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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Plant Watering in Low Gravity: The Study of the Fluid Dynamic Characteristics of Lunar Regolith Simulant for the Application of Crop Production Systems Using Scaled Capillary Devices

Bioregenerative life support is key to sustaining human life on the surface of other celestial bodies for missions beyond Low Earth Orbit (LEO). One example of such life support includes crop production systems capable of producing both plants and fungi for astronaut consumption. There have been numerous efforts to design and deploy such systems; however, there have been persistent fundamental issues with the water delivery systems. This study aims to understand and model the fluid dynamics of crop production systems to inform engineers about the key parameters needed for the design of such systems. The primary objective is to validate and expand upon NASA’s previous study, the Plant Water Management (PWM) experiments, to understand the fluid phenomena present in substrate-based systems. The model will then be expanded to include parameters for changing the test material of potential growth media as well as the varying water demands of the crop throughout its different stages in its life cycle. Specifically, this study is focused on the viability of using lunar regolith for agricultural purposes. Terrestrial-based scaled capillary models will be utilized to mimic the fluid phenomena occurring due to the low gravity environment seen on the lunar surface. Ultimately, the study will model the fluid flow present in the lunar regolith simulant and inform researchers on how to incorporate the material into crop production systems on the lunar surface.

gravity↗
NASA NTRS2023

Towards Soil Moisture Profile Estimation in the Root Zone Using L- and P-Band Radiometer Observations: A Coherent Modelling Approach

Precision irrigation management and crop water stress assessment rely on accurate estimation of root zone soil moisture. However, only the top 5cm soil moisture can be estimated using the two current passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), which operate at L-band (wavelength of ~21cm). Since the contributing depth of the soil to brightness temperature increases with observation wavelength, it is expected that a P-band (wavelength of ~40cm) radiometer could potentially provide soil moisture information from deeper layers of the soil profile. Moreover, by combining both L- and P- bands, it is hypothesized that the soil moisture profile can be estimated even beyond their individual observation depths. The aim of this study was to demonstrate the potential of combined L-band and P-band radiometer observations to estimate the soil moisture profile under flat bare soil using a stratified coherent forward model. Brightness temperature observations at L-band and P-band from a tower based experimental site across a dry (April 2019) and a wet (March 2020) period, covering different soil moisture profile shapes, were used in this study. Results from an initial synthetic study showed that the performance of a combined L-band and P-band approach was better than the performance of using either band individually, with an average depth over which reliable soil moisture profile information could be estimated (i.e. with a target root mean square error (RMSE) of less than 0.04 m3/m3) being 20cm for linear and 15cm for second-order polynomial functions. Other functions were also tested but found to have a poorer performance. Applying the method to the tower-based brightness temperature achieved an average estimation depth of 28cm (20cm) and 5cm (5cm) during the dry and wet periods respectively when using a second-order polynomial (linear) function. These findings highlight the opportunity of a satellite mission with L-band and P-band observations to accurately estimate the soil moisture profile to as deep as 30cm globally.

Foad Brakhasi↗
NASA NTRS1975

Plant, soil, and shadow reflectance components of row crops

Data from the first Earth Resource Technology Satellite (LANDSAT-1) multispectral scanner (MSS) were used to develop three plant canopy models (Kubelka-Munk (K-M), regression, and combined K-M and regression models) for extracting plant, soil, and shadow reflectance components of cropped fields. The combined model gave the best correlation between MSS data and ground truth, by accounting for essentially all of the reflectance of plants, soil, and shadow between crop rows. The principles presented can be used to better forecast crop yield and to estimate acreage.

Richardson, A. J.↗
NASA NTRS2020

Integrated assessment of climate change impacts on crop productivity and income of commercial maize farms in northeast South Africa

Agriculture in South Africa sustains about 70% of the region’s population for food, income and employment, playing an important role for food security and the local economy. The focus of the study was the commercial maize farms of the Free State Province given their importance in the National economy. The Regional Integrated Assessment (phase I) was implemented to assess climate change and adaptation that links climate, crops, economic data and tools developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP). In this context, the“system”is defined as a whole of agronomic and socio-economic factors. Within that framework three core questions were being evaluated: (i) Impacts of climate change under current system; (ii) Impacts of climate change under future system; (iii) The role of adaptation under climate change and the future system. Maize production will decrease between 10% to 16% as a result of projected climate impacts. Also, current agricultural production systems are negatively affected by climate change with an increase in poverty rates between 2% to 3%. The projected adoption of the adapted technology would result in positive increased net returns and a decrease in poverty rate of between 12%and 22%. The results of this study show that implementing adaptation measures and other strategies as indicated by the local stakeholders will have positive impacts on the agricultural production systems and can contribute to support and inform climate change policy decision making such as the development of National Adaptation Plans.

Integrated assessment↗
NASA NTRS2010

Utilizing In Situ Directional Hyperspectral Measurements to Validate Bio-Indicator Simulations for a Corn Crop Canopy

Two radiative transfer canopy models, SAIL and the two-layer Markov-Chain Canopy Reflectance Model (MCRM), were coupled with in situ leaf optical properties to simulate canopy-level spectral band ratio vegetation indices with the focus on the photochemical reflectance index in a cornfield. In situ hyperspectral measurements were made at both leaf and canopy levels. Leaf optical properties were obtained from both sunlit and shaded leaves. Canopy reflectance was acquired for eight different relative azimuth angles (psi) at three different view zenith angles (Theta (sub v)), and later used to validate model outputs. Field observations of photochemical reflectance index (PRI) for sunlit leaves exhibited lower values than shaded leaves, indicating higher light stress. Canopy PRI expressed obvious sensitivity to viewing geometry, as a function of both Theta (sub v) and psi . Overall, simulations from MCRM exhibited better agreements with in situ values than SAIL. When using only sunlit leaves as input, the MCRM-simulated PRI values showed satisfactory correlation and RMSE, as compared to in situ values. However, the performance of the MCRM model was significantly improved after defining a lower canopy layer comprised of shaded leaves beneath the upper sunlit leaf layer. Four other widely used band ratio vegetation indices were also studied and compared with the PRI results. MCRM simulations were able to generate satisfactory simulations for these other four indices when using only sunlit leaves as input; but unlike PRI, adding shaded leaves did not improve the performance of MCRM. These results support the hypothesis that the PRI is sensitive to physiological dynamics while the others detect static factors related to canopy structure. Sensitivity analysis was performed on MCRM in order to better understand the effects of structure related parameters on the PRI simulations. Leaf area index (LAI) showed the most significant impact on MCRM-simulated PRI among the parameters studied. This research shows the importance of hyperspectral and narrow band sensor studies, and especially the necessity of including the green wavelengths (e.g., 531 nm) on satellites proposing to monitor carbon dynamics of terrestrial ecosystems.

Cheng, Yen-Ben↗
NASA NTRS1984

Discrete Random Media Techniques for Microwave Modeling of Vegetated Terrain

Microwave remote sensing of agricultural crops and forested regions is studied. Long term goals of the research involve modeling vegetation so that radar signatures can be used to infer the parameters which characterize the vegetation and underlying ground. Vegetation is modeled by discrete scatterers viz, leaves, stems, branches and trunks. These are replaced by glossy dielectric discs and cylinders. Rough surfaces are represented by their mean and spectral characteristics. Average scattered power is then calculated by employing discrete random media methodology such as the distorted Born approximation or transport theory. Both coherent and incoherent multiple scattering techniques are explored. Once direct methods are developed, inversion techniques can be investigated.

Lang, R. H.↗
NASA NTRS2022

Impacts of Climate Change on Global Food Trade Networks

Countries' reliance on global food trade networks implies that regionally different climate change impacts on crop yields will be transmitted across borders. This redistribution constitutes a significant challenge for climate adaptation planning and may affect how countries engage in cooperative action. This paper investigates the long-term (2070–2099) potential impacts of climate change on global food trade networks of three key crops: wheat, rice and maize. We propose a simple network model to project how climate change impacts on crop yields may be translated into changes in trade. Combining trade and climate impact data, our analysis proceeds in three steps. First, we use network community detection to analyse how the concentration of global production in present-day trade communities may become disrupted with climate change impacts. Second, we study how countries may change their network position following climate change impacts. Third, we study the total climate-induced change in production plus import within trade communities. Results indicate that the stability of food trade network structures compared to today differs between crops, and that countries' maize trade is least stable under climate change impacts. Results also project that threats to global food security may depend on production change in a few major global producers, and whether trade communities can balance production and import loss in some vulnerable countries. Overall, our model contributes a baseline analysis of cross-border climate impacts on food trade networks.

climate change↗
NASA NTRS1983

An Empirical Bayes Approach to Spatial Analysis

Multi-channel LANDSAT data are collected in several passes over agricultural areas during the growing season. How empirical Bayes modeling can be used to develop crop identification and discrimination techniques that account for spatial correlation in such data is considered. The approach models the unobservable parameters and the data separately, hoping to take advantage of the fact that the bulk of spatial correlation lies in the parameter process. The problem is then framed in terms of estimating posterior probabilities of crop types for each spatial area. Some empirical Bayes spatial estimation methods are used to estimate the logits of these probabilities.

Morris, C. N.↗
NASA NTRS1985

Crop Characteristics Research: Growth and Reflectance Analysis

Much of the early research in remote sensing follows along developing spectral signatures of cover types. It was found, however, that a signature from an unknown cover class could not always be matched to a catalog value of known cover class. This approach was abandoned and supervised classification schemes followed. These were not efficient and required extensive training. It was obvious that data acquired at a single time could not separate cover types. A large portion of the proposed research has concentrated on modeling the temporal behavior of agricultural crops and on removing the need for any training data in remote sensing surveys; the key to which is the solution of the so-called 'signature extension' problem. A clear need to develop spectral estimaters of crop ontogenic stages and yield has existed even though various correlations have been developed. Considerable effort in developing techniques to estimate these variables was devoted to this work. The need to accurately evaluate existing canopy reflectance model(s), improve these models, use them to understand the crop signatures, and estimate leaf area index was the third objective of the proposed work. A synopsis of this research effort is discussed.

Badhwar, G. D.↗
NASA NTRSDate not supplied

Derivation and Testing of Consumptive Water Use Fraction for Specialty Crops

The Crop Consumptive Use Fraction (CCUF) expresses beneficial water use in the form of seasonal evapotranspiration of applied water (ETAW), relative to total irrigation volume. The metric is an indicator of the efficiency of agricultural water use and is a recommended component for preparation of agricultural water management plans in California. An FAO-56 based web application has been developed to facilitate retrospective evaluation of ETAW, and hence CCUF, at field level. The current application is optimized for prevailing climate in four of the state’s main growing regions: San Joaquin Valley, Sacramento Valley, Central Coast, and North Coast. User inputs include crop type, soil texture, irrigation method, daily irrigation volume, daily rainfall, and seasonal start/stop dates to define the analysis period. Time series of fractional green canopy cover (Fc), based on Landsat and Sentinel-2 Earth-resource satellite observations, are imported from NASA’s Satellite Irrigation Management Support (SIMS) system. Grass reference evapotranspiration (ETo) time series are accessed from Spatial CIMIS (California Irrigation Management Information System). Daily crop height (h) is estimated as a simple function of typical maximum height for the given crop type (from FAO-56) and Fc. A vegetation density coefficient (Kd) is derived from Fc and h. Stomatal control factors during mid-and late-season are applied to tree and vine crops. Typical values for minimum daily relative humidity and mean daily windspeed, derived from historical CIMIS weather data, are used to correct for regional deviations from standard climate (defined as RHmin=45%, windspeed =2m/s). The resulting daily basal crop coefficient (Kcb) represents the ET of a well-watered crop with minimal soil evaporation, relative to ETo. A soil water balance sub-model for the top 0.1 m is used to calculate daily evaporation coefficients (Ke). A second sub-model applies to the root zone to calculate crop water stress coefficients (Ks) and effective precipitation (Pe), which is the fraction of rainfall that is available for crop use. Those coefficients are combined with Kcb and ETo to calculate daily ETc. ETAW is then derived as cumulative ETc less cumulative Pe. For verification purposes, sensor installations were used to measure seasonal ETc in commercial fields for several annual and perennial specialty crops by soil water balance and energy balance methods. Model estimates of seasonal ETc show mean absolute error of <10% compared to the ground measurements.

Derivation↗
NASA NTRS1974

An experimental test of plant canopy reflectance models on cotton

Extensive data on the plant parameters necessary to evaluate any model are presented for a cotton crop. The variation of the bidirectional reflectance function with observer altitude, observer azimuth, and sun altitude angle is presented for a high density cotton crop having leaf index of 19. A comparison with the quantitative behavior obtained from the Suits model is accomplished in the wavelength region from 400 nm to 1050 nm.

Lemaster, E. W.↗
NASA NTRS1976

Linear dimensionality of Landsat agricultural data with implications for classification

A model for the Landsat multispectral scanner data, representing a generalization of the commonly used Gaussian model, has been formulated and analyzed. The model hypothesizes that the data for different crop types essentially lie on distinct hyperplanes in the feature space. Tests of this model reveal that: (1) the agricultural data from any single acquisition (i.e., four-channel) of Landsat are essentially two dimensional, regardless of the crop type; and (2) the data from different sites and different stages of crop development all lie on planes which are parallel. These findings have significant implications for data display, classification, feature extraction, and signature extension.

Wheeler, S. G.↗
NASA NTRS1985

Estimation of total above-ground phytomass production using remotely sensed data

Remote sensing potentially offers a quick and nondestructive method for monitoring plant canopy condition and development. In this study, multispectral reflectance and thermal emittance data were used in conjunction with micrometeorological data in a simple model to estimate above-ground total dry phytomass production of several spring wheat canopies. The fraction of absorbed photosynthetic radiation (PAR) by plants was estimated from measurements of visible and near-infrared canopy reflectance. Canopy radiation temperature was used as a crop stress indicator in the model. Estimated above-ground phytomass values based on this model were strongly correlated with the measured phytomass values for a wide range of climate and plant-canopy conditions.

Asrar, G.↗
NASA NTRS2012

Multi-Factor Impact Analysis of Agricultural Production in Bangladesh with Climate Change

Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040-2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in Yu et al. (2010), this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins' hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.

Ruane, Alex C.↗
NASA NTRS2016

Monitoring Interannual Variation in Global Crop Yield Using Long-Term AVHRR and MODIS Observations

Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data have been extensively applied for crop yield prediction because of their daily temporal resolution and a global coverage. This study investigated global crop yield using daily two band Enhanced Vegetation Index (EVI2) derived from AVHRR (1981-1999) and MODIS (2000-2013) observations at a spatial resolution of 0.05deg (approx.5 km). Specifically, EVI2 temporal trajectory of crop growth was simulated using a hybrid piecewise logistic model (HPLM) for individual pixels, which was used to detect crop phenological metrics. The derived crop phenology was then applied to calculate crop greenness defined as EVI2 amplitude and EVI2 integration during annual crop growing seasons, which was further aggregated for croplands in each country, respectively. The interannual variations in EVI2 amplitude and EVI2 integration were combined to correlate to the variation in cereal yield from 1982-2012 for individual countries using a stepwise regression model, respectively. The results show that the confidence level of the established regression models was higher than 90% (P value < 0.1) in most countries in the northern hemisphere although it was relatively poor in the southern hemisphere (mainly in Africa). The error in the yield predication was relatively smaller in America, Europe and East Asia than that in Africa. In the 10 countries with largest cereal production across the world, the prediction error was less than 9% during past three decades. This suggests that crop phenology-controlled greenness from coarse resolution satellite data has the capability of predicting national crop yield across the world, which could provide timely and reliable crop information for global agricultural trade and policymakers.

Crop phenology↗
NASA NTRS1976

Large Area Crop Inventory Experiment (LACIE). Results of LACIE integrated drought analysis (Southern U.S. Great Plains drought 1975-76)

The development and intensification of the drought in the United States southern Great Plains was monitored during the initial growing period of the 1975-76 winter wheat crop. Because of the severity of the drought conditions, a drought analysis plan was developed and implemented beginning on March 8, 1976. Sample segments and full-frame imagery were used at 9-day intervals to identify the drought area and quantify the effects on the wheat acreage. Yield model simulations were run to extrapolate the effects of the drought on yield estimates at harvest, assuming 10 and 90 percent of normal rainfall for subsequent months and 30-day forecast. A survey of LANDSAT data for improvement of distribution of rainfall patterns in the drought area was done for April and yield models run for drought affected crop reporting districts. Special aggregations were performed by the Crop Assessment Subsystem on the drought area to evaluate the utility of remote sensing to monitor the effect of the drought on wheat area, yield, and production.

Thompson, D. R.↗
NASA NTRS2011

Combining Observations in the Reflective Solar and Thermal Domains for Improved Mapping of Carbon, Water and Energy FLuxes

This study investigates the utility of integrating remotely sensed estimates of leaf chlorophyll (C(sub ab)) into a thermal-based Two-Source Energy Balance (TSEB) model that estimates land-surface CO2 and energy fluxes using an analytical, light-use-efficiency (LUE) based model of canopy resistance. Day to day variations in nominal LUE (LUE(sub n)) were assessed for a corn crop field in Maryland U.S.A. through model calibration with CO2 flux tower observations. The optimized daily LUE(sub n) values were then compared to estimates of C(sub ab) integrated from gridded maps of chlorophyll content weighted over the tower flux source area. Changes in Cab exhibited a curvilinear relationship with corresponding changes in daily calibrated LUE(sub n) values derived from the tower flux data, and hourly water, energy and carbon flux estimation accuracies from TSEB were significantly improved when using C(sub ab) for delineating spatio-temporal variations in LUE(sub n). The results demonstrate the synergy between thermal infrared and shortwave reflective wavebands in producing valuable remote sensing data for monitoring of carbon and water fluxes.

Houborg, Rasmus↗
NASA NTRS1977

Evaluating soil moisture and yield of winter wheat in the Great Plains using Landsat data

Locating areas where soil moisture is limiting to crop growth is important for estimating winter-wheat yields on a regional basis. In the 1975-76 growing season, we evaluated soil-moisture conditions and winter-wheat yields for a five-state region of the Great Plains using Landsat estimates of leaf area index (LAI) and an evapotranspiration (ET) model described by Kanemasu et al (1977). Because LAI was used as an input, the ET model responded to changes in crop growth. Estimated soil-water depletions were high for the Nebraska Panhandle, southwestern Kansas, southeastern Colorado, and the Texas Panhandle. Estimated yields in five-state region ranged from 1.0 to 2.9 metric ton/ha.

Heilman, J. L.↗