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In the Large Area Crop Inventory Experiment a technique was devised using a vector transformation of Landsat digital data to indicate when vegetation is undergoing moisture stress. A relation was established between the remote-sensing-based criterion (the Green Index Number) and a ground-based criterion (Crop Moisture Index).
Wheat is the most important commodity traded in the international food market. Thus, accurate and timely information on wheat production can help mitigate food price fluctuations. Within the existing operational regional and global scale agricultural monitoring systems that provide information on global crop yield and area forecasts, there are still fundamental gaps: #1. Lack of quantitative Earth Observation (EO) derived crop information, #2. Lack of global but detailed (national or subnational level) and timely crop production forecasts and #3. Lack of information on forecast uncertainties. In this study we present the Agriculture Remotely-sensed Yield Algorithm (ARYA) an EO-based method, advancing the state of EO data application and usage (addressing gap #1) to forecast wheat yield. The algorithm is based on the evolution of the Difference Vegetation Index (DVI) using MODIS data at 1km resolution and the Growing Degree Days (GDD) from reanalysis data. Additionally, we explore how Land Surface Temperature (LST) can be included into the model and whether this parameter adds any value to the model performance when combined with the optical information. ARYA is implemented at the national and subnational level to forecast winter wheat yield in the main wheat exporting countries of US, Russia, Ukraine, France, Germany, Australia and Argentina from 2001 to 2019 (covering over 70% of wheat exports globally) in a timely manner by providing daily forecasts (addressing gap #2). The results show that ARYA provides yield estimations with RMSE’s within 0.3 ± 0.1 t/ha at national level and 0.6 ± 0,1 t/ha at subnational level after Day Of the Year (DOY) 140 (mid May) in the Northern Hemisphere and DOY 280 (beginning of October) in the Southern Hemisphere. This means that ARYA can provide crop yield estimates of wheat yield with 5-15 % error at national and 7-20 % error at subnational level starting from 2 to 2.5 months prior to harvest.
The multispectral scanner capabilities of LANDSAT 1 were tested over California's Monterey Bay area and portions of the San Joaquin Valley. Using both computer aided and image interpretive processing techniques, the LANDSAT 1 data were analyzed to determine their potential application in terms of land use and agriculture. Utilizing LANDSAT 1 data, analysts were able to provide the identifications and areal extent of the individual land use categories ranging from very general to highly specific levels (e.g., from agricultural lands to specific field crop types and even the different stages of growth). It is shown that the LANDSAT system is useful in the identification of major crop species and the delineation of numerous land use categories on a global basis and that repeated surveillance would permit the monitoring of changes in seasonal growth characteristics of crops as well as the assessment of various cultivation practices with a minimum of onsite observation. The LANDSAT system is demonstrated to be useful in the planning and development of resource programs on earth.
Insect sex pheromones are chemical compounds that insects release to attract mates over distances of hundreds of meters or even kilometers, in complete darkness and without any audible signals. Use of synthetic forms of key compounds have in some cases become an essential component of monitoring and/or managing key pests of agricultural crops, including navel orangeworm (Amyelois transitella) (NOW) in California tree nuts There are currently multiple commercially available mating disruption products available for NOW and recent studies have demonstrated that they can be effectively used to reduce crop damage. How mating disruption works is not fully established and likely varies across products and target species. For instance, it is not well understood how synthetic pheromones compete with natural pheromones and, in the case of monitoring, how efficaciously the insect follows the diffusing plumes and how those evolve from emission points, especially across large blocks and at plot borders. We proposed to evaluate the use of enhanced Raman spectroscopy for the detection of synthetic and natural NOW pheromones and, were that to be successful, use it in subsequent years to measure pheromone diffusion in orchards.
Insect sex pheromones are chemical compounds that insects release to attract mates over distances of hundreds of meters or even kilometers, in complete darkness and without any audible signals. Use of synthetic forms of key compounds have in some cases become an essential component of monitoring and/or managing key pests of agricultural crops, including navel orangeworm (Amyelois transitella) (NOW) in California tree nuts There are currently multiple commercially available mating disruption products available for NOW and recent studies have demonstrated that they can be effectively used to reduce crop damage. How mating disruption works is not fully established and likely varies across products and target species. For instance, it is not well understood how synthetic pheromones compete with natural pheromones and, in the case of monitoring, how efficaciously the insect follows the diffusing plumes and how those evolve from emission points, especially across large blocks and at plot borders. We proposed to evaluate the use of enhanced Raman spectroscopy for the detection of synthetic and natural NOW pheromones and, were that to be successful, use it in subsequent years to measure pheromone diffusion in orchards.
Insect sex pheromones are chemical compounds that insects release to attract mates over distances of hundreds of meters or even kilometers, in complete darkness and without any audible signals. Use of synthetic forms of key compounds have in some cases become an essential component of monitoring and/or managing key pests of agricultural crops, including navel orangeworm (Amyelois transitella) (NOW) in California tree nuts. There are currently multiple commercially available mating disruption products available for NOW and recent studies have demonstrated that they can effectively help reducing crop damage. How mating disruption works is not fully established and likely varies across products and target species. For instance, it is not well understood how synthetic pheromones compete with natural ones and, in the case of monitoring, how efficaciously the insect follows the diffusing plumes and how those evolve from emission points, especially across large blocks and at plot borders.
Insect sex pheromones are chemical compounds that insects release to attract mates over distances of hundreds of meters or even kilometers, in complete darkness and without any audible signals. Use of synthetic forms of key compounds have in some cases become an essential component of monitoring and/or managing key pests of agricultural crops, including navel orangeworm (Amyelois transitella) (NOW) in California tree nuts. There are currently multiple commercially available mating disruption products available for NOW and recent studies have demonstrated that they can be effectively used to reduce crop damage. How mating disruption works is not fully established and likely varies across products and target species. For instance, it is not well understood how synthetic pheromones compete with natural pheromones and, in the case of monitoring, how efficaciously the insect follows the diffusing plumes, especially across large blocks and at plot borders. Whether an aerosol puffer is used for mating disruption or lure for monitoring population development, we currently lack the ability to fully understand how these synthetic pheromone compounds diffuse away from their point-source of emission – and subsequently how this might affect the efficacy of mating disruption and/or the accuracy of monitoring efforts. In response to these unknowns, Raman spectroscopy may offer a way to detect synthetic pheromone because of its analytical properties and recent technological advancements. In combination with nanostructured probes, RS has demonstrated the capability to detect volatiles at extremely low concentrations. We proposed to evaluate the use of Raman for the detection of synthetic and natural NOW pheromones and, were that to be successful, use this tool in subsequent years to measure pheromone diffusion in orchards.
The National Predictive Modeling Tool Initiative (NPMTI) is a multi-year program to develop research-based “tools” to forecast incidences of diseases and mycotoxins affecting U.S. crops. The overall goals of NPMTI are to: (1) ensure crop sustainability and crop quality; (2) provide climate change resilience; (3) improve soil health; (4) monitor pathogens and microbial diversity in the environment, but not limited to crop residues, soils, and air; (5) improve disease management thereby reducing yield losses; (6) increase precision of in-season disease management tactics; and (7) increase precision of pesticide use.
Directional reflectance factors of an alfalfa canopy were determined and related to canopy structure, agronomic variables, and irradiance conditions at four periods during a cutting cycle. Nadir and off-nadir reflectance factors decreased with increasing biomass in Thematic Mapper band 3(0.63-0.69 micrometer) and increased with increasing biomass in band 4(0.76-0.90 micrometer). The sensor view angle had less impact on perceived reflectance as the alfalfa progressed from an erectophile canopy of stems after harvest to a near planophile canopy of leaves at maturity. Studies of directional reflectance are needed for testing and upgrading vegetation canopy models and to aid in the complex interpretation problems presented by aircraft scanners and pointable satellites where illumination and viewing geometries may vary widely. Distinct changes in the patterns of radiance observed by a sensor as structural and biomass changes occur are keys to monitoring the growth and condition of crops.
The following multispectral remote-sensing techniques were compared to determine the most suitable method for routinely monitoring agricultural subsurface drain conditions: airborne scanning, covering the visible through thermal-infrared (IR) portions of the spectrum; color-IR photography; and natural-color photography. Color-IR photography was determined to be the best approach, from the standpoint of both cost and information content. Aerial monitoring of drain conditions for early warning of tile malfunction appears practical. With careful selection of season and rain-induced soil-moisture conditions, extensive regional surveys are possible. Certain locations, such as the Imperial Valley, Calif., are precluded from regional monitoring because of year-round crop rotations and soil stratification conditions. Here, farms with similar crops could time local coverage for bare-field and saturated-soil conditions.
The Ecological Life Support System, a plant growth experiment now in its third year of closed chamber production at the NASA Kennedy Space Center, is discussed. Possible spin-off applications of hydrophonics experiments are noted. It is projected that long-term goals will include the integration of this garden system into the process of waste recycling for fertilization, air refreshment, and potable water recovery in a closed environment. The Biomass Production Chamber, a two-story bubble-shape steel biosphere modified from a Mercury/Gemini program attitude chamber provides a usable volume of 7.3 m x 3.6 m in diameter containing growing racks, piping for nutrient solutions, specialized lighting and sensors that provide information to the computers controlling the chamber and its functions. Computer programs provide highly sensitive monitoring and regulation of the system. Crops successfully harvested to date include dwarf wheat, lettuce, and soybeans.
It is very important to know how much rain and snow falls around the world for uses that range from crop forecasting to disaster response, drought monitoring to flood forecasting, and weather analysis to climate research. Precipitation is usually measured with rain gauges, but rain gauges don t exist in areas that are sparsely populated, which tends to be a good portion of the globe. To overcome this, meteorologists use satellite data to estimate global precipitation. However, it is difficult to estimate rain and especially snow in cold climates using most current satellites. The satellite sensors are often "confused" by a snowy or frozen surface and therefore cannot distinguish precipitation. One commonly used satellite-based precipitation data set, the Global Precipitation Climatology Project (GPCP) data, overcomes this frozen-surface problem through the innovative use of two sources of satellite data, the Television Infrared Observation Satellite Operational Vertical Sounder (TOVS) and the Atmospheric Infrared Sounder (AIRS). Though the GPCP estimates are generally considered a very reliable source of precipitation, it has been difficult to assess the quality of these estimates in cold climates due to the lack of gauges. Recently, the Finnish Meteorological Institute (FMI) has provided a 12-year span of high-quality daily rain gauge observations, covering all of Finland, that can be used to compare with the GPCP data to determine how well the satellites estimate cold-climate precipitation. Comparison of the monthly GPCP satellite-based estimates and the FMI gauge observations shows remarkably good agreement, with the GPCP estimates being 6% lower in the amount of precipitation than the FMI observations. Furthermore, the month-to-month correlation between the GPCP and FMI is very high at 0.95 (1.0 is perfect). The daily GPCP estimates replicate the FMI daily occurrences of precipitation with a correlation of 0.55 in the summer and 0.45 in the winter. The winter result indicates the GPCP estimates have skill in "seeing" snowfall, which is the most challenging situation. Thus, the GPCP data set successfully overcomes a current limitation in satellite meteorology, namely the estimation of cold-climate precipitation. The success of the GPCP data set bodes well for future missions, whose instrumentation is specifically designed to give even more information for addressing cold-climate precipitation.
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This study uses Synthetic Aperture Radar (SAR) data to identify active agricultural extent in Mato Grosso, Brazil. Coefficient of variation (CoVar) calculations were applied to year-long time series stacks to identify areas of crop growth. A CoVar threshold value was determined using crop reference layers, then applied to create a crop/non-crop binary map. In the Mato Grosso region, CoVar-based processing resulted in high performance crop classification and allowed for the monitoring of dynamic cropland expansion between 2016 and 2021.
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Space crop production will be important in future long duration exploration missions to supplement the packaged diet with fresh bioactive nutrients. Plant care and the addition of fresh veggies to the diet may also have a role in astronaut well-being. Pick-and-eat salad crops are the best candidates for this near-term supplementation since they require minimal processing or preparation to add to meals. While light quality can strongly influence plant responses on Earth, the impacts of light quality on plant growth and composition in spaceflight remain unclear. The VEG-04 experiment uses two Veggie plant growth chambers on the International Space Station to simultaneously test different red: blue light ratios on the growth of Mizuna mustard, a leafy green salad crop. In addition to plant health and yield, the composition of key nutrients is assessed. Astronauts conduct on-board organoleptic evaluation of the fresh produce. Microbial food safety of returned produce is examined, and a Hazard Analysis Critical Control Point (HACCP) plan has been developed for this crop. VEG-04 consists of two experiments, one lasting 28 days with a single harvest, and the second lasting 56 days, with three cut-and-come-again harvests. These different scenarios provide an opportunity to test two production concepts, examine different fertilizers, monitor microbial changes over time for this crop, and assess potential impacts of interacting with plants on crew behavioral health and performance in spaceflight operations. In ground testing, plant growth was not significantly different across the different light treatments, however nutrient composition did differ significantly. Flight test results will be compared with ground data. This research was co-funded by NASA's Human Research Program and Space Biology in the ILSRA 2015 NRA call.
The emerging sector of offshore kelp aquaculture represents an opportunity to produce biofuel feedstock to help meet growing energy demand. Giant kelp represents an attractive aquaculture crop due to its rapid growth and production, however precision farming over large scales is required to make this crop economically viable. These demands necessitate high frequency monitoring to ensure outplant success, maximum production, and optimum quality of harvested biomass, while the long distance from shore and large necessary scales of production makes in person monitoring impractical. Remote sensing offers a practical monitoring solution and nascent imaging technologies could be leveraged to provide daily products of the kelp canopy and subsurface structures over unprecedented spatial scales. Here, we evaluate the efficacy of remote sensing from satellites and aerial and underwater autonomous vehicles as potential monitoring platforms for offshore kelp aquaculture farms. Decadal-scale analyses of the Southern California Bight showed that high offshore summertime cloud cover restricts the ability of satellite sensors to provide high frequency direct monitoring of these farms. By contrast, daily monitoring of offshore farms using sensors mounted to aerial and underwater drones seems promising. Small Unoccupied Aircraft Systems (sUAS) carrying lightweight optical sensors can provide estimates of canopy area, density, and tissue nitrogen content on the time and space scales necessary for observing changes in this highly dynamic species. Underwater color imagery can be rapidly classified using deep learning models to identify kelp outplants on a longline farm and high acoustic returns of kelp pneumatocysts from side scan sonar imagery signal an ability to monitor the subsurface development of kelp fronds. Current sensing technologies can be used to develop additional machine learning and spectral algorithms to monitor outplant health and canopy macromolecular content, however future developments in vehicle and infrastructure technologies are necessary to reduce costs and transcend operational limitations for continuous deployment in an offshore setting.