As-built design specification for the Yield Estimation Subsystem (YES) monthly yield data base and supporting programs
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Cook, D.↗
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Operation of the yield estimation subsystem
The organization and products of the yield estimation subsystem (YES) are described with particular emphasis on meteorological data acquisition, yield estimation, crop calendars, weekly weather summaries, and project reports. During the three phases of LACIE, YES demonstrated that it is possible to use the flow of global meteorological data and provide valuable information regarding global wheat production. It was able to establish a capability to collect, in a timely manner, detailed weather data from all regions of the world, and to evaluate and convert that data into information appropriate to the project's needs.
Mccrary, D. G.↗
Yield estimation of sugarcane based on agrometeorological-spectral models
This work has the objective to assess the performance of a yield estimation model for sugarcane (Succharum officinarum). The model uses orbital gathered spectral data along with yield estimated from an agrometeorological model. The test site includes the sugarcane plantations of the Barra Grande Plant located in Lencois Paulista municipality in Sao Paulo State. Production data of four crop years were analyzed. Yield data observed in the first crop year (1983/84) were regressed against spectral and agrometeorological data of that same year. This provided the model to predict the yield for the following crop year i.e., 1984/85. The model to predict the yield of subsequent years (up to 1987/88) were developed similarly, incorporating all previous years data. The yield estimations obtained from these models explained 69, 54, and 50 percent of the yield variation in the 1984/85, 1985/86, and 1986/87 crop years, respectively. The accuracy of yield estimations based on spectral data only (vegetation index model) and on agrometeorological data only (agrometeorological model) were also investigated.
Rudorff, Bernardo Friedrich Theodor↗
Determination of the optimal level for combining area and yield estimates
Several levels of obtaining both area and yield estimates of corn and soybeans in Iowa were considered: county, refined strata, refined/split strata, crop reporting district, and state. Using the CCEA model form and smoothed weather data, regression coefficients at each level were derived to compute yield and its variance. Variances were also computed with stratum level. The variance of the yield estimates was largest at the state and smallest at the county level for both crops. The refined strata had somewhat larger variances than those associated with the refined/split strata and CRD. For production estimates, the difference in standard deviations among levels was not large for corn, but for soybeans the standard deviation at the state level was more than 50% greater than for the other levels. The refined strata had the smallest standard deviations. The county level was not considered in evaluation of production estimates due to lack of county area variances.
Bauer, M. E.↗
Wheat yield estimation at the farm level using TM Landsat and agrometeorological data
A model for estimating wheat yields on the farm level was developed, that integrates the Landsat TM data and agrometeorological information. Results obtained for a test site in southern Brasil for years of 1986 and 1987 show that the vegetation index derived from Landsat TM could account for the 60 to 40 percent wheat-yield variability observed between the two crop years. Compared to results using either the Landsat TM vegetation index or the agrometeorological data alone, the joint use of both types of data in a single model yielded a significant improvement.
Rudorff, B. F. T.↗
Assimilating Remote Sensing Observations of Leaf Area Index and Soil Moisture for Wheat Yield Estimates: An Observing System Simulation Experiment
Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.
Nearing, Grey S.↗
The use of spectral data in wheat yield estimation - An assessment of techniques explored in LACIE
The object of the paper is to assess the results of the Large Area Crop Inventory Experiment (LACIE) and closely related research on yield estimation techniques based on remote sensing variables. The exploratory research conducted during LACIE substantiated the hypothesis of yield related information contained in Landsat multispectral scanner data and indicated some of its empirical characteristics. It is noted that leaf area and possibly other foliage features can be derived from spectral data for yield estimation through agrometeorological models and that multiple vegetative and grain related features may be discernable by Landsat derived wheat spectra at different points in the crop development.
Stuff, R. G.↗
The law of the minimum and an application to wheat yield estimation
The adaptation of the law of the minimum (LOM) to wheat yield estimation is discussed. It is demonstrated through a trial application that the LOM concept is a valuable tool for model building when regression tools are inadequate.
Cate, R. B.↗
Status of yield estimation technology: A review of second-generation model development and evaluation
Multiple regression models were studied in order to determine their yield estimation capability for any arbitrary unit area and to obtain greater responsiveness and accuracy through the use of additional data sources applied at smaller spatial and temporal scales. It was concluded that data base inadequacy was the factor limiting performance in the models studied and that each of the models has more yield predicting capability than was reached during LACIE.
Stuff, R. G.↗
Evaluation of a Combined Drought Indicator Against Crop Yield Estimations and Simulations Over the Argentine Humid Pampas
Droughts pose serious threats to the agricultural sector, especially in rainfed-dominated agricultural regions like those in Argentina’s Humid Pampas. This region was recently impacted by slow-evolving and long-lasting droughts as well as by flash droughts, resulting in losses reaching thousands of millions of US dollars. Improvements of drought early warning systems are essential, particularly given the projected increase in drought frequency and severity over southern South America. The spatial and temporal relationship between precipitation deficits, soil moisture and vegetation health anomalies are crucial for better understanding and representation of the agricultural droughts and their impacts. In this context, the Combined Drought Indicator (CDI) considers the causal and time-lagged relationship of these three variables. The study’s objective is twofold: (1) Analyze the time-lagged response between precipitation deficits, soil moisture and satellite fAPAR anomalies; and (2) Evaluate the CDI’s capability to characterize the severity of drought events on the Humid Pampas against agricultural yield estimations and simulations, as well as agricultural emergency declarations. The correlation among the variables shows strong spatial variability. The highest Pearson correlation values (r > 0.42) are observed over parts of the Humid Pampas for time lags of 0, 10, and 20 days between the variables. Although the CDI has limitations, such as its coarse spatial resolution and monthly temporal resolution of precipitation data, it effectively tracks the progression of major drought events in the region. The CDI’s performance aligns well with estimations and simulations of soybean and corn yields, as well as official declarations of agricultural emergencies. Insights from this study also provide a basis for discussing potential improvements to the CDI. This study highlights the global and regional significance of evaluating and enhancing the CDI for effective drought monitoring, emphasizing the role of collaborative efforts for future advancements in drought early warning systems.
Combined Drought Indicator↗
Science Yield Estimates and Sensitivities With the AstroPIC Integrated Photonic Coronagraph for the Habitable Worlds Observatory
The Habitable Worlds Observatory (HWO) flagship has a goal to survey ~100 of the nearest stellar systems and their habitable zones in order to detector and spectroscopically characterize ~25 potentially “Earth-like planets” (or “Exoearths”). The HWO telescope will feature a >6-m diameter aperture and a coronagraph that will suppress starlight by ten orders of magnitude to enable the detection and characterization of Earth analogues in the habitable zone. Three Exploratory Analytic Cases (EACs) are currently considering different aperture configurations for HWO including on-axis and off-axis apertures and both different hexagonal and keystone segmentation configurations. Photonic Integrated Circuits (PICs) offer a path to miniaturize traditional, bulk optics coronagraph functionality reducing risk margins and increasing mission science exoearth yields for the HWO mission. We have developed a simulation pipeline that enables evaluation of the AstroPIC photonic integrated coronagraph through to estimates mission yields. We present sensitivities to aberrations for different types of segmentation and low-order aberrations for HWO EACs demonstrating that AstroPIC is suitable for both on-axis and off-axis configurations. We perform a parametric sweep of the exoearth science yield sensitivities in terms of key coronagraph parameters including coronagraphic end-to-end throughput, inner working angle, robustness to aberrations, and bandwidth. We consider both a hybrid and a pure photonic architecture; a pure integrated photonic architecture provides the greatest potential in science yield improvements, although a hybrid PIC limited in its numbers of modes can provide complementary science to a traditional, bulk-optic coronagraph and increasing the estimated science yield when operated jointly.
Photonics↗
Accuracy and performance of LACIE yield estimates in major wheat producing regions of the world
The LACIE yield models developed, implemented, and tested during the three phases of the experiment represent the first generation of models designed for the large-scale prediction of wheat production. The models are capable of supporting the stated project goal of being within 10 percent of the actual wheat production 90 percent of the time. The limitations of the models are inherent in their nature. The temporal resolution limits their ability to handle the erratic weather occurring in critical situations. The relatively large spatial resolution of the individual models limits the capture of localized but important episodic events.
Phinney, D. E.↗
USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better Than Simple Trend Analyses?
Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R^(2) of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R^(2) results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.
crop yield↗
The use of large-area spectral data in wheat yield estimation
Large-area relations between satellite spectral data and end-of-season crop yield were investigated. Green Index Number (GIN) values from Landsat MSS data of sample segments throughout the U.S. Great Plains winter wheat belt in 1978 were correlated to county USDA-SRS reported yields. A linear relation between GIN and yield appeared to exist up to GIN values of 40 or 50, covering cases of severe to moderate stress. In a test on 1978 Texas winter wheat at the county level, GIN values for sample segments in the counties were used in conjunction with an agronomic-meteorological yield model. The combined fit explained significantly more of the observed yield variation at the county level than the agromet model alone.
Barnett, T. L.↗
Exoplanet Classification and Yield Estimates for Direct Imaging Missions
Future NASA concept missions that are currently under study, like the Habitable Exoplanet Imaging Mission (HabEx) and the Large Ultra-violet Optical Infra Red Surveyor (UVOIR), could discover a large diversity of exoplanets. We propose here a classification scheme that distinguishes exoplanets into different categories based on their size and incident stellar flux, for the purpose of providing the expected number of exoplanets observed (yield) with direct imaging missions. The boundaries of this classification can be computed using the known chemical behavior of gases and condensates at different pressures and temperatures in a planetary atmosphere. In this study, we initially focus on condensation curves for sphalerite ZnS, H2O, CO2, and CH4. The order in which these species condense in a planetary atmosphere define the boundaries between different classes of planets. Broadly, the planets are divided into rocky planets (0.5-1.0 solar radius), super-Earths (1.0-1.75 solar radius), sub-Neptunes (1.75-3.5 solar radius), sub-Jovians (3.5-6.0 solar radius), and Jovians (6-14.3 solar radius) based on their planet sizes, and "hot," "warm," and "cold" based on the incident stellar flux. We then calculate planet occurrence rates within these boundaries for different kinds of exoplanets, eta (sub planet) [i.e. exoplanet], using the community coordinated results of NASA's Exoplanet Program Analysis Group's Science Analysis Group-13 (SAG-13). These occurrence rate estimates are in turn used to estimate the expected exoplanet yields for direct imaging missions of different telescope diameters.
Kopparapu, Ravi Kumar↗
The Large Area Crop Inventory Experiment (LACIE). Part 2: Yield estimates from meteorological information
Crop-weather models developed for wheat from an assembled base of approximately 45 years of climatic records and historic wheat yield and production data at the stratum level are described. Utilization of meteorological data acquired by the NOAA satellites is discussed.
Waite, P. J.↗
Spring wheat-leaf phytomass and yield estimates from airborne scanner and hand-held radiometer measurements
An attempt has been made to relate hand-held radiometer measurements, and airborne multispectral scanner readings, with both different wheat stand densities and grain yield. Aircraft overflights were conducted during the tillering, stem extension and heading period stages of growth, while hand-held radiometer readings were taken throughout the growing season. The near-IR/red ratio was used in the analysis, which indicated that both the aircraft and the ground measurements made possible a differentiation and evaluation of wheat stand densities at an early enough growth stage to serve as the basis of management decisions. The aircraft data also corroborated the hand-held radiometer measurements with respect to yield prediction. Winterkill was readily evaluated.
Aase, J. K.↗
Winter wheat stand density determination and yield estimates from handheld and airborne scanners
Radiance measurements from handheld (Exotech 100-A) and air-borne (Daedalus DEI 1260) radiometers were related to wheat (Triticum aestivum L.) stand densities (simulated winter wheat winterkill) and to grain yield for a field located 11 km northwest of Sidney, Montana, on a Williams loam soil (fine-loamy, mixed Typic Argiborolls) where a semidwarf hard red spring wheat cultivar was needed to stand. Radiances were measured with the handheld radiometer on clear mornings throughout the growing season. Aircraft overflight measurements were made at the end of tillering and during the early stem extension period, and the mid-heading period. The IR/red ratio and normalized difference vegetation index were used in the analysis. The aircraft measurements corroborated the ground measurements inasmuch as wheat stand densities were detected and could be evaluated at an early enough growth stage to make management decision. The aircraft measurements also corroborated handheld measurements when related to yield prediction. The IR/red ratio, although there was some growth stage dependency, related well to yield when measured from just past tillering until about the watery-ripe stage.
Aase, J. K.↗