A Crops and Soils Data Base for Scene Radiation Research (original) (raw)
Rocznik Ochrona Środowiska, 2024
The RE band of electromagnetic radiation has recently become the subject of interest in remote sensing due to its greater penetration into the plant structure than the commonly used NIR band. It is particularly important in cultivating corn, which is characterised by considerable thick foliage during the growth period. While sensors equipped with this channel are used in satellite remote sensing and onboard drones, they are not implemented in airborne imaging systems. An airborne remote sensing station was constructed, including, in addition to the traditional R, G, B and NIR image components, also the RE channel and a laser scanner (ALS). Data processing involves geometric calibration and the creation of a multi-channel orthophoto map. The data processed in this way was tested by analysing several series of aerial recordings of a corn field, which involved developing interpretation keys based on selected vegetation indices and assigning individual groups of pixels with five plant health classes. This study focused on the comparative assessment of the effects of using the NDVI, GNDVI, NDRE and SAVI indices, comparing their results to yield measurements (CHM) and the results of field measurements of plants at the end of the growing season. Promising results with a high degree of correlation were obtained.
Remote Sensing of Environment, 2010
Crop descriptors, such as leaf area index, crop cover fraction, and leaf chlorophyll content, can be successfully estimated using appropriate spectral indices from the visible and near infrared spectral regions. However, these indices do not provide estimates of dry biomass, an important indicator of crop productivity. For estimating crop aboveground dry biomass and yield, this study developed an approach to integrate crop stressors and crop descriptors derived from optical remote sensing data with the Monteith radiation use efficiency model. Multi-temporal remote sensing data were acquired by the Compact Airborne Spectrographic Imager and the Landsat-5/7 Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) sensors to monitor the growth conditions of corn in the 2001 and 2006 growing seasons. The modified triangular vegetation index (MTVI2) derived from the remote sensing data was used to estimate the fraction of absorbed photosynthetically active radiation (f APAR ). A canopy structure dynamics model was then used to simulate the seasonal variation of f APAR . Crop water stress was estimated from the near and shortwave infrared reflectance of the Landsat images for a dry period in the 2001 growing season. By estimating leaf chlorophyll content using the Transformed Chlorophyll Absorption in Reflectance Index (TCARI) in combination with the Optimized Soil Adjusted Vegetation Index (OSAVI), different levels of nitrogen content could be identified. For the two growing seasons, the aboveground dry biomass and yield were linearly related with the cumulative absorbed photosynthetically active radiation (APAR) using the Monteith radiation use efficiency model. The cumulative APAR accounted for 96% of the corn aboveground dry biomass variability and 72% of the yield variability. Biomass and yield variability were partly explained by the variations in crop water stress intensity, which was dependent on soil texture. The seasonal radiation use efficiency was stable over the 2 years and was about 3.9 g MJ − 1 , with a confidence interval of 0.6 g MJ − 1 at the 95% confidence level. The assimilation of remotely sensed data into the radiation use efficiency model performed well for monitoring dry biomass accumulation and estimating corn yields.
The radiation balance of several field crops
Archiv für Meteorologie, Geophysik und Bioklimatologie Serie B, 1969
Radiation balance components have been measured over field plots of oats, beans, sunflower and corn. Daytime variations of incoming shortwave radiation, net radiation, net long-wave radiation and the reflection coefficient are discussed. Linear regression equations relating measurements of hourly averages of net radiation on incoming shortwave radiation and net shortwave radiation, for each crop and for all crops grouped in cloudless and in overcast days, were highly significant: r-~ > 0.98. Daily totals of net radiation, reflected shortwave radiation and net long-wave radiation, as a percentage of incoming shortwave radiation, were nearly the same for each crop as well for cloudy as for cloudless days. About 25 % of the daily total of incoming shortwave radiation was reflected, 18 ~
Remote Ground-Based and Satellite Monitoring of Vegetation
Herald of the Russian Academy of Sciences, 2018
The purpose of this study is to analyze the relationship between crop yields and total chlorophyll potential of different barley and oats cultivars. For this purpose, we used the spectra of grain crops obtained from ground-based remote sensing, and laboratory data. Ground-based data were obtained at the experimental fields located in the Krasnoyarskii Krai. Experiments were carried out in different seasons and under various lighting conditions. Spectral measurements were done with a double-beam spectrophotometer. It was installed on the mobile work platform at heights of 5 to 18 m. The study showed good correlation between crop yields and total chlorophyll potential for barley and oats cultivars.
2000
CHRIS is the first spaceborne multiangular imaging spectrometer with high spatial resolution. CHRIS data was analyzed to deliver input information for precision agriculture. The reflectance spectra obtained were compared with optical radiative transfer simulation results of SLC, an extended version of the canopy reflectance model GeoSAIL. Directional reflectance spectra are extracted and the directional variations compared to the model results.
Introduction to Remote Sensing of Biomass
2011
Planet Earth is distinguished from other Solar System planets by two major categories: Oceans and Land Vegetation. The oceans cover ~70% of the Earth's surface; land comprises 30%. On the land itself, the first order categories break down as follows: Trees = 30%; Grasses = 30%; Snow and Ice = 15%; Bare Rock = 18%; Sand and Desert Rock = 7%. Remote sensing has proven a powerful "tool" for assessing the identity, characteristics, and growth potential of most kinds of vegetative matter at several levels (from biomes to individual plants). Vegetation behaviour depends on the nature of the vegetation itself, its interactions with solar radiation and other climate factors, and the availability of chemical nutrients and water within the host medium (usually soil, or water in marine environments). A common measure of the status of a given plant, such as a crop used for human consumption, is its potential productivity (one such parameter has units of bushels/acre or tons/hectare, or similar units). Productivity is sensitive to amounts of incoming solar radiation and precipitation (both influence the regional climate), soil chemistry, water retention factors, and plant type, keeping in mind that various remote sensing systems (e.g., meteorological or earth-observing satellites) can provide inputs to productivity estimation. Remote sensing can be broadly defined as the collection and interpretation of information about an object, area, or event without being in physical contact with the object. Aircraft and satellites are the common platforms for remote sensing of the earth and its natural resources. Aerial photography in the visible portion of the electromagnetic wavelength was the original form of remote sensing but technological developments has enabled the acquisition of information at other wavelengths including near infrared, thermal infrared and microwave. Collection of information over a large numbers of wavelength bands is referred to as multispectral or hyperspectral data. The development and deployment of manned and unmanned satellites has enhanced the collection of remotely sensed data and offers an inexpensive way to obtain information over large areas. The capacity of remote sensing to identify and monitor land surfaces and environmental conditions has expanded greatly over the last few years and remotely sensed data will be an essential tool in natural resource management.
Application of NOAA satellite data for agrometeorological purposes
Advances in Space Research, 1993
Satellite measurements of reflected and emitted radiation are an important element of Earth's surface and atmosphere investigations. Therefore the applications of multlchannel satellite observations, obtained from NOAA meteorological satellite for agrometeorological purposes has been discussed. The use of NOAAI (Advanced Very High Resolution Radlometer-AVHRR) for cloud detection and the relationship between upward radiation and cloud parameters was studied. Comparative study of SPOT and NOAA/AVHRR satellite images for agricultural purposes has been presented and the possibility for atmospheric correction of satellite data has been discussed. The methods and principles discussed herewith have found application in NOAA, SPOT and TM data processing for agrometeorological purposes.
Journal of Geophysical Research, 2006
Incident photosynthetically active radiation (PAR) is an important parameter for terrestrial ecosystem models. Because of its high temporal resolution, the Geostationary Operational Environmental Satellite (GOES) observations are very suited to catch the diurnal variation of PAR. In this paper, a new method is developed to derive PAR using GOES data. What makes this new method distinct from the existing method is that it does not need external knowledge of atmospheric conditions. The new method retrieves both atmospheric and surface conditions using only at-sensor radiance through interpolation of time series of observations. Validations against ground measurement are carried out at four "FLUXNET" sites. The values of RMSE of estimated and ground-measured instantaneous PAR at the four sites are 130. 71, 131.44, 141.16, and 190.22 mol m Ϫ2 s Ϫ1 , respectively. At the four validation sites, the RMSE as the percentage of estimated mean PAR value are 9.52%, 13.01%, 13.92%, and 24.09%, respectively; the biases are Ϫ101.54, 16.56, 11.09, and 53.64 mol m Ϫ2 s Ϫ1 , respectively. The independence of external atmospheric information enables this method to be applicable to many situations in which external atmospheric information is not available. In addition, topographic impacts on surface PAR are examined at the 1-km resolution at which PAR is retrieved using the GOES visible band data.