A Crops and Soils Data Base for Scene Radiation Research (original) (raw)
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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 ~
Useful surrogates of soil texture for plant ecologists from airborne gamma-ray detection
Ecology and evolution, 2018
Plant ecologists require spatial information on functional soil properties but are often faced with soil classifications that are not directly interpretable or useful for statistical models. Sand and clay content are important soil properties because they indicate soil water-holding capacity and nutrient content, yet these data are not available for much of the landscape. Remotely sensed soil radiometric data offer promise for developing statistical models of functional soil properties applicable over large areas. Here, we build models linking radiometric data for an area of 40,000 kmwith soil physicochemical data collected over a period of 30 years and demonstrate a strong relationship between gamma radiometric potassium (K), thorium (²³²Th), and soil sand and clay content. Our models showed predictive performance of 43% with internal cross-validation (to held-out data) and ~30% for external validation to an independent test dataset. This work contributes to broader availability an...
Spaceborne Imaging Spectroscopy for Sustainable Agriculture: Contributions and Challenges
Surveys in Geophysics
Agriculture faces the challenge of providing food, fibre and energy from limited land resources to satisfy the changing needs of a growing world population. Global megatrends, e.g., climate change, influence environmental production factors; production and consumption thus must be continuously adjusted to maintain the producer-consumer-equilibrium in the global food system. While, in some parts of the world, smallholder farming still is the dominant form of agricultural production, the use of digital information for the highly efficient cultivation of large areas has become part of agricultural practice in developed countries. Thereby, the use of satellite data to support site-specific management is a major trend. Although the most prominent use of satellite technology in farming still is navigation, Earth Observation is increasingly applied. Some operational services have been established, which provide farmers with decision-supporting spatial information. These services have mostly been boosted by the increased availability of multispectral imagery from NASA and ESA, such as the Landsat or Copernicus programs, respectively. Using multispectral data has arrived in the agricultural commodity chain. Compared to multispectral data, spectrally continuous narrow-band sampling, often referred to as hyperspectral sensing, can potentially provide additional information and/or increased sampling accuracy. However, due to the lack of hyperspectral satellite systems with high spatial resolution, these advantages mostly are not yet used in practical farming. This paper summarizes where hyperspectral data provide additional value and information in an agricultural context. It lists the variables of interest and highlights the contribution of hyperspectral sensing for information-driven agriculture, preparing the application of future operational spaceborne hyperspectral missions.