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At least 271 records · Page 15

Is There Ecological Information in Optical Polarization Data?

Optical linear polarization? In remote sensing it's due to specular reflection. The first surface that incident light encounters - a smooth water surface or the waxy first surface of a leaf's cuticle, if it's even somewhat smooth (i.e. shiny) - will specularly reflect and linearly polarize the incident light. We provide three examples of the types of ecological information contained in remotely sensed optical linear polarization measurements. Remove the surface reflection to better see the interior. The linearly polarized light reflected by leaf surfaces contains no information about cellular pigments, metabolites, or water contained in the leaf interiors of a plant canopy, because it never enters the leaf interior to interact with them. Thus, for purposes of remotely sensing the leaf interiors of a plant canopy, the linearly polarized light should be subtracted from the total reflected light, because including it would add noise to the measurement. In particular 'minus specular' vegetation indices should allow improved monitoring of a plant canopy's physiological processes. Estimate plant development stage and yield. Wheat and sorghum grain heads, following emergence, rapidly extend upward and very quickly tower over nearby leaves, partially blocking our view of the sunlight reflected by those leaf surfaces. The resulting decrease in the amount of surface reflected and polarized sunlight, if monitored over time, potentially allows per-field estimates of the dates of the heading and flowering development stages to be interleaved with weather data in models, which is key to better estimating per-field grain yield. Similar polarization changes may occur in other grasses, such as oats, barley, corn and rice, each a crop so widely grown that it potentially affects climate at the regional scale. Wetlands Mapping. The sunlight specularly reflected by surface waters is blindingly bright, spectrally flat and polarized - all of which telegraphs that the ground area is inundated. Inundated soils exchange methane with the atmosphere; non-inundated soils, carbon dioxide. Aquatic plants growing through the water surface pipe the soil-produced methane via the stomata to the atmosphere, enhancing exchanges rates by factors of 10-20 compared to ebullition (bubbling) or diffusion through the water column to the atmosphere. Thus, mapping wetland areas into three community types - inundated areas with emergent vegetation, open water and uplands - provides potentially key information to water, carbon and energy budgets at landscape to global scales.

Water↗

Investigation of thematic mapper spatial, radiometric, and spectral resolution

Low-altitude aircraft scanner data were employed in simulating the spatial resolution, radiometric sensitivity and spectral bandwidth parameters of the proposed Landsat Thematic Mapper (TM). The 30 to 40 m resolution of the TM was found to provide significant improvement over current Landsat resolution (50 to 60 m) in crop mensuration, especially for Western Europe and India where field sizes average from one to four hectares. In terms of radiometric sensitivity, a noise equivalent reflectance value of 0.5% was held to be necessary for discrimination of spectrally similar data; in addition, all of the six proposed TM spectral bands were shown to be necessary for monitoring at some point during the growing season.

Morgenstern, J. P.↗

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↗

Global vegetation dynamics - Satellite observations over Asia

The weekly global vegetation index (GVI) derived from the NOAA AVHRR instrument has been analyzed for the 1982-1985 period over a wide range of vegetation formations of Asia. Temporal development curves of the index are presented for environments ranging from the desert of central Asia to the tropical forest of Borneo. The paper shows that, despite the coarse resolution of the GVI product, a large set of useful information on ecosystem dynamics and cropping practices can be consistently derived from time series of such data. In addition, it is shown that the impact of the 1982-1983 El Nino Southern Oscillation-related drought can be detected in the GVI data through an analysis of anomalies in the development of selected vegetation formations. The relevance of such analysis for global vegetation monitoring and change detection is then underlined.

Malingreau, J.-P.↗

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↗

USDA/federal user of LANDSAT remote sensing

Developed and potential uses of remote sensing in crop condition and acreage assessment, renewable resources inventories, conservation practices, and water and forest management applications are described. Operational approaches, the adaptation of procedures to needs, and the agency's concern about data continuity and cost are discussed as well as support for future technology development for enhanced sensing capability. The use of improved camera systems for soil mapping and conservation monitoring from space shuttle, and of aerospace radar to improve soil moisture monitoring are mentioned.

Allen, R.↗

Verification and Validation of NASA-Supported Enhancements to PECAD's Decision Support Tools

The NASA Applied Sciences Directorate (ASD), part of the Earth-Sun System Division of NASA's Science Mission Directorate, has partnered with the U.S. Department of Agriculture (USDA) to enhance decision support in the area of agricultural efficiency-an application of national importance. The ASD integrated the results of NASA Earth science research into USDA decision support tools employed by the USDA Foreign Agricultural Service (FAS) Production Estimates and Crop Assessment Division (PECAD), which supports national decision making by gathering, analyzing, and disseminating global crop intelligence. Verification and validation of the following enhancements are summarized: 1) Near-real-time Moderate Resolution Imaging Spectroradiometer (MODIS) products through PECAD's MODIS Image Gallery; 2) MODIS Normalized Difference Vegetation Index (NDVI) time series data through the USDA-FAS MODIS NDVI Database; and 3) Jason-1 and TOPEX/Poseidon lake level estimates through PECAD's Global Reservoir and Lake Monitor. Where possible, each enhanced product was characterized for accuracy, timeliness, and coverage, and the characterized performance was compared to PECAD operational requirements. The MODIS Image Gallery and the GRLM are more mature and have achieved a semi-operational status, whereas the USDA-FAS MODIS NDVI Database is still evolving and should be considered

McKellipo, Rodney↗

A Lunar Ground Truth of Microbes that are Integral to Sustaining Biological Life Support Systems

A lunar outpost such as Gateway or a Mars transit vehicle will use a semi-closed to closed loop biological life support system (BLSS) adapted for microgravity and high levels of radiation. A moon habitat will likely exist under partial gravity conditions and sustained levels of high radiation as a semi-closed loop system able to get resources from Earth via Gateway. Additionally, the moon will act as a closed-loop testbed for Mars habitat operations and will employ various elements of in situ resource utilization (ISRU). Perhaps the longest running BLSS collective study to date is that of the European MELiSSA (Micro Ecological Life Support System Alternative) project, a circular life support system, established to gain knowledge on regenerative systems aimed at the highest degree of autonomy to produce food, water and oxygen from mission wastes. This setup has evaluated the use of discrete microbial compartments and a higher plant compartment to carry out the necessary life support functions to support crew. Such compartments utilize thermophilic anaerobes to break down human and inedible plant wastes, photoheterotrophic bacteria that can further metabolize volatile fatty acids, nitrifying bacteria that can convert ammonium to plant and microalgae available nitrates, and photoautotrophic bacteria and higher plants which will convert carbon dioxide to oxygen, purify water, and provide food for human consumption. This being the most advanced BLSS model example to date -aside from the Yuegong-1, Chinese Lunar Palace- with the most defined compartment composition, and the assumption that other BLSSs will build upon such models, it becomes relevant to study the effects of the lunar environment upon the microbes that are integral to a BLSS. In ramping up to advanced stage BLSS systems, relatively simple experiments can be conducted on Artemis missions using continuous culturing and sampling of the model microbes, subsequently subjected to DNA sequencing for mutational analysis monitoring and chemical analysis to assess the sustained ability to carry out their hallmark biochemical processes efficiently. A suite of microbes should be assessed, prioritizing those with the duel capacity to be utilized in an BLSS and the ability to biochemically facilitate ISRU goals, such as the ability to transform the biogeochemistry of Moon or Martian regolith into materials that can support crop growth or extract elements of industrial significance such as aluminum or iron.

Biological Life Support Systems↗

International Symposium on Remote Sensing of Environment, 9th, University of Michigan, Ann Arbor, Mich., April 15-19, 1974, Proceedings. Volumes 1, 2 & 3

The present work gathers together numerous papers describing the use of remote sensing technology for mapping, monitoring, and management of earth resources and man's environment. Studies using various types of sensing equipment are described, including multispectral scanners, radar imagery, spectrometers, lidar, and aerial photography, and both manual and computer-aided data processing techniques are described. Some of the topics covered include: estimation of population density in Tokyo districts from ERTS-1 data, a clustering algorithm for unsupervised crop classification, passive microwave sensing of moist soils, interactive computer processing for land use planning, the use of remote sensing to delineate floodplains, moisture detection from Skylab, scanning thermal plumes, electrically scanning microwave radiometers, oil slick detection by X-band synthetic aperture radar, and the use of space photos for search of oil and gas fields. Individual items are announced in this issue.

Source record↗

Applications of remote sensing technology to U.S. Water resource management

Applications of Landsat data to assessing the available water supply in the U.S. for agricultural puposes as a program of the NASA Office of Space Science and Applications are described. Snow melt runoff predictions are performed with multitemporal Landsat imagery to measure the extent of mountain snow packs in the Rockies in order to produce stream flow forecast models. The data is used for decisions of which crops to plant, based on irrigation water which will be accessible in eleven western U.S. states. Imagery has tracked the growth of irrigated lands watered from the Ogallala aquifer in the central U.S., and is providing a data base for calculating the aquifer depletion rates. Studies are preceeding in temporally mapping the kinds of vegetation growing in the Suwannee Sound in Florida to monitor the intrusion of salt water, which would reduce the River's usefulness for irrigation. Finally, capabilities of the Thematic Mapper are reviewed.

Weber, J. D.↗

Forecasting Plant Productivity and Health Using Diffuse-to-Global Irradiance Ratios Extracted from the OMI Aerosol Product

Atmospheric aerosols are a major contributor to diffuse irradiance. This Candidate Solution suggests using the OMI (Ozone Monitoring Instrument) aerosol product as input into a radiative transfer model, which would calculate the ratio of diffuse to global irradiance at the Earth s surface. This ratio can significantly influence the rate of photosynthesis in plants; increasing the ratio of diffuse to global irradiance can accelerate photosynthesis, resulting in greater plant productivity. Accurate values of this ratio could be useful in predicting crop productivity, thereby improving forecasts of regional food resources. However, disagreements exist between diffuse-to-global irradiance values measured by different satellites and ground sensors. OMI, with its unique combination of spectral bands, high resolution, and daily global coverage, may be able to provide more accurate aerosol measurements than other comparable sensors.

Knowlton, Kelly↗

The Design of a Remote Sensing Data Acquisition Campaign for Precision Agriculture and Some Early Results

In the 1970s NASA and the Department of Agriculture attempted to use the new Landsat MSS system for agricultural purposes. The program had relatively little success. With the advent of differential GPS, yield monitors on harvest equipment and higher spatial resolution remote sensing systems it seemed likely the situation should be reexamined. Therefore, a campaign of data acquisition involving remote sensing and other modalities with dependent research was assembled and funded by the Space Grant Consortia in Alabama and Georgia. The design of the remote sensing data acquisition was driven by the biology and physics of the crop system and limited by the available sensor platforms. Major parameters included crop stage, spatial resolution, seasonal and daily weather conditions, and which portion of the EM spectrum would actually capture the most discriminating information. Joint visible and Near IR with Thermal IR would permit use of existing indices, such as greenness, as well as phenomena driven by the plant' s evapotranspiration. Spatial resolution in the 2-5 meter range was chosen, avoiding many complexities caused by aliasing crop row spacing at, higher resolutions yet finer than the harvester's tightest recording rate. This dictates use of an airborne system. Use of an airborne system also makes scheduling around weather much simpler than use of satellite data. Active video calibration was recognized as essential if quantitative measures were ever to be obtained or reproduced. The system would also have to have onboard geoOF1 Based on these elements 3 data acquisitions have been flown. Seven flight lines were flown twice in 1998 and 16 lines flown in 1999. Total raw data is several GBytes. All of the data has now been geometrically corrected and some preliminary analysis accomplished. The thermal bands have an extremely high correlation with yield. For one@test case with corn, correlation in excess of 0.86 was obtained from a data acquisition two months prior to harvest! Soil images show significant within field variation in clay, soil brightness and emissivity. Light wind has been found to effect the reflectance and temperature of broad leaf crops, including soybeans, cotton and peanuts. Clearly, this work has already demonstrated some very important results. With continued development of the remote sensing technology there is good reason to believe this research will soon be able to help the individual farmer.

Rickmanl, D.↗

A Continuation of Base-Line Studies for Environmentally Monitoring Space Transportation System (STS) at John F. Kennedy Space Center: Terrestrial Community Analysis - Volume 1

Vegetation and small mammal populations in or around the Merritt Island area were studied. Thirty sites were selected from plant communities which were relatively free of logging, grazing, and clearing operations. The vegetative analysis was designed to yield a quantitative description and ecological explanation of the major types of upland vegetation in order to determine the possible future effects of NASA space activities on them. Changes in the relative abundance of small mammal populations, species diversity, standing crop biomass, reproductive activity, and other demographic features were documented in order to gather sufficient information on these populations so that it would be possible to detect even the smaller nonnatural behavior changes in the mammals which might be attributable to NASA space activities.

Stout, I. J.↗

The Photochemical Reflectance Index from Directional Cornfield Reflectances: Observations and Simulations

The two-layer Markov chain Analytical Canopy Reflectance Model (ACRM) was linked with in situ hyperspectral leaf optical properties to simulate the Photochemical Reflectance Index (PRI) for a corn crop canopy at three different growth stages. This is an extended study after a successful demonstration of PRI simulations for a cornfield previously conducted at an early vegetative growth stage. Consistent with previous in situ studies, sunlit leaves exhibited lower PRI values than shaded leaves. Since sunlit (shaded) foliage dominates the canopy in the reflectance hotspot (coldspot), the canopy PRI derived from field hyperspectral observations displayed sensitivity to both view zenith angle and relative azimuth angle at all growth stages. Consequently, sunlit and shaded canopy sectors were most differentiated when viewed along the azimuth matching the solar principal plane. These directional PRI responses associated with sunlit/shaded foliage were successfully reproduced by the ACRM. As before, the simulated PRI values from the current study were closer to in situ values when both sunlit and shaded leaves were utilized as model input data in a two-layer mode, instead of a one-layer mode with sunlit leaves only. Model performance as judged by correlation between in situ and simulated values was strongest for the mature corn crop (r = 0.87, RMSE = 0.0048), followed by the early vegetative stage (r = 0.78; RMSE = 0.0051) and the early senescent stage (r = 0.65; RMSE = 0.0104). Since the benefit of including shaded leaves in the scheme varied across different growth stages, a further analysis was conducted to investigate how variable fractions of sunlit/shaded leaves affect the canopy PRI values expected for a cornfield, with implications for 20 remote sensing monitoring options. Simulations of the sunlit to shaded canopy ratio near 50/50 +/- 10 (e.g., 60/40) matching field observations at all growth stages were examined. Our results suggest in the importance of the sunlit/shaded fraction and canopy structure in understanding and interpreting PRI.

Cheng, Yen-Ben↗

Using Landsat digital data to detect moisture stress

A technique utilizing transformed Landsat digital data for detection of agricultural drought was empirically defined during the 1976 South Dakota drought. During 1977, the procedure was expanded to the Great Plains for evaluation as a technique for detecting and monitoring vegetative water stress over large areas. The technique, Green Index Number (GIN), uses Landsat digital data from 5 by 6 nautical mile sampling frames (segments) to indicate when the vegetation within the segment is undergoing drought. At known growth stages for wheat, segments were classified as drought or non-drought areas. The remote-sensing-based information was compared to a weekly ground-based index (Crop Moisture Index) provided by the United States Department of Commerce. This comparison demonstrated a high degree of agreement between the 18-day remote sensing technique and the ground-based weekly data. Maps based on GIN of parts of the USSR and Australia were produced with a two-week lag and later compared with other crop assessments of crop conditions in these areas. These maps were judged to be in general agreement with the other data sources.

Thompson, D. R.↗

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.↗

Monitoring flood damage with satellite imagery

During analysis of ERTS-1 imagery for land use patterns a large impoundment of water was observed in a location that was normally farmland. Subsequent investigation revealed that the satellite had recorded the remaining floodwaters from a severe local rainstorm that had occurred four days prior to the overpass. The inundated area was measured using the automatic planimeter associated with the signal analysis and dissemination equipment located at the Remote Sensing Institute. The area measurement coupled with estimates of the land use and productivity of the region permitted an estimate of the crop damage loss for the inundated area.

Benson, L. A.↗

Landsat Science: 40 Years of Innovation and Opportunity

Landsat satellites have provided unparalleled Earth-observing data for nearly 40 years, allowing scientists to describe, monitor and model the global environment during a period of time that has seen dramatic changes in population growth, land use, and climate. The success of the Landsat program can be attributed to well-designed instrument specifications, astute engineering, comprehensive global acquisition and calibration strategies, and innovative scientists who have developed analytical techniques and applications to address a wide range of needs at local to global scales (e.g., crop production, water resource management, human health and environmental quality, urbanization, deforestation and biodiversity). Early Landsat contributions included inventories of natural resources and land cover classification maps, which were initially prepared by a visual interpretation of Landsat imagery. Over time, advances in computer technology facilitated the development of sophisticated image processing algorithms and complex ecosystem modeling, enabling scientists to create accurate, reproducible, and more realistic simulations of biogeochemical processes (e.g., plant production and ecosystem dynamics). Today, the Landsat data archive is freely available for download through the USGS, creating new opportunities for scientists to generate global image datasets, develop new change detection algorithms, and provide products in support of operational programs such as Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD). In particular, the use of dense (approximately annual) time series to characterize both rapid and progressive landscape change has yielded new insights into how the land environment is responding to anthropogenic and natural pressures. The launch of the Landsat Data Continuity Mission (LDCM) satellite in 2012 will continue to propel innovative Landsat science.

Cook, Bruce D.↗