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NASA NTRS2016

Multi-Frequency Investigation into Scattering from Vegetation over the Growth Cycle

In this investigation, we aim to collect and use time-series multi-frequency microwave data over winter wheat during entire growth cycle to characterize vegetation dynamics and to quantify its effects on soil moisture retrievals. We plan to incorporate C-band radar and VHF receiver within the existing L-band radarradiometer system called ComRAD (SMAPs ground based simulator). With C-bands ability to sense vegetation details and VHFs root-zone soil moisture within ComRADs footprint, we will be able to test our discrete scatterer vegetation models and parameters at various surface conditions. The purpose of this study is to determine optical depth and effective scattering albedo of vegetation of a given type (i.e. winter wheat) at various stages of growth that are need to refine soil moisture retrieval algorithms being developed for the SMAP mission.

Lang, R. H.↗

NASA NTRS2013

Daily MODIS 500 m Reflectance Anisotropy Direct Broadcast (DB) Products for Monitoring Vegetation Phenology Dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.

Vegetation↗

NASA NTRS1979

LACIE area, yield, and production estimate characteristics: U.S.S.R.

No estimates were generated for the U.S.S.R. during LACIE phase 1. Phase 2 effort was limited to two indicator regions: winter wheat areas where 385 segments were allocated, and spring wheat areas with 362 allocated segments. The level of activity for phase 3 was extended to the entire country which automatically increased the segment workload from 747 to 1947 segments. Production, area, and yield estimates, and their accuracy are discussed for phases 2 and 3 with emphasis on scope, sampling strategy, data base, LANDSAT data, yield analysis for winter and spring wheat, area and production analysis for winter and spring wheat, and technical issues and problems.

Hickman, J. R.↗

NASA NTRS1978

Large Area Crop Inventory Experiment (LACIE). LACIE third interim phase 3 accuracy assessment report

The author has identified the following significant results. An accuracy of 90/85 was achieved with the October estimates which had a relative bias of -9.9 percent and a coefficient of variation of 5.2 percent for the total wheat production in the USGP. The probability was 0.9 that the LACIE estimate was within + or - 15 percent of true wheat production for the USGP. The LACIE spring wheat production underestimates in August, September, and October were the results of area underestimates for spring wheat in the USNGP region. The winter wheat blind study showed that the average proportion estimates were significantly different from the average dot-count, ground truth proportions at the USSGP and USGP-7 levels.

Source record↗

NASA NTRS1977

Wheat yield forecasts using LANDSAT data

Several considerations of winter wheat yield prediction using LANDSAT data were discussed. In addition, a simple technique which permits direct early season forecasts of wheat production was described.

Colwell, J. E.↗

NASA NTRS1979

Large Area Crop Inventory Experiment (LACIE). An overview of the Large Area Crop Inventory Experiment and the outlook for a satellite crop inventory

The author has identified the following significant results. The most important LACIE finding was that the technology worked very well in estimating wheat production in important geographic locations. Based on working through the many successes and shortcomings of LACIE, it can be stated with confidence that: (1) the current technology can successfully monitor what production in regions having similar characteristics to those of the U.S.S.R. wheat areas and the U.S. hard red winter wheat areas; (2) with additional applied research, significant improvements in capabilities to monitor wheat in these and other important production regions can be expected in the near future; (3) the remote sensing and weather effects modeling technology approached used by LACIE is generally applicable to other major crops and crop-producing regions of the world; and (4) with suitable effort, this technology can now advance rapidly and could be widespread use in the late 1980's.

Erb, R. B.↗

NASA NTRS1980

Identification of USSR Indicator Regions

Potential indicator regions were determined by comparing the statistics for barley and wheat at the lowest administrative levels for which published statistics were available. Fourteen were selected for review based on their relative abundances of wheat and barely. These potential indicator regions were grouped according to three conditions that could affect labeling and classification accuracies: (1) high-barley content; (2) presence of barley and spring wheat; and (3) presence of barley and winter wheat. Each region was further evaluated based on the availability of crop calendars, LANDSAT acquisitions, and ancillary data. Based on the relative abundance of wheat and barley and the availability of data, three indicator regions were recommended. Within each region, individual oblasts and/or krays were selected according to segment availability and segment acquisition histories for potential barley separation.

Disler, J.↗

NASA NTRS2021

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↗

NASA NTRS2017

Integrating Water Supply Constraints into Irrigated Agricultural Simulations of California

Simulations of irrigated croplands generally lack key interactions between water demand from plants and water supply from irrigation systems. We coupled the Water Evaluation and Planning system (WEAP) and Decision Support System for Agrotechnology Transfer (DSSAT) to link regional water supplies and management with field-level water demand and crop growth. WEAP-DSSAT was deployed and evaluated over Yolo County in California for corn, rice, and wheat. WEAP-DSSAT is able to reproduce the results of DSSAT under well-watered conditions and reasonably simulate observed mean yields, but has difficulty capturing yield interannual variability. Constraining irrigation supply to surface water alone reduces yields for all three crops during the 1987-1992 drought. Corn yields are reduced proportionally with water allocation, rice yield reductions are more binary based on sufficient water for flooding, and wheat yields are least sensitive to irrigation constraints as winter wheat is grown during the wet season.

Agriculture; Irrigation; Water Resources Managemen↗

NASA NTRS2016

Spatial Sampling of Weather Data for Regional Crop Yield Simulations

Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50, 100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed.

upscaling↗

NASA NTRS1977

Applications of Suits spectral model to wheat

Canopy reflectance calculations for a spring type Mexican wheat, Penjamo, are compared with published data on Scout winter wheat. Good agreement exists between model calculations and experimental data in the spectral range, 500 nm to 750 nm, suggesting that the model parameters for wheat can be applied to different cultivars of wheat in the same growth stage. Wheat canopy reflectance is dependent upon surface soil type and this dependency is examined with the Suits' spectral model. In this particular growth stage wheat reflectance is shown to be nearly independent of soil reflectance in the visible wavelengths and progressively dependent at longer wavelengths in the infrared.

Chance, J. E.↗

NASA NTRS1984

A laboratory investigation of a physical mechanism for the extended infrared absorption ('red shift') in wheat

Laboratory spectral measurements, on the components of both greenhouse and field grown winter wheat, were performed to identify the component and its appropriate response which gave rise to the extended infrared absorption or 'red shift' reported by Collins. Results of this study indicated that inherent intraplant adaxial (upper) leaf reflectances were of sufficient variability to suggest that an admixture of mechanisms may have utility on identifying the booting and head emergence stages in the life cycle of wheat. The physical mechanism for the shift was found to be relatively independent of the inherent variability in leaf spectra, and to be dependent upon the difference in the mode of deposition of cuticle upon the abaxial (lower) surface relative to that of the adaxial (upper) surface, the position of the flag leaf, and thus the surface exposed to the incident light during heading and after emergence of the head.

Schutt, J. B.↗

NASA NTRS1982

Selection of USSR foreign similarity regions

The similarity regions in the United States and Canada were selected to parallel the conditions that affect labeling and classification accuracies in the U.S.S.R. indicator regions. In addition to climate, a significant condition that affects labeling and classification accuracies in the U.S.S.R. is the proportion of barley and wheat grown in a given region (based on sown areas). The following regions in the United States and Canada were determined to be similar to the U.S.S.R. indicator regions: (1) Montana agrophysical unit (APU) 104 corresponds to the Belorussia high barley region; (2) North Dakota and Minnesota APU 20 and secondary region southern Manitoba and Saskatchewan correspond to the Ural RSFSR barley and spring wheat region; (3) Montana APU 23 corresponds to he North Caucasus barley and winter wheat region. Selection criteria included climates, crop type, crop distribution, growth cycles, field sizes, and field shapes.

Disler, J. M.↗

NASA NTRS1977

Suits reflectance models for wheat and cotton - Theoretical and experimental tests

Plant canopy reflectance models developed by Suits are tested for cotton and Penjamo winter wheat. Properties of the models are discussed, and the concept of model depth is developed. The models' predicted exchange symmetry for specular irradiance with respect to sun polar angle and observer polar angle agreed with field data for cotton and wheat. Model calculations and experimental data for wheat reflectance vs sun angle disagreed. Specular reflectance from 0.50 to 1.10 micron shows fair agreement between the model and wheat measurements. An Appendix includes the physical and optical parameters for wheat necessary to apply Suits' models.

Chance, J. E.↗

NASA NTRS1986

Testing and validating the CERES-wheat (Crop Estimation through Resource and Environment Synthesis-wheat) model in diverse environments

CERES-Wheat is a computer simulation model of the growth, development, and yield of spring and winter wheat. It was designed to be used in any location throughout the world where wheat can be grown. The model is written in Fortran 77, operates on a daily time stop, and runs on a range of computer systems from microcomputers to mainframes. Two versions of the model were developed: one, CERES-Wheat, assumes nitrogen to be nonlimiting; in the other, CERES-Wheat-N, the effects of nitrogen deficiency are simulated. The report provides the comparisons of simulations and measurements of about 350 wheat data sets collected from throughout the world.

Otter-Nacke, S.↗

NASA NTRS1977

Investigations of spectral separability of small grains, early season wheat detection, and multicrop inventory planning

The author has identified the following significant results. LANDSAT data from seven 5 by 6 segments having crop type information were analyzed to determine the potential for spectral separation of spring wheat from other small grains as an alternative to the primary LACIE procedure for estimating spring wheat acreage. Within segment field-center, classification accuracies for spring wheat vs. barley tended to be best in mid-July when crop color changes were in progress. When correlations were made for differences in atmospheric haze, data from several segments could be aggregated, and results that approached within segment accuracies were obtained for selected dates. LACIE field measurement spectral reflectance data provided information on both wheat development patterns and the importance of various agronomic factors on wheat reflectance, the most important being availability of soil moisture. To investigate early season detection for winter wheat, reflectance of developing wheat patterns was simulated through reflectance modeling and was analyzed along with field measured reflectance from a Kansas site. The green component development of the wheat field was analyzed as a function of data throughout the season. A selected threshold was not crossed by all fields until mid-April. These reflectance data were shown to be consistent actual LANDSAT data.

Nalepka, R. F.↗

NASA NTRS1980

LACIE - An application of meteorology for United States and foreign wheat assessment

This paper describes the overall Large Area Crop Inventory Experiment technical approach utilizing the global weather-reporting network and the Landsat satellite to make a quasi-operational application of existing research results, and the accomplishments of this cooperative experiment in utilizing the weather information. Global weather data were utilized in preparing timely yield estimates for selected areas of the U.S. Great Plains, the U.S.S.R. and Canada. Additionally, wheat yield models were developed and pilot tested for Brazil, Australia, India and Argentina. The results of the work show that heading dates for wheat in North America can be predicted with an average absolute error of about 5 days for winter wheat and 4 days for spring wheat. Independent tests of wheat yield models over a 10-year period for the U.S. Great Plains produced a root-mean-square error of 1.12 quintals per hectare (q/ha) while similar tests in the U.S.S.R. produced an error of 1.31 q/ha. Research designed to improve the initial capability is described as is the rationale for further evolution of a capability to monitor global climate and assess its impact on world food supplies.

Hill, J. D.↗

NASA NTRS1977

Wheat productivity estimates using LANDSAT data

The author has identified the following significant results. An initial demonstration was made of the capability to make direct production forecasts for winter wheat using early season LANDSAT data. The approach offers the potential to make production forecasts quickly and simply, possibly avoiding some of the complexities of alternate procedures.

Nalepka, R. F.↗