Reflectance of vegetation, soil, and water

There are no author-identified significant results in this report

Irrigation scheduling, freeze warning and soil salinity detecting

There are no author-identified significant results in this report

Comparisons among a new soil index and other two- and four-dimensional vegetation indices

The 2-D difference vegetation index (DVI) and perpendicular vegetation index (PVI), and the 4-D green vegetation index (GVI) are compared in LANDSAT MSS data from grain sorghum (Sorghum bicolor, L. Moench) fields for the years 1973 to 1977. PVI and DVI were more closely related to LAI than was GVI. A new 2-D soil line index (SLI), the vector distance from the soil line origin to the point of intersection of PVI with the soil line, is defined and compared with the 4-D soil brightness index, SBI. SLI (based on MSS and MSS7) and SL16 (based on MSS 5 and MSS 6) were smaller in magnitude than SBI but contained similar information about the soil background. These findings indicate that vegetation and soil indices calculated from the single visible and reflective infrared band sensor systems, such as the AVHRR of the TIROS-N polar orbiting series of satellites, will be meaningful for synoptic monitoring of renewable vegetation

Spectral reflectance from plant canopies and optimum spectral channels in the near infrared

Theoretical and experimental aspects of the interaction of light with a typical plant canopy are considered. Both theoretical and experimental results are used to establish optimum electromagnetic wavelength channels for remote sensing in agriculture. The spectral range considered includes half of the visible and much of the near-infrared regions

Reflectance of vegetation, soil, and water

The author has identified the following significant results. The Kubelka-Munk model, a regression model, and a combination of these models were used to extract plant, soil, and shadow reflectance components of vegetated surfaces. The combination model was superior to the others; it explained 86% of the variation in band 5 reflectance of corn and sorghum, and 90% of the variation in band 6 reflectance of cotton. A fractional shadow term substantially increased the proportion of the digital count sum of squares explained when plant parameters alone explained 85% or less of the variation. Overall recognition of 94 agricultural fields using simultaneously acquired aircraft and spacecraft MSS data was 61.8 and 62.8%, respectively; recognition of vegetable fields larger than 10 acres and taller than 25 cm, rose to 88.9 and 100% for aircraft and spacecraft, respectively. Agriculture and rangeland, were well discriminated for the entire county but level 2 categories of vegetables, citrus, and idle cropland, except for citrus, were not

Methods of editing cloud and atmospheric layer affected pixels from satellite data

Subvisible cirrus clouds (SCi) were easily distinguished in mid-infrared (MIR) TIROS-N daytime data from south Texas and northeast Mexico. The MIR (3.55-3.93 micrometer) pixel digital count means of the SCi affected areas were more than 3.5 standard deviations on the cold side of the scene means. (These standard deviations were made free of the effects of unusual instrument error by factoring out the Ch 3 MIR noise on the basis of detailed examination of noisy and noise-free pixels). SCi affected areas in the IR Ch 4 (10.5-11.5 micrometer) appeared cooler than the general scene, but were not as prominent as in Ch 3, being less than 2 standard deviations from the scene mean. Ch 3 and 4 standard deviations and coefficients of variation are not reliable indicators, by themselves, of the presence of SCi because land features can have similar statistical properties

Soil, Water, and Vegetation Conditions in South Texas

The author has identified the following significant results. Reflectance differences between the dead leaves of six crops (corn, cotton, sorghum, sugar cane, citrus, and avocado) and the respective bare soils where the dead leaves were lying on the ground were determined from laboratory spectrophotometric measurements over the 0.5- to 2.5 micron wavelength interval. The largest differences were in the near infrared waveband 0.75- to 1.35 microns. Leaf area index was predicted from plant height, percent ground cover, and plant population for irrigated and nonirrigated grain sorghum fields for the 1975 growing season

Vegetation density as deduced from ERTS-1 MSS response

Reflectance from vegetation increases with increasing vegetation density in the 0.75- to 1.35 micron wavelength interval. Therefore, ERTS-1 bands 6 (0.7 to 0.8 micron) and 7 (0.8 to 1.1 micron) contain information that should relate to the probable yield of crops and the animal carrying capacity of rangeland. The results of an experiment designed specifically to test the relations among leaf area index (LAI), plant population, plant cover and plant height, and the ERTS-1 MSS responses for 3 corn, 10 sorghum, and 10 cotton fields are given. Plant population was as useful as LAI for characterizing the sorghum and corn fields, and plant height was as good as LAI for characterizing cotton fields. These findings generally support the utility of ERTS-1 data for explaining variability in green biomass, harvestable forage and other indicators of productivity

Methods of editing cloud and atmospheric layer affected pixels from satellite data

The location and migration of cloud, land and water features were examined in spectral space (reflective VIS vs. emissive IR). Daytime HCMM data showed two distinct types of cloud affected pixels in the south Texas test area. High altitude cirrus and/or cirrostratus and "subvisible cirrus" (SCi) reflected the same or only slightly more than land features. In the emissive band, the digital counts ranged from 1 to over 75 and overlapped land features. Pixels consisting of cumulus clouds, or of mixed cumulus and landscape, clustered in a different area of spectral space than the high altitude cloud pixels. Cumulus affected pixels were more reflective than land and water pixels. In August the high altitude clouds and SCi were more emissive than similar clouds were in July. Four-channel TIROS-N data were examined with the objective of developing a multispectral screening technique for removing SCi contaminated data

Reflectance of vegetation, soil, and water

The author has identified the following significant results. Iron deficient and normal grain sorghum plants were sufficiently different spectrally in ERTS-1 band 5 CCT data to detect chlorotic sorghum areas 2.8 acres (1.1 hectares) or larger in size in computer printouts of the MSS data. The ratio of band 5 to band 7 or band 7 minus band 5 relates to vegetation ground cover conditions and helps to select training samples representative of differing vegetation maturity or vigor classes and to estimate ground cover or green vegetation density in the absence of ground information. The four plant parameters; leaf area index, plant population, plant cover, and plant height explained 87 to 93% of the variability in band 6 digital counts and from 59 to 90% of the variation in bands 4 and 5. A ground area 2244 acres in size was classified on a pixel by pixel basis using simultaneously acquired aircraft support and ERTS-1 data. Overall recognition for vegetables, immature crops and mixed shrubs, and bare soil categories was 64.5% for aircraft and 59.6% for spacecraft data, respectively. Overall recognition results on a per field basis were 61.8% for aircraft and 62.8% for ERTS-1 data