vegetation production
A hydro-economic analysis of end-of-century climate projections on agricultural land and water use, production, and revenues in the U.S. Northern Rockies and Great Plains
(Elsevier BV, 2022-08) Lauffenburger, Zachary H.; Maneta, Marco P.; Cobourn, Kelly M.; Jencso, Kelsey; Chaffin, Brian; Crockett, Anna; Maxwell, Bruce; Kimball, John S.
Study region,Montana, U.S.A. Study focus Creating adaptation plans for projected imbalances in the western U.S. agricultural water demand-supply system are difficult given uncertainty in climate projections. It is critical to understand the uncertainties and vulnerabilities of the regional agricultural system and hydrologic impacts of climate change adaptation. We applied a stochastic, integrated hydro-economic model that simulates land and water allocations to analyse Montana farmer adaptations to a range of projected climate conditions and the response of the hydrologic system to those adaptations. Satellite observations of crop types, productivity, water use, and land allocation were used for model calibration. A suite of climate models was employed to quantify end-of-century impacts on streamflows, water and land use, production, and net revenues.New hydrological insights for the region Simulations showed summer streamflows were influenced by a state-wide 18.2% increase in agricultural water use. Decreased summer water availability with increased demand could have far reaching impacts downstream. Land use for irrigated crops increased 1.6%, while rainfed crops decreased 6.5%, implying state-level decrease in planted area. Even with increased land and water use for irrigated crops, production decreased 0.5%, while rainfed production decreased 2.7%. Corresponding losses in net revenues totaled 1.5% and 7.2% for irrigated and rainfed crops, respectively.Results highlight vulnerabilities of semi-arid agricultural regions and can aid water managers in sustaining agriculture in these regions.
Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
(2018-03) He, Mingzhu; Kimball, John S.; Maneta, Marco P.; Maxwell, Bruce D.; Moreno, Alvaro; Begueria, Santiago; Wu, Xiaocui
Accurate crop yield assessments using satellite remote sensing-based methods are of interest for regional monitoring and the design of policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations is generally too coarse to capture cropland heterogeneity. The fusion of data from different sensors can provide enhanced information and overcome many of the limitations of individual sensors. In thitables study, we estimate annual crop yields for seven important crop types across Montana in the continental USA from 2008-2015, including alfalfa, barley, maize, peas, durum wheat, spring wheat and winter wheat. We used a satellite data-driven light use efficiency (LUE) model to estimate gross primary productivity (GPP) over croplands at 30-m spatial resolution and eight-day time steps using a fused NDVI dataset constructed by blending Landsat (5 or 7) and Terra MODIS reflectance data. The fused 30-m NDVI record showed good consistency with the original Landsat and MODIS data, but provides better spatiotemporal delineations of cropland vegetation growth. Crop yields were estimated at 30-m resolution as the product of estimated GPP accumulated over the growing season and a crop-specific harvest index (HIGPP). The resulting GPP estimates capture characteristic cropland productivity patterns and seasonal variations, while the estimated annual crop production results correspond favorably with reported county-level crop production data (r = 0.96, relative RMSE = 37.0%, p < 0.05) from the U.S. Department of Agriculture (USDA). The performance of estimated crop yields at a finer (field) scale was generally lower, but still meaningful (r = 0.42, relative RMSE = 50.8%, p < 0.05). Our methods and results are suitable for operational applications of crop yield monitoring at regional scales, suggesting the potential of using global satellite observations to improve agricultural management, policy decisions and regional/global food security.
A satellite-driven hydro-economic model to support agricultural water resources management
(2020-12) Maneta, Marco P.; Coburn, K.; Kimball, John S.; He, Mingzhu; Silverman, N. L.; Chaffin, Brian C.; Ewing, Stephanie A.; Ji, X.; Maxwell, Bruce D.
The management of water resources among competing uses presents a complex technical and policy challenge. Integrated hydro-economic models capable of simulating the hydrologic system in irrigated and non-irrigated regions including the response of farmers to hydrologic constraints and economic and policy incentives, provide a framework to understand biophysical and socioeconomic implications of changing water availability. We present a transformative hydro-economic model of agricultural production driven by multi-sensor satellite observations, outputs from regional climate models, and socioeconomic data. Our approach overcomes the limitations of current decision support systems for agricultural water management and provides policymakers and natural resource managers with satellite data-driven, state-wide, operational models capable of anticipating how farmers allocate water, land, and other resources when confronted with new climate patterns, policy rules, or market signals. The model can also quantify how farming decisions affect agricultural water supplies. We demonstrate the model through an application in the state of Montana.
Sunlight mediated seasonality in canopy structure & photosynthetic activity of Amazonian rainforests
(2015-06) Bi, Jian; Knyazikhin, Yuri; Choi, Sungho; Park, Taejin; Barichivich, Jonathon; Ciais, Philippe; Fu, Rong; Ganguly, Sangram; Hall, Forrest; Hilker, Thomas; Huete, Alfredo; Jones, Matthew; Kimball, John S.; Lyapustin, Alexei; Mottus, Matti; Nemani, Ramakrishna R.; Piao, Shilong; Poulter, Benjamin; Saleska, Scott R.; Saatchi, Sassan S.; Xu, Liang; Zhou, Liming; Myneni, Ranga B.
Resolving the debate surrounding the nature and controls of seasonal variation in the structure and metabolism of Amazonian rainforests is critical to understanding their response to climate change. In situ studies have observed higher photosynthetic and evapotranspiration rates, increased litterfall and leaf flushing during the Sunlight-rich dry season. Satellite data also indicated higher greenness level, a proven surrogate of photosynthetic carbon fixation, and leaf area during the dry season relative to the wet season. Some recent reports suggest that rainforests display no seasonal variations and the previous results were satellite measurement artefacts. Therefore, here we re-examine several years of data from three sensors on two satellites under a range of sun positions and satellite measurement geometries and document robust evidence for a seasonal cycle in structure and greenness of wet equatorial Amazonian rainforests. This seasonal cycle is concordant with independent observations of solar radiation. We attribute alternative conclusions to an incomplete study of the seasonal cycle, i.e. the dry season only, and to prognostications based on a biased radiative transfer model. Consequently, evidence of dry season greening in geometry corrected satellite data was ignored and the absence of evidence for seasonal variation in lidar data due to noisy and saturated signals was misinterpreted as evidence of the absence of changes during the dry season. Our results, grounded in the physics of radiative transfer, buttress previous reports of dry season increases in leaf flushing, litterfall, photosynthesis and evapotranspiration in well-hydrated Amazonian rainforests.