Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program

Boryan, Claire G.; Yang, Zhengwei; Mueller, Rick; Craig, Mike

doi:10.1080/10106049.2011.562309

Cited by 899 publications

(573 citation statements)

References 7 publications

Supporting

Mentioning

503

Contrasting

Unclassified

Order By: Relevance

“…The NASS CDL produces crop-specific land use maps for each calendar year at a spatial resolution of 30 or 56 m for the United States based on the classification of multi-sensor satellite imagery with training data from extensive ground surveys. The reported producer's and user's accuracies are 97.1% and 98.6% for corn, and 96.4% and 97.4% for soybean, respectively (Boryan et al,2011). Fine-resolution CDL maps are mosaicked, re-projected, and scaled up to coarser resolution as percentage maps in the MODIS sinusoidal projection.…”

Section: Regional Datamentioning

confidence: 99%

Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States

Xin

Broich

Suyker

et al. 2015

Agricultural and Forest Meteorology

View full text Add to dashboard Cite

Section: Regional Datamentioning

confidence: 99%

Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States

Xin

Broich

Suyker

et al. 2015

Agricultural and Forest Meteorology

View full text Add to dashboard Cite

“…Knowing the crop present on each agricultural field is a very valuable information at a range of scales. At the local and regional scales this information is a basic requirement to forecast yields and manage crop production [1], but also to design agricultural policies and manage subsidies (e.g., European Common Agricultural Policy (CAP) subsidies) [2]. At the continental and global scales this information is key to ensure food security, but can also impact the market prices of major staple crops, and even affect forecasts on climate dynamics and water and carbon balances [3].…”

Section: Introductionmentioning

confidence: 99%

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

Larrañaga

Álvarez‐Mozos

2016

Remote Sensing

View full text Add to dashboard Cite

Polarimetric SAR images are a rich data source for crop mapping. However, quad-pol sensors have some limitations due to their complexity, increased data rate, and reduced coverage and revisit time. The main objective of this study was to evaluate the added value of quad-pol data in a multi-temporal crop classification framework based on SAR imagery. With this aim, three RADARSAT-2 scenes were acquired between May and June 2010. Once we analyzed the separability and the descriptive analysis of the features, an object-based supervised classification was performed using the Random Forests classification algorithm. Classification results obtained with dual-pol (VV-VH) data as input were compared to those using quad-pol data in different polarization bases (linear H-V, circular, and linear 45˝), and also to configurations where several polarimetric features (Pauli and Cloude-Pottier decomposition features and co-pol coherence and phase difference) were added. Dual-pol data obtained satisfactory results, equal to those obtained with quad-pol data (in H-V basis) in terms of overall accuracy (0.79) and Kappa values (0.69). Quad-pol data in circular and linear 45˝bases resulted in lower accuracies. The inclusion of polarimetric features, particularly co-pol coherence and phase difference, resulted in enhanced classification accuracies with an overall accuracy of 0.86 and Kappa of 0.79 in the best case, when all the polarimetric features were added. Improvements were also observed in the identification of some particular crops, but major crops like cereals, rapeseed, and sunflower already achieved a satisfactory accuracy with the VV-VH dual-pol configuration and obtained only minor improvements. Therefore, it can be concluded that C-band VV-VH dual-pol data is almost ready to be used operationally for crop mapping as long as at least three acquisitions in dates reflecting key growth stages representing typical phenology differences of the present crops are available. In the near future, issues regarding the classification of crops with small field sizes and heterogeneous cover (i.e., fallow and grasslands) need to be tackled to make this application fully operational.

show abstract

“…In the US, cropland ecosystems occupy about one fifth of the total land area (Boryan et al 2011). Maize, soybean, and wheat are the main crops, each occupying over 20 % of the total harvested cropland area (Lokupitiya et al 2012) of the country.…”

mentioning

confidence: 99%

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

et al. 2016

View full text Add to dashboard Cite

Croplands are highly productive ecosystems that contribute to land-atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization Author's personal copy for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO 2 seasonal uptake over agricultural regions.Keywords Carbon and energy fluxes Á Cropland ecosystems Á Land-atmosphere exchange Á Modeldata comparison Á Cropland carbon and energy exchange

show abstract

Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program

Cited by 899 publications

References 7 publications

Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States

Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

Contact Info

Product

Resources

About