@article{DeAlwis2007, title = {Unsupervised classification of saturated areas using a time series of remotely sensed images}, journal = {Hydrology and Earth System Sciences}, pages = {1663-1696}, volume = 4, year = 2007, url = {http://www.hydrol-earth-syst-sci-discuss.net/4/1663/2007/}, abstract = {The spatial distribution of saturated areas is an important consideration in numerous applications, such as water resource planning or sighting of management practices. However, in humid well vegetated climates where runoff is produced by saturation excess processes on hydrologically active areas (HAA) the delineation of these areas can be difficult and time consuming. Much of the non-point source pollution in these watersheds originates from these HAAs. Thus, a technique that can simply and reliably predict these areas would be a powerful tool for scientists and watershed managers tasked with implementing practices to improve water quality. Remotely sensed data is a source of spatial information and could be used to identify HAAs, should a proper technique be developed. The objective of this study is to develop a methodology to determine the spatial variability of saturated areas using a temporal sequence of remotely sensed images. The Normalized Difference Water Index (NDWI) was derived from medium resolution LANDSAT 7 ETM+ imagery collected over seven months in the Town Brook watershed in the Catskill Mountains of New York State and used to characterize the areas that were susceptible to saturation. We found that within a single landcover type, saturated areas were characterized by the soil surface water content when the vegetation was dormant and leaf water content of vegetation during the growing season. The resulting HAA map agreed well with both observed and spatially distributed computer simulated saturated areas. This methodology appears promising for delineating saturated areas in the landscape. }, biburl = {http://www.bibsonomy.org/bibtex/25e698d7a754cf6e475ad088214c251ee/jgomezdans}, keywords = {satellite drought vegetation reflectance classification moisture remotesensing} } @article{Penuelas1993, title = {The reflectance at the 950–970 nm region as an indicator of plant water status}, journal = {International Journal of Remote Sensing}, month = {July}, number = 10, pages = {1887 - 1905}, volume = 14, year = 1993, url = {http://www.informaworld.com/smpp/content~content=a778209739~db=all}, abstract = {We present new remote sensing indices of plant water status: the ratio between the reflectance at 970 nm, one of the water absorption bands, and the reflectance at a reference wavelength, 900 nm (R970/R9000; the first derivative minimum in this near-infrared region (dNIRminimum) and the wavelength where this minimum is found ( λNIRminimum). In order to evaluate them, we carried out three experiments. Daily irrigated gerbera plants were allowed to dry until almost wilting and then daily irrigation was restarted; pepper and bean plants were grown for four months submitted to two different irrigation treatments; and bean detached leaves were submitted to progressive dehydration whereas pressure-volume curves were being carried out. In gerbera plants, the trough about 950-970 nm decreased as the drought was increasing. Therefore, the R970/R900 index and the dNIRminimum closely tracked the changes in relative water content (RWC), leaf water potential, stomatal conductance and the foliage-air temperature differences. The λdNIRminimum tracked even better these changes in gerberas. However, these water status indices began to be significant when the water stress was already well developed, at RWC smaller than 85 per cent. The same happened to detached leaves of beans which did not present differences above -1·55 MPa water potential. Beans and peppers growing at soil matric potentials larger than -0·04 MPa presented higher R970/R900 values than those growing at soil matric potentials only larger than -0·01 MPa. In all the cases, the maximum response of these indices was found in the varieties or the species that lost cell wall elasticity in response to drought stress. This could indicate an important structural component in these indices changes. Relative water content itself seemed to be, however, the most important factor as shown by the highest correlation coefficients with these spectral indices. These spectral signals were more evident at canopy level than at leaf level. They seem to be useful as water status indicators at ground level, especially when there are not important changes of LAI and when plants wholly cover the soil.}, biburl = {http://www.bibsonomy.org/bibtex/28f7c8557834315ec1c473fa5bb5aa2c8/jgomezdans}, keywords = {optical reflectance evaporation canopy evapotranspiration drought moisture vegetation remotesensing} } @article{Jackson2004, title = {Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans}, author = {Thomas J. Jackson and Daoyi Chen and Michael Cosh and Fuqin Li and Martha Anderson and Charles Walthall and Paul Doriaswamy and E. Ray Hunt}, booktitle = {2002 Soil Moisture Experiment (SMEX02)}, journal = {Remote Sensing of Environment}, month = {Sep}, number = 4, pages = {475--482}, volume = 92, year = 2004, url = {http://www.sciencedirect.com/science/article/B6V6V-4B8BWHY-1/1/bcff206f3a4c9678088761e8bc7d7e98}, description = {ScienceDirect - Remote Sensing of Environment : Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans}, abstract = {Information about vegetation water content (VWC) has widespread utility in agriculture, forestry, and hydrology. It is also useful in retrieving soil moisture from microwave remote sensing observations. Providing a VWC estimate allows us to control a degree of freedom in the soil moisture retrieval process. However, these must be available in a timely fashion in order to be of value to routine applications, especially soil moisture retrieval. As part of the Soil Moisture Experiments 2002 (SMEX02), the potential of using satellite spectral reflectance measurements to map and monitor VWC for corn and soybean canopies was evaluated. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data and ground-based VWC measurements were used to establish relationships based on remotely sensed indices. The two indices studied were the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The NDVI saturated during the study period while the NDWI continued to reflect changes in VWC. NDWI was found to be superior based upon a quantitative analysis of bias and standard error. The method developed was used to map daily VWC for the watershed over the 1-month experiment period. It was also extended to a larger regional domain. In order to develop more robust and operational methods, we need to look at how we can utilize the MODIS instruments on the Terra and Aqua platforms, which can provide daily temporal coverage.}, biburl = {http://www.bibsonomy.org/bibtex/29ff1efcaa6d2e7d83c5fcb36f7cf5f39/jgomezdans}, keywords = {remotesensing ndwi vegetation satellite stress soilmoisture Soil reflectance evaporation moisture drought} } @article{DA_MunhozCalvet2008, title = {Joint assimilation of surface soil moisture and LAI observations into a land surface model}, author = {Joaquín Muñoz Sabater and Christoph Ruediger and Jean-Christophe Calvet and Noureddine Fritz and Lionel Jarlan and Yann Kerr}, journal = {Agricultural and Forest Meteorology}, pages = {--}, volume = {In Press, Corrected Proof}, year = 2008, url = {http://www.sciencedirect.com/science/article/B6V8W-4SK598N-1/1/45da7bc7dae540b3dc3b09d8a9fc5911}, description = {ScienceDirect - Agricultural and Forest Meteorology : Joint assimilation of surface soil moisture and LAI observations into a land surface model}, abstract = {Land Surface Models (LSM) offer a description of land surface processes and set the lower boundary conditions for meteorological models. In particular, the accurate description of those surface variables which display a slow response in time, like root-zone soil moisture or vegetation biomass, is of great importance. Errors in their estimation yield significant inaccuracies in the estimation of heat and water fluxes in Numerical Weather Prediction (NWP) models. In the present study, the ISBA-A-gs LSM is used decoupled from the atmosphere. In this configuration, the model is able to simulate the vegetation growth, and consequently LAI. A simplified 1D-VAR assimilation method is applied to observed surface soil moisture and LAI observations of the SMOSREX site near Toulouse, in south-western France, from 2001 to 2004. This period includes severe droughts in 2003 and 2004. The data are jointly assimilated into ISBA-A-gs in order to analyse the root-zone soil moisture and the vegetation biomass. It is shown that the 1D-VAR improves the model results. The efficiency score of the model (Nash criterion) is increased from 0.79 to 0.86 for root-zone soil moisture and from 0.17 to 0.23 for vegetation biomass.}, biburl = {http://www.bibsonomy.org/bibtex/2aca203cdcb28d5d70591e447b2f5d5b0/jgomezdans}, keywords = {Data uncertainty models Modelling SMOSREX remotesensing modeling optical moisture vegetation microwave} } @article{Dente2008, title = {Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield}, author = {Laura Dente and Giuseppe Satalino and Francesco Mattia and Michele Rinaldi}, booktitle = {Remote Sensing Data Assimilation Special Issue}, journal = {Remote Sensing of Environment}, month = {apr}, number = 4, pages = {1395--1407}, volume = 112, year = 2008, url = {http://www.sciencedirect.com/science/article/B6V6V-4PPFTF3-2/1/d3b3163d37d96e353f028e2605370379}, description = {ScienceDirect - Remote Sensing of Environment : Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield}, abstract = {This study presents a method to assimilate leaf area index retrieved from ENVISAT ASAR and MERIS data into CERES-Wheat crop growth model with the objective to improve the accuracy of the wheat yield predictions at catchment scale. The assimilation method consists in re-initialising the model with optimal input parameters allowing a better temporal agreement between the LAI simulated by the model and the LAI estimated by remote sensing data. A variational assimilation algorithm has been applied to minimise the difference between simulated and remotely-sensed LAI and to determine the optimal set of input parameters. After the re-initialisation, the wheat yield maps have been obtained and their accuracy evaluated. The method has been applied over Matera site located in Southern Italy and validated by using the dataset of an experimental campaign carried out during the 2004 wheat growing season. Results indicate that, LAI maps retrieved from MERIS and ASAR data can be effectively assimilated into CERES-Wheat model thus leading to accuracies of the yield maps ranging from 360�kg/ha to 420�kg/ha.}, biburl = {http://www.bibsonomy.org/bibtex/294d5116363dc948666ef0f63f36cf2db/jgomezdans}, keywords = {SAR Crop retrieval Data models maps MERIS LAI growth ASAR Wheat vegetation microwave crops satellite} } @article{LobellOrtizMonasterio, title = {Yield uncertainty at the field scale evaluated with multi-year satellite data}, author = {David B. Lobell and J. Ivan Ortiz-Monasterio and Walter P. Falcon}, journal = {Agricultural Systems}, month = {jan}, number = {1-3}, pages = {76--90}, volume = 92, year = 2007, url = {http://www.sciencedirect.com/science/article/B6T3W-4JRKD5T-1/1/220ea15932d7d8f536bb13dab8507379}, description = {ScienceDirect - Agricultural Systems : Yield uncertainty at the field scale evaluated with multi-year satellite data}, abstract = {The level of yield risk faced by a farmer is an important factor in the design of appropriate management and insurance strategies. The difference between field scale and regional scale yield risk, which can be significant, also represents an important measure of the factors that cause the yield gap - the difference between average and maximum yields. While field scale yield risk is difficult to assess with traditional data sources, yield maps derived from remote sensing offer promise for obtaining the necessary data in any region. We analyzed remotely sensed yield datasets for two regions in Northwest Mexico, the Yaqui and San Luis Rio Colorado Valleys, in conjunction with time series of aggregated regional yields for 1976-2002. Regional scale yield risk was roughly 8% of average yields in both regions. Field scale yield risk was determined to be 58% higher than regional scale risk in both regions. The difference between field and regional scale risk accounted for 50% of the spatial variance in yields in the Yaqui Valley, and 70% in the San Luis Rio Colorado Valley, indicating that climatic uncertainty represents an important source of the spatial yield variability. This implies that accurate seasonal climate forecasts could substantially reduce yield losses in farmers' fields. The results were shown to be fairly sensitive to assumptions about the magnitude and nature of errors in yield estimation, suggesting that improved understanding of estimation errors are needed to realize the full potential of remote sensing for yield risk analysis.}, biburl = {http://www.bibsonomy.org/bibtex/22c053f8b367e816fa79f54802c14e77b/jgomezdans}, keywords = {modeling models satellite crops uncertainty vegetation optical yield remotesensing} } @article{DeWit2007, title = {Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts}, author = {A.J.W. de Wit and C.A. van Diepen}, journal = {Agricultural and Forest Meteorology}, month = {sep}, number = {1-2}, pages = {38--56}, volume = 146, year = 2007, url = {http://www.sciencedirect.com/science/article/B6V8W-4P3TY91-1/1/11ca2cd0d675dce3cb1853586f852b17}, description = {ScienceDirect - Agricultural and Forest Meteorology : Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts}, abstract = {Uncertainty in spatial and temporal distribution of rainfall in regional crop yield simulations comprises a major fraction of the error on crop model simulation results. In this paper we used an Ensemble Kalman filter (EnKF) to assimilate coarse resolution satellite microwave sensor derived soil moisture estimates (SWI) for correcting errors in the water balance of the world food studies (WOFOST) crop model. Crop model simulations with the EnKF for winter wheat and grain maize were carried out for Spain, France, Italy and Germany for the period 1992-2000. The results were evaluated on the basis of regression with known crop yield statistics at national and regional level. Moreover, the EnKF filter innovations were analysed to see if any systematic trends could be found that could indicate deficiencies in the WOFOST water balance. Our results demonstrate that the assimilation of SWI has clearly improved the relationship with crop yield statistics for winter wheat for the majority of regions (66%) where a relationship could be established. For grain maize the improvement is less evident because improved relationships could only be found for 56% of the regions. We suspect that partial crop irrigation could explain the relatively poor results for grain maize, because irrigation is not included in the model. Analyses of the filter innovations revealed spatial and temporal patterns, while the distribution of normalised innovations is not Gaussian and has a non-zero mean indicating that the EnKF performs suboptimal. The non-zero mean is caused by differences in the mean value of the forecasted and observed soil moisture climatology, while the excessive spread in the distribution of normalised innovations indicates that the error covariances of forecasts and observations have been underestimated. These results clearly indicate that additional sources of error need to be included in the simulations and observations.}, biburl = {http://www.bibsonomy.org/bibtex/266b20e4a0f4ece31f4f0d269bcbafefc/jgomezdans}, keywords = {crops kalmanfilter assimilation} } @article{JonesLang19878, title = {Simulation of the phenology of soybeans}, author = {P. G. Jones and D. R. Lang}, journal = {Agricultural Systems}, month = {oct}, number = 4, pages = {295--311}, volume = 3, year = 1978, url = {http://www.sciencedirect.com/science/article/B6T3W-49NPS6P-1C/1/fa7c8dde7fa82cf9092d0d44f52b1f2e}, description = {ScienceDirect - Agricultural Systems : Simulation of the phenology of soybeans}, abstract = {Simulation models of three phases of soybean phenology--sowing to primary leaf, primary leaf to flower initiation and flower initiation to flowering--were generated for the soybean cultivar Lee and others. The basic experimental data were obtained from a series of glasshouse experiments under six temperature regimes in natural light. The data for leaf development rates showed a change in the response to temperature at the first trifoliate leaf stage; this was incorporated into the model. The flower initiation model included parameters for the production and decay of a theoretical flower promoter and for the rate of change of daylength. Time to flower initiation showed a complex relationship to photoperiod which could not be described or approximated to by any simple function. The effects of temperature were marked and showed a strong interaction with photoperiod. The flower development model accounted for a strong temperature response which was conditioned by a photoperiod effect. The three models were combined to form a soybean phenology model which was validated against phenological data obtained from date of sowing experiments conducted under field conditions.}, biburl = {http://www.bibsonomy.org/bibtex/223d3d9e8eae156900f9160ae09431612/jgomezdans}, keywords = {crops phenology agriculture} } @article{Davenport2003, title = {The use of earth observation techniques to improve catchment-scale pollution predictions}, author = {I. J. Davenport and M. Silgram and J. S. Robinson and A. Lamb and J. J. Settle and A. Willig}, booktitle = {Recent Development in River Basin Research and Management}, journal = {Physics and Chemistry of the Earth, Parts A/B/C}, number = {33-36}, pages = {1365--1376}, volume = 28, year = 2003, url = {http://www.sciencedirect.com/science/article/B6X1W-49XPFYX-1/1/2ff9292b097359bfd238a7ed8a943a96}, description = {ScienceDirect - Physics and Chemistry of the Earth, Parts A/B/C : The use of earth observation techniques to improve catchment-scale pollution predictions}, abstract = {Remote sensing can potentially provide information useful in improving pollution transport modelling in agricultural catchments. Realisation of this potential will depend on the availability of the raw data, development of information extraction techniques, and the impact of the assimilation of the derived information into models. High spatial resolution hyperspectral imagery of a farm near Hereford, UK is analysed. A technique is described to automatically identify the soil and vegetation endmembers within a field, enabling vegetation fractional cover estimation. The aerially-acquired laser altimetry is used to produce digital elevation models of the site. At the subfield scale the hypothesis that higher resolution topography will make a substantial difference to contaminant transport is tested using the AGricultural Non-Point Source (AGNPS) model. Slope aspect and direction information are extracted from the topography at different resolutions to study the effects on soil erosion, deposition, runoff and nutrient losses. Field-scale models are often used to model drainage water, nitrate and runoff/sediment loss, but the demanding input data requirements make scaling up to catchment level difficult. By determining the input range of spatial variables gathered from EO data, and comparing the response of models to the range of variation measured, the critical model inputs can be identified. Response surfaces to variation in these inputs constrain uncertainty in model predictions and are presented. Although optical earth observation analysis can provide fractional vegetation cover, cloud cover and semi-random weather patterns can hinder data acquisition in Northern Europe. A Spring and Autumn cloud cover analysis is carried out over seven UK sites close to agricultural districts, using historic satellite image metadata, climate modelling and historic ground weather observations. Results are assessed in terms of probability of acquisition probability and implications for future earth observation missions.}, biburl = {http://www.bibsonomy.org/bibtex/289a4c57c980c4aac90a3c99208a5f041/jgomezdans}, keywords = {vegetation pollution remotesensing nitrogen models satellite assimilation erostion crops reflectance optical} } @article{Bach:2003, title = {Methods and examples for remote sensing data assimilation in land surface process modeling}, author = {H. Bach and W. Mauser}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, pages = {1629- 1637}, volume = 41, year = 2003, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1221824}, issn = {0196-2892}, doi = {10.1109/TGRS.2003.813270}, description = {Welcome to IEEE Xplore 2.0: Methods and examples for remote sensing data assimilation in land surface process modeling}, abstract = {Land surface process models describe the energy, water, carbon, and nutrient fluxes on a local to regional scale using a set of environmental land surface parameters and variables. They need time series of spatially distributed inputs to account for the large spatial and temporal variability of land surface processes. In principle many of these inputs can be derived through remote sensing using both optical and microwave sensors. New approaches in four-dimensional data-assimilation (4DDA) form the basis to combine remote sensing data and spatially explicit land surface process models more effectively. This paper describes basic techniques for 4DDA in land surface process modeling. Two case studies were carried out to demonstrate different successful approaches of remote sensing data assimilation into land surface process models. The assimilation of surface soil moisture estimates from European Remote Sensing (ERS) synthetic aperture radar data in a flood forecasting scheme is presented, as well as the combination of a land surface process model and a radiative transfer model to improve the accuracy of land surface parameter retrieval from optical data [Landsat Thematic Mapper (TM)].}, biburl = {http://www.bibsonomy.org/bibtex/251b9d391f143522e70101f62e7c61f0f/jgomezdans}, keywords = {satellite models remotesensing crops uncertainty optical reflectance BRDF vegetation model assimilation canopy sensing} } @article{pinter_review, title = {Remote Sensing for Crop Management}, author = {Paul J. Pinter Jr. and Jerry L. Hatfield and James S. Schepers and Edward M. Barnes and M. Susan Moran and Craig S.T. Daughtry and Dan R. Upchurch}, journal = {Photogrammetric Engineering and Remote Sensing}, month = {June}, number = 6, pages = {647-664}, volume = 69, year = 2003, url = {http://www.asprs.org/publications/pers/2003journal/june/abstracts.html#647}, abstract = {Scientists with the Agricultural Research Service (ARS) and various government agencies and private institutions have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. This knowledge has facilitated the development and use of various remote sensing methods for non-destructive monitoring of plant growth and development and for the detection of many environmental stresses which limit plant productivity. Coupled with rapid advances in computing and positionlocating technologies, remote sensing from ground, air, and space-based platforms is now capable of providing detailed spatial and temporal information on plant response to their local environment that is needed for site specific agricultural management approaches. This manuscript, which emphasizes contributions by ARS researchers, reviews the biophysical basis of remote sensing; examines approaches that have been developed, refined, and tested for management of water, nutrients, and pests in agricultural crops; and assesses the role of remote sensing in yield prediction. It concludes with a discussion of challenges facing remote sensing in the future.}, biburl = {http://www.bibsonomy.org/bibtex/244578cbf68f7162d659c5c1d6be15a45/jgomezdans}, keywords = {satellite optical crops yield vegetation remotesensing} } @article{Doraiswamy2003, title = {Crop Yield Assessment from Remote Sensing}, author = {Paul C. Doraiswamy and Sophie Moulin and Paul W. Cook and Alan Stern}, journal = {Photogrammetric Engineering and Remote Sensing}, month = {June}, number = 6, pages = {665-674}, volume = 69, year = 2003, url = {http://www.asprs.org/publications/pers/2003journal/june/abstracts.html#665}, abstract = {Monitoring crop condition and production estimates at the state and county level is of great interest to the U.S. Department of Agriculture. The National Agricultural Statistical Service (NASS) of the U.S. Department of Agriculture conducts field interviews with sampled farm operators and obtains crop cuttings to make crop yield estimates at regional and state levels. NASS needs supplemental spatial data that provides timely information on crop condition and potential yields. In this research, the crop model EPIC (Erosion Productivity Impact Calculator) was adapted for simulations at regional scales. Satellite remotely sensed data provide a real-time assessment of the magnitude and variation of crop condition parameters, and this study investigates the use of these parameters as an input to a crop growth model. This investigation was conducted in the semi-arid region of North Dakota in the southeastern part of the state. The primary objective was to evaluate a method of integrating parameters retrieved from satellite imagery in a crop growth model to simulate spring wheat yields at the sub-county and county levels. The input parameters derived from remotely sensed data provided spatial integrity, as well as a real-time calibration of model simulated parameters during the season, to ensure that the modeled and observed conditions agree. A radiative transfer model, SAIL (Scattered by Arbitrary Inclined Leaves), provided the link between the satellite data and crop model. The model parameters were simulated in a geographic information system grid, which was the platform for aggregating yields at local and regional scales. A model calibration was performed to initialize the model parameters. This calibration was performed using Landsat data over three southeast counties in North Dakota. The model was then used to simulate crop yields for the state of North Dakota with inputs derived from NOAA AVHRR data. The calibration and the state level simulations are compared with spring wheat yields reported by NASS objective yield surveys.}, biburl = {http://www.bibsonomy.org/bibtex/23ca88e789ab62d92a16f25d310ed1f66/jgomezdans}, keywords = {uncertainty crops yield model optical assimilation remotesensing} } @article{Gardner1981, title = {Relationships between crop temperature, grain yield, evapotranspiration and phenological development in two hybrids of moisture stressed sorghum}, author = {B. R. Gardner and B. L. Blad and D. P. Garrity and D. G. Watts}, journal = {Irrigation Science}, month = {nov}, number = 4, pages = {213--224}, volume = 2, year = 1981, url = {http://dx.doi.org/10.1007/BF00258375}, description = {SpringerLink - Journal Article}, abstract = {Recent studies have shown that the grain yields of corn (Zea mays L.) and wheat (Triticum aestivum L.) are related to the degree of water stress they undergo. The purpose of the study reported here was to establish relationships between crop temperature and the grain yields, phenological development, evapotranspiration rates (ET) and leaf water potential (?l) of two hybrids of grain sorghum (Sorghum bicolor L. Moench) subjected to varying levels of plant water stress. The study was conducted at the University of Nebraska Sandhills Agricultural Laboratory in 1978 on a Typic Ustipsamment (Valentine fine sand) soil. The sorghum hybrids used were RS 626 and NB 505. Four irrigation treatments were applied in order to subject the crops to varying levels of water stress during each of three major growth stages. Soil moisture was monitored with a neutron probe. ET was estimated with the water balance technique. Crop temperature was measured with an IR thermometer and leaf water potential was measured with a Scholander pressure bomb. Grain yields were reduced by water stress occuring at anytime during the growing season. Yield reductions were largest when stress occurred during only the grainfill period and were least when stress occurred during the entire growing season. The percentage reduction in sorghum grain yield can be described by an index involving the seasonal accumulation of the daily mid-day temperature differences between well-watered and stressed crops (S TSD). As S TSD values increased, ET decreased. However, the correlation of ET with S TSD was relatively low (R2 = 0.60) probably due to the limited amount of data available for analysis and inaccuracies in the soil water balance method used to estimate ET. The mid-day temperature of well-watered rows ranged between 18.0 and 32.8 °C with a mid-day temperature range of about 0.5 °C between the well-watered rows in various plots for several days following an irrigation. However, in certain instances, the mid-day temperature range increased to 1–2 °C for a few days before irrigation. This suggests that certain of the rows experienced water stress and should have been irrigated earlier. Yield data support that conclusion. Range in crop temperature within a field appeared to be a sensitive indicator of crop water stress in sorghum. No significant difference in the phenological development of sorghum resulted from water stress except in one NB 505 plot in which plants were stressed throughout the entire season. In that plot, the stressed plants lagged in development behind non-stressed plants by approximately ten days. The differences in mid-day leaf water potentials (??l) and crop temperatures (?T) between stressed and non-stressed vegetation were examined. As ?T increased up to about 4 °C, ??l, also increased. Beyond that point, ??l decreased while ?T continued to increase. This behavior was attributed to stomatal closure which permitted an increase in ?l of the stressed plants (hence reducing ??l) even as ?T continued to increase. ER -}, biburl = {http://www.bibsonomy.org/bibtex/2d2be43cc461b25ee81293d856ed2d008/jgomezdans}, keywords = {crops agriculture yield phenology evapotranspiration} } @article{Allen2000, title = {Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study}, author = {R. G. Allen}, journal = {Journal of Hydrology}, month = {mar}, number = {1-2}, pages = {27--41}, volume = 229, year = 2000, url = {http://www.sciencedirect.com/science/article/B6V6C-401HGRJ-4/1/7fd84be7681bf7844e4db38b1990dbf7}, description = {ScienceDirect - Journal of Hydrology : Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study}, abstract = {Evapotranspiration (ET) calculations were made on a daily basis throughout 1988 for two locations near Menemen, Turkey. Calculations used the FAO-56 "dual" crop coefficient approach that includes separate prediction of evaporation from soil. Two days were drawn from the data set to correspond with Landsat flyovers to provide for comparison with remote sensing estimates of ET. One study site was a cotton field in a relatively flat, irrigated region. The second study site was an integrated area in the Gediz River Basin where the farm and field sizes are small, of the order of 3-5�ha, and about ten different "crops" are grown. Predicted ET (ETc act) for the cotton site was 3.1 and 5.3�mm/day for 26 June and 29 August, and was 4.9 and 4.3�mm/day for the integrated crops in the Gediz valley. Total calendar year ETc act was predicted to be 800 for cotton and 940�mm for the Gediz valley. Evaporation during the crop growing periods averaged 9% of total evapotranspiration for cotton and 14% for the mixed crops. The predictions of ETc act were within 20% of predictions by the Landsat-based SEBAL remote sensing method at only one site and date. Predictions were within 20% of ET based on an energy feedback remote-sensing application using NOAA-AVHRR and Landsat data for both sites on one of the two dates. Before comparison, the predictions of ETc act by the FAO-56 procedure were reduced by 15%, to account for less than pristine crop establishment, growth and water management in the area.}, biburl = {http://www.bibsonomy.org/bibtex/26b169ac27e601bc1ef194f75022b1424/jgomezdans}, keywords = {crop evapotranspiration vegetation evaporation} } @article{Stapper1989, title = {Assessing the productivity of wheat genotypes in a Mediterranean climate, using a crop-simulation model}, author = {M. Stapper and H. C. Harris}, journal = {Field Crops Research}, month = {mar}, number = 2, pages = {129--152}, volume = 20, year = 1989, url = {http://www.sciencedirect.com/science/article/B6T6M-4915788-2H/1/d9261bc73829abe77ec5d945e7adf4d6}, description = {ScienceDirect - Field Crops Research : Assessing the productivity of wheat genotypes in a Mediterranean climate, using a crop-simulation model}, abstract = {A locally developed and validated crop-growth model for wheat, SIMTAG, using historic daily weather data, was used to simulate wheat crops at four locations in northern Syria. The chosen sites have a Mediterranean climate with an average annual rainfall between 280 and 480 mm (cv 35-28%). The analysis examined the effects on crop productivity of manipulating crop genotype (early, medium or late-maturing) or management (sowing date, fallowing, sowing rate). Cumulative frequency distributions of grain-yield, and dates of germination, anthesis and maturity, were derived from the simulation results and were used to discriminate between alternative choices. Means of simulated crop growth, water use, rooting depth and wetting depth were used to illustrate aspects of wheat production in this highly variable climate. Sowing close to the break of season showed a definite yield advantage at all sites, with simulated yield reductions of 4.2% per 1-week delay in germination. Average grain-yields increased from 217 g m-2 ( 53%) at the driest site to 471 g m-2 ( 48%) at the wettest site. Optimum anthesis dates were identified for each location, with an average yield reduction of 3-9% per 1-week delay in anthesis. Environmental pressure on wheat production in the area is high, as is shown by average simulated water-stress-free periods finishing 3 weeks prior to the optimum anthesis date at the driest site, and around anthesis at the wettest site. It was concluded that an early-maturing cultivar was most appropriate for the driest site and a medium-late-season cultivar for the wettest site, with medium-early cultivars for the intermediate sites. The simulated output was judged to be a realistic representation of the studied crop system. The wheat model SIMTAG can therefore be recommended for use in similar climates.}, biburl = {http://www.bibsonomy.org/bibtex/2556b78323602e1c85342d4c1725f853c/jgomezdans}, keywords = {crops model yield modeling vegetation} } @article{Ferrazzoli:1997, title = {The potential of multifrequency polarimetric SAR in assessingagricultural and arboreous biomass}, author = {P. Ferrazzoli and S. Paloscia and P. Pampaloni and G. Schiavon and S. Sigismondi and D. Solimini}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, pages = {5-17}, volume = 35, year = 1997, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=551929}, issn = {0196-2892}, doi = {10.1109/36.551929}, description = {IEEEXplore# The potential of multifrequency polarimetric SAR in assessing agricultural and arboreous biomass}, abstract = {Polarimetric radar data collected by AIRSAR and SIR-C over agricultural fields, forests, and olive groves of the Italian Montespertoli site are analyzed. The objective is to investigate the radar capability in discriminating among various vegetation species and its sensitivity to agricultural and arboreous biomass. Results indicate that a combined use of P(0.45 GHz) and L- (1.2 GHz) bands allows one to discriminate between agricultural fields and other targets, while a combined use of L- and C- (5.3 GHz) bands allows the authors to discriminate within agricultural areas. To monitor biomass, P-band gives the best results for forests and olive groves, L-band appears to be good for crops with low plant density (m-2), while for crops with high plant density, both L- and C-bands are useful. The availability of crosspolarized data is important for both classification and biomass retrieval}, biburl = {http://www.bibsonomy.org/bibtex/27ec9969c1e92be1d2b9f830c12420b72/jgomezdans}, keywords = {biomass SAR vegetation satellite remotesensing microwave crops} } @article{Ferrazzoli:1999, title = {Experimental and model investigation on radar classificationcapability}, author = {P. Ferrazzoli and L. Guerriero and G. Schiavon}, journal = {IEEE Transactions on Geoscience and Remote Sensing,}, pages = {960-968}, volume = 37, year = 1999, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=752214}, issn = {0196-2892}, doi = {10.1109/36.752214}, description = {IEEEXplore# Experimental and model investigation on radar classification capability}, abstract = {The capability of multifrequency polarimetric synthetic aperture radar (SAR) to discriminate among nine vegetation classes is shown using both experimental data and model simulations. The experimental data were collected by the multifrequency polarimetric AIRSAR at the Dutch Flevoland site and the Italian Montespertoli site. Simulations are carried out using an electromagnetic model, developed at Tor Vergata University, Rome, Italy, which computes microwave vegetation scattering. The classes have been defined on the basis of geometrical differences among vegetation species, leading to different polarimetric signatures. It is demonstrated that, for each class, there are some combinations of frequencies and polarizations producing a significant separability. On the basis of this background, a simple, hierarchical parallelepiped algorithm is proposed}, biburl = {http://www.bibsonomy.org/bibtex/29de04e14a059b9256b07ca62b96fe15b/jgomezdans}, keywords = {satellite remotesensing microwave classification SAR crops} } @article{Berry2003, title = {A calibrated model of wheat lodging compared with field measurements}, author = {P. M. Berry and M. Sterling and C. J. Baker and J. Spink and D. L. Sparkes}, journal = {Agricultural and Forest Meteorology}, month = {#nov#}, number = {3-4}, pages = {167--180}, volume = 119, year = 2003, url = {http://www.sciencedirect.com/science/article/B6V8W-49CMX0K-6/1/7e30418b307b4dac058602ae4ce7786e}, description = {ScienceDirect - Agricultural and Forest Meteorology : A calibrated model of wheat lodging compared with field measurements}, abstract = {This paper describes how an existing model of lodging in winter wheat (Triticum aestivum L.) has been further developed to enable it to predict the timing and amount of lodging from inputs about the crop, soil and weather. Improvements include the use of recently specified values for the drag coefficient and damping ratio of wheat shoots, accounting for temporal and spatial non-uniformity of plant characteristics, and using daily rainfall and wind run data. Stem and root lodging are predicted when the base bending moment of the shoot(s) exceed the failure moments of the stem base and anchorage system, respectively. Tests show that the model can predict the timing and quantity of lodging in crops with a wide range of lodging risks. Each area of further development contributed to the improvement of the model. Using accurate values of drag coefficient and damping ratio reduced failure wind speeds by 44% to more realistic values. Accounting for temporal non-uniformity of plants meant that the increase in lodging risk towards harvest was correctly predicted. Accounting for spatial non-uniformity enabled different sized areas of lodging to be predicted.}, biburl = {http://www.bibsonomy.org/bibtex/202874d8b4250841533418963f9e68503/jgomezdans}, keywords = {Wheat model risk Yyeld Lodging agriculture vegetation} } @article{gent, title = {Physiological and agronomic consequences of Rht genes in wheat}, author = {M P N Gent and R. K. Kiyomoto}, journal = {Journal of crop production}, number = 1, pages = {27-46}, volume = 1, year = 1998, biburl = {http://www.bibsonomy.org/bibtex/207e005d5d2f97f0d26a16f9b4791a7db/jgomezdans}, keywords = {crops agronomic lodging agriculture} } @article{chinoy1950, title = { Effect of Vernalization and Photoperiodic Treatments on Growth and Development of Wheat}, author = {J. J. Chinoy and K. K. Nanda}, journal = {Nature}, month = {882 - 883 }, volume = 165, year = 1950, biburl = {http://www.bibsonomy.org/bibtex/24645a5a988607efbcb82c732034d8def/jgomezdans}, keywords = {phenology agriculture wheat} }