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34
Landsat-8: Science and product vision for terrestrial global change research
- Remote Sens. Environ. 2014
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Upscaling Flux Observations from Local to Continental Scales Using Thermal Remote Sensing
- AGRONOMY JOURNAL
, 2007
"... A number of recent intensive and extended field campaigns have been devoted to the collection of land-surface fluxes from a variety of platforms, with the purpose of inferring the long-term C, water, and energy budgets across large areas (watershed, continental, or global scales). One approach to fl ..."
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Cited by 13 (5 self)
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A number of recent intensive and extended field campaigns have been devoted to the collection of land-surface fluxes from a variety of platforms, with the purpose of inferring the long-term C, water, and energy budgets across large areas (watershed, continental, or global scales). One approach to flux upscaling is to use land–atmosphere transfer schemes (LATS) linked to remotely sensed boundary conditions as an intermediary between the sensor footprint and regional scales. In this capacity, we examined the utility of a multiscale LATS framework that uses thermal, visible and near infrared remote sensing imagery from multiple satellites to partition surface temperature and fluxes between the soil and canopy. We conducted exercises using tower and aircraft flux data collected at three experiment sites in Oklahoma and Iowa, each with a different configuration of instrumentation. Combined, the two flux-monitoring systems were found to be complementary: the towers provided high-spatial-resolution, timecontinuous validation at discrete points within the modeling domain, while with the aircraft data it could be confirmed that the model was reproducing broad spatial patterns observed at specific moments in time. High-resolution flux maps created with the LATS allowed evaluation of differences in footprint associated with turbulent, radiative, and conductive flux sensors, which may be contributing to energy budget closure problems observed with eddy correlation systems. The ability to map fluxes at multiple resolutions (1 m–10 km) with a common model framework is beneficial in providing spatial context to an experiment by bracketing the scale of interest. Multiscale flux maps can also assist in the experimental design stage, in a priori assessments of sensor representativeness in complex landscapes.
Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection
, 2013
"... Abstract: Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generall ..."
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Abstract: Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring theRemote Sens. 2013, 5 1705
Statistical estimation of daily maximum and minimum air temperatures from MODIS LST data over the state of Mississippi. GIScience and Remote
- Sensing
, 2006
"... Abstract: Recent studies have shown that the Land Surface Temperature (LST) data measured by Moderate Resolution Imaging Spectro-Radiometer (MODIS) from both the Terra and Aqua platforms can be successfully used for linear regression estimates of daily maximum and minimum air temperatures at a local ..."
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Cited by 8 (0 self)
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Abstract: Recent studies have shown that the Land Surface Temperature (LST) data measured by Moderate Resolution Imaging Spectro-Radiometer (MODIS) from both the Terra and Aqua platforms can be successfully used for linear regression estimates of daily maximum and minimum air temperatures at a local scale. Incorpo-ration of these estimates into spatial interpolation schemes results in accuracy improvement of the surface air temperature, provided that the correlation coefficient (R) between the air temperature and LST is rather high. The purpose of this work was to examine the importance of pixel resolution (1.0 and 5.0 km2), satellite over-pass time, season, land cover type, and the vegetation fraction (depending on the view zenith angle of the MODIS instrument) in controlling the observed level of R. The relative contribution of these factors in producing R variations has been assessed over the state of Mississippi during 2000–2004. Similarly, the sensitivity analysis of the difference between daily maximum and minimum air temperatures and LST to the same factors was performed. Results from these analyses have shown that R and the average difference between temperatures exhibited rather consistent variations
Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques
- Photogramm. Engi. Remote Sens. 2007
"... Accurate, objective, reliable, and timely predictions of crop yield over large areas are critical to helping ensure the adequacy of a nation’s food supply and aiding policy makers on import/export plans and prices. Development of objective mathematical models of crop yield prediction using remote se ..."
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Cited by 7 (1 self)
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Accurate, objective, reliable, and timely predictions of crop yield over large areas are critical to helping ensure the adequacy of a nation’s food supply and aiding policy makers on import/export plans and prices. Development of objective mathematical models of crop yield prediction using remote sensing is highly desirable. In this study, we develop a new methodology using an artificial neural network (ANN) to estimate and predict corn and soybean yields on a county-by-county basis, in the “corn belt ” area in the Midwestern and Great Plains regions of the United States. The historical yield data and long time-series NDVI derived from AVHRR and MODIS are used to develop the models. A new procedure is developed to train the ANN model using the SCE-UA optimization algorithm. The performance of ANN models is compared with multivariate linear regression (MLR) models and validation is made on the model’s stability and forecasting ability. The new algorithms can effectively train ANN models, and the prediction accuracy can be as high as 85 percent.
Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale
, 2013
"... Abstract: The long term archiving of both Landsat imagery and wheat yield mapping datasets sensed by precision agriculture technology has the potential through the development of statistical relationships to predict high resolution estimates of wheat yield over large areas for multiple seasons. Quan ..."
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Abstract: The long term archiving of both Landsat imagery and wheat yield mapping datasets sensed by precision agriculture technology has the potential through the development of statistical relationships to predict high resolution estimates of wheat yield over large areas for multiple seasons. Quantifying past yield performance over different growing seasons can inform agricultural management decisions ranging from fertilizer applications at the sub-paddock scale to changes in land use at a landscape scale. However, an understanding of the magnitude of prediction errors is needed. In this study, we examine the predictive wheat yield relationships developed from Normalised Difference Vegetation Index (NDVI) acquired Landsat imagery and combine-mounted yield monitors for three Western Australian farms over different growing seasons. We further analysed their predictive capability when these relationships are used to extrapolate yield from one farm to another. Over all seasons, the best predictions were achieved with imagery acquired in September. Of the five seasons reviewed, three showed very reasonable prediction accuracies, with the low and high rainfall years providing good predictions. Medium rainfall years showed the greatest variation in prediction accuracy with marginal to poor
A new concept for simulation of vegetated land surface dynamics - Part 1: The event driven phenology model
- Biogosciences
, 2012
"... www.biogeosciences.net/9/141/2012/ ..."
Applications of microwave remote sensing of soil moisture for agricultural applications
- Int. J. Terraspace Sci. Eng. 2009
"... Agricultural irrigation is the largest (80%) user of freshwater resources. With increasing freshwater demand, it is important to make optimal use of water resources with improved agricultural productivity through objective and accurate information provided by remote sensing. This paper reviews the p ..."
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Agricultural irrigation is the largest (80%) user of freshwater resources. With increasing freshwater demand, it is important to make optimal use of water resources with improved agricultural productivity through objective and accurate information provided by remote sensing. This paper reviews the potential of applications of microwave remote sensing of soil moisture and vegetation for agricultural application. Microwave remote sensing can be used to estimate soil moisture on the basis of large contrast that exists between the dielectric constant values for dry and wet soils. Temporal monitoring of water availability at soil root zone during growth periods of crop could prevent water stress and improve the productivity. At field scales, the high resolution soil moisture data can be better used for irrigation scheduling through precision agriculture. At larger scales, low resolution soil moisture data as alternative to vegetation index can be used to monitor and predict crop yield. Because microwaves penetrate cloud, microwave remote sensing could be a good alternative to VIS/IR hyperspectral data for monitoring vegetation distribution, health and water needs for agricultural applications.
Soil Moisture Retrieval From AMSR-E Data in Xinjiang (China): Models and Validation
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
"... Abstract—Accurate soil moisture information is required for studying the global water and energy cycles as well as the carbon cycle. The AMSR-E sensor onboard NASA’s Aqua satellite offers a new means to accurately retrieve soil moisture information at a regional and global scale. However, the charac ..."
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Abstract—Accurate soil moisture information is required for studying the global water and energy cycles as well as the carbon cycle. The AMSR-E sensor onboard NASA’s Aqua satellite offers a new means to accurately retrieve soil moisture information at a regional and global scale. However, the characterization of the factors such as precipitation, vegetation, cloud, ground roughness, and ice-snow packs is sensitive to the retrieval of the soil moisture content from the remotely sensed data. This paper examines the models that are used to generate soil moisture products from US National Snow and Ice Data Center (NSIDC), and to adapt the models to improve the accuracy of soil moisture retrieval in Xinjiang, northwest China. The ground truth data collected by the WET and WatchDog instruments in Xinjiang were used to derive the empirical parameters for the regressive model that are suited to the conditions in Xinjiang. To improve the accuracy of inversion, the impact of precipitation’s lag-effect on the surface soil moisture has been addressed using the param-eters monthly bases, daily variation and the lag-effect impact of precipitation in the improved model. The improved model is then used to retrieve the soil moisture information from the AMSR-E data. A comparative study between the result from the proposed model and the NSIDC products of May to September 2009 were performed with the AMSR-E data. Validation with ground truth and the comparison indicate that the improved model performs better and produces more accurate soil moisture maps than the NSIDC products in the study area. Index Terms—AMSR-E, arid area, inversion, precipitation, soil moisture. I.
ISPRS Archives XXXVI-8/W48 Workshop proceedings: Remote sensing support to crop yield forecast and area estimates GENERALIZED SOFTWARE TOOLS FOR CROP AREA ESTIMATES AND YIELD FORECAST
"... The procedure that leads to the estimates of the variables of interest, such as land use and crop yield and their sampling standard deviations, is rather tedious and complex, till to make necessary for a statistician to have a stable and generalized computational systems available. The SAS is often ..."
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The procedure that leads to the estimates of the variables of interest, such as land use and crop yield and their sampling standard deviations, is rather tedious and complex, till to make necessary for a statistician to have a stable and generalized computational systems available. The SAS is often the ideal instrument to face with these needs, because it permits the handling of data effectively and provides all the necessary functions to manage easily surveys with thousands of micro-data. This paper focus on the use of this system in different steps of the survey: sample design, data editing and estimation. The information produced is, however, available for one user only, the manager of the survey. Our idea is to reduce the time needed to process the collected data and to reduce the inefficiencies which unfortunately characterize the management and dissemination of aggregated information, without loosing the stability of the SAS and at the same time increasing the informative content of the survey. For this reason we developed a user oriented Visual Basic (VB) software, whose aim is the production of reports based on the data coming from the SAS central system, capable of meeting the user needs of private and public agencies involved in this sector. If SAS is excellent for large scale data processing and has a very rich function set, VB 2005 helped us develop a high quality graphical interface that make possible to personalize estimations. Particularly, in this type of survey, we may be asked to provide estimates not only at the national level, but also at provincial and local levels, in other terms we may be interested at the estimation for domains: VB software support the definition of a priori domains. We feel that through the use of this two integrated systems we could help researchers and institutions working on remote sensing in
