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19
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
Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions, Remote Sens
- Environ
"... Abstract Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though synthetic aperture radar (SAR) s ..."
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Cited by 7 (1 self)
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Abstract Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though synthetic aperture radar (SAR) systems have the advantages of cloud penetration, all-weather coverage, high spatial resolution, day/night acquisitions, and signal independence of the solar illumination angle. In this study, we obtained coincident optical and SAR images of an agricultural area to investigate the use of SAR imagery for farm management. The optical and SAR data were normalized to indices ranging from 0 to 1 based on the meteorological conditions and sun/sensor geometry for each date to allow temporal analysis. Using optical images to interpret the response of SAR backscatter (s o ) to soil and plant conditions, we found that SAR s o was sensitive to variations in field tillage, surface soil moisture, vegetation density, and plant litter. In an investigation of the relation between SAR s o and soil surface roughness, the optical data were used for two purposes: (1) to filter the SAR images to eliminate fields with substantial vegetation cover and/or high surface soil moisture conditions, and (2) to evaluate the results of the investigation. For dry, bare soil fields, there was a significant correlation (r 2 =.67) between normalized SAR s o and near-infrared (NIR) reflectance, due to the sensitivity of both measurements to surface roughness. Recognizing the limitations of optical remote sensing data due to cloud interference and atmospheric attenuation, the findings of this study encourage further studies of SAR imagery for crop and soil assessment. D
Harvist: A system for agricultural and weather studies using advanced statistical models
- In Proceedings of the Earth-Sun Systems Technology Conference
, 2005
"... Abstract — Remote sensing instruments in Earth orbit provide a rich source of information about current agricultural conditions. Observed over time, patterns emerge that can assist in the prediction of future conditions, such as the yield expected for a given crop at the end of the growing season. I ..."
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Cited by 4 (1 self)
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Abstract — Remote sensing instruments in Earth orbit provide a rich source of information about current agricultural conditions. Observed over time, patterns emerge that can assist in the prediction of future conditions, such as the yield expected for a given crop at the end of the growing season. It is suspected that these predictions can be made more accurate by incorporting other sources of information, such as weather conditions from ground stations, soil properties, etc. The tools required to access and combine large amounts of data from multiple sources, at different spatial resolutions, are not readily available. The HARVIST (Hetereogeneous Agricultural Research Via Interactive, Scalable Technology) project seeks to address this lack by demonstrating the technology required to perform largescale studies of the interactions between agriculture and climate. Previously, we have developed successful software tools for multispectral pixel classification using support vector machines, and multispectral image pixel clustering using constrained kmeans, which we are leveraging in this effort. To date, we have developed a graphical interface that allows users to interactively run automatic classification and clustering algorithms on multispectral remote-sensing data. We have incorporated technical advances that exploit the spatial nature of the data to greatly increase classification efficiency. Our next goal is to incorporate a predictive component to support applications such as crop yield prediction. I.
Comparison of Earth Observing-1 ALI and Landsat ETM+ for crop identification and yield prediction in Mexico
- IEEE Transactions on Geoncience and Remote Sensing
, 2003
"... Abstract—This paper presents a comparison of Earth Ob- ..."
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Cited by 4 (0 self)
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Abstract—This paper presents a comparison of Earth Ob-
Optical remote sensing requirements for operational crop monitoring and yield forecasting in Europe
- In Proceedings of Sentinel-3 OLCI/SLSTR and MERIS/(A) ATSR Workshop, ESA SP-711
, 2012
"... The European Commission requires in-season crop yield forecasts at a European level as part of the decision making process on market intervention and for policy support. For the past twenty years, the Monitoring Agricultural Resources (MARS) Unit of the European Commission Joint Research Centre (JRC ..."
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The European Commission requires in-season crop yield forecasts at a European level as part of the decision making process on market intervention and for policy support. For the past twenty years, the Monitoring Agricultural Resources (MARS) Unit of the European Commission Joint Research Centre (JRC) has operationally produced such forecasts using the MARS Crop Yield Forecasting System (MCYFS), a modelling infrastructure driven by agro-meteorological data and assisted by remotely sensed observations. The potential of quantitative assessments of crop canopy status by remote sensing is currently underexploited in MCYFS because the available data do not satisfy the requirements for crop specific monitoring and yield forecasting. After presenting the current MCYFS, this paper discusses these ideal data requirements with the objective to see how the forthcoming Sentinel3-OLCI data could satisfy them. 1.
ESTIMATION OF STRATUM VARIANCES IN PLANNING OF CROP ACREAGE SURVEYS
, 1986
"... Abstract: A modelling approach is used to obtain initial estimates of stratum crop acreage variances for designing crop surveys, particularly those using the remote sensing technology. The present methodology is developed based on the concept of stratum variance as a function of the sampling unit s ..."
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Abstract: A modelling approach is used to obtain initial estimates of stratum crop acreage variances for designing crop surveys, particularly those using the remote sensing technology. The present methodology is developed based on the concept of stratum variance as a function of the sampling unit size and it uses primarily the historical crop statistics which are commonly available in most countries. Methods are proposed for the determination of stratum variances corresponding to unit sizes different from the sampling unit size and for which the historical crop statistics can be used. The methodology is applied to estimate stratum variances for wheat in the U.S. Great Plains. An evaluation of these estimates made by comparing them to those obtained from certain satellite sample data shows that the proposed method leads to reliable stratum variance estimates for a fairly large size (5 x 6 nautical miles area segment) sampling unit.
Project
, 2010
"... Abstract: In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. Timely information on global crop production is indispensable for combating the growing stress on the world’s crop production and for securing both short-term and lo ..."
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Abstract: In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. Timely information on global crop production is indispensable for combating the growing stress on the world’s crop production and for securing both short-term and long-term stable and reliable supply of food. Global agriculture monitoring systems are critical to providing this kind of intelligence and global earth observations are an essential component of an effective globalRemote Sensing 2010, 2 1590 agricultural monitoring system as they offer timely, objective, global information on
www.mdpi.com/journal/remotesensing Article Spectral Reflectance of Wheat Residue during Decomposition and Remotely Sensed Estimates of Residue Cover
, 2010
"... Abstract: Remotely sensed estimates of crop residue cover (fR) are required to assess the extent of conservation tillage over large areas; the impact of decay processes on estimates of residue cover is unknown. Changes in wheat straw composition and spectral reflectance were measured during the deca ..."
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Abstract: Remotely sensed estimates of crop residue cover (fR) are required to assess the extent of conservation tillage over large areas; the impact of decay processes on estimates of residue cover is unknown. Changes in wheat straw composition and spectral reflectance were measured during the decay process and their impact on estimates of fR were assessed. Proportions of cellulose and hemicellulose declined, while lignin increased. Spectral features associated with cellulose diminished during decomposition. Narrow-band spectral residue indices robustly estimated fR, while broad-band indices were inconsistent. Advanced multi-spectral sensors or hyperspectral sensors are required to assess fR reliably over diverse agricultural landscapes.
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"... ii Acknowledgments I would first of all like to thank the members of my dissertation committee, Ranga Myneni, Yuri Knyazikhin, Mark Friedl, Curtis Woodcock, and Alexander Marshak, for their guidance and support during the past several years. I appreciate the help and generous contributions of my com ..."
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ii Acknowledgments I would first of all like to thank the members of my dissertation committee, Ranga Myneni, Yuri Knyazikhin, Mark Friedl, Curtis Woodcock, and Alexander Marshak, for their guidance and support during the past several years. I appreciate the help and generous contributions of my committee members. My heartfelt thanks are due to my advisors, Dr. Myneni and Dr. Knyazikhin, for their advice, academic encouragement and trust in my research ability during my years at Boston University. Their insight to key research questions inspired me to dig deeper and deeper. Countless discussions with them about research and life had significant impact on my views about science and life. I thank them for their patience, support, and mentoring. I am grateful to Curtis Woodcock, who shared his knowledge of remote sensing, geostatistics and validation. I thank him for providing tremendous guidance and forthright comments on my research work. Many thanks to Jan Bogaert, whose knowledge of spatial pattern metrics provided important insights on spatial processes and enriched this dissertation. Special thanks to Jeff Privette and Jeff Morisette for providing
Abstract Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland
, 2004
"... Finland, during a field campaign in June 14–21, 2000, was used to validate Moderate Resolution Imaging Spectroradiometer (MODIS) LAI algorithm. The field LAI data was first related to 30-m resolution Enhanced Thermal Mapper Plus (ETM+) images using empirical methods to create a high-resolution LAI m ..."
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Finland, during a field campaign in June 14–21, 2000, was used to validate Moderate Resolution Imaging Spectroradiometer (MODIS) LAI algorithm. The field LAI data was first related to 30-m resolution Enhanced Thermal Mapper Plus (ETM+) images using empirical methods to create a high-resolution LAI map. The analysis of empirical approaches indicates that preliminary segmentation of the image followed by empirical modeling with the resulting patches, was an effective approach to developing an LAI validation surface. Comparison of the aggregated high-resolution LAI map and corresponding MODIS LAI retrievals suggests satisfactory behavior of the MODIS LAI algorithm although variation in MODIS LAI product is higher than expected. The MODIS algorithm, adjusted to high resolution, generally overestimates the LAI due to the influence of the understory vegetation. This indicates the need for improvements in the algorithm. An improved correlation between field measurements and the reduced simple ratio (RSR) suggests that the shortwave infrared (SWIR) band may provide valuable information for needle-leaf forests.
