Remotely sensing plant canopy water status remains a long term goal of remote sensing research.
Established approaches to estimating canopy water status — the Crop Water Stress Index, the Water
Deficit Index and the Equivalent Water Thickness — involve measurements in the thermal or reflective
infrared. Here we report plant water status estimates based upon analysis of polarized visible imagery of a
cotton canopy measured by ground Multi-Spectral Polarization Imager (MSPI). Such estimators potentially
provide access to the plant hydrological photochemistry that manifests scattering and absorption effects in
the visible spectral region.
High-Throughput Phenotyping (HTP) is a discipline for rapidly identifying plant architectural and physiological responses to environmental factors such as heat and water stress. Experiments conducted since 2010 at Maricopa, Arizona with a three-fold sensor group, including thermal infrared radiometers, active visible/near infrared reflectance sensors, and acoustic plant height sensors, have shown the validity of HTP with a tractor-based system. However, results from these experiments also show that accuracy of plant phenotyping is limited by the system’s inability to discriminate plant components and their local environmental conditions. This limitation may be overcome with plant imaging and laser scanning which can help map details in plant architecture and sunlit/shaded leaves. To test the capability for mapping cotton plants with a laser system, a track-mounted platform was deployed in 2015 over a full canopy and defoliated cotton crop consisting of a scanning LIDAR driven by Arduinocontrolled stepper motors. Using custom Python and Tkinter code, the platform moved autonomously along a pipe-track at 0.1 m/s while collecting LIDAR scans at 25 Hz (0.1667 deg. beam). These tests showed that an autonomous LIDAR platform can reduce HTP logistical problems and provide the capability to accurately map cotton plants and cotton bolls.
A prototype track-mounted platform was developed to test the use of LIDAR scanning for High- Throughput Phenotyping (HTP). The platform was deployed in 2015 at Maricopa, Arizona over a senescent cotton crop. Using custom Python and Tkinter code, the platform moved autonomously along a pipe-track at <1 m/s while collecting LIDAR scans at 25 Hz (0.1667 deg. beam). Scanning data mapped the canopy heights and widths, and detected cotton bolls.
Accurate, spatially distributed surface temperatures are required for modeling evapotranspiration (ET) over agricultural fields under wide ranging conditions, including stressed and unstressed vegetation. Modeling approaches that use surface temperature observations, however, have the burden of estimating surface emissivities. Emissivity estimation, the subject of much recent research, is facilitated by observations in multiple thermal infrared bands. But it is nevertheless a difficult task. Using observations from multiband thermal sensors, ASTER and MASTER, estimated surface emissivities and temperatures are retrieved in two different ways: the temperature emissivity separation approach (TES), and the normalized emissivity approach (NEM). Both rely upon empirical relationships, but the assumed relationships are different. TES relies upon a relationship between the minimum spectral emissivity and the range of observed emissivities. NEM relies upon an assumption that at least one thermal band has a predetermined emissivity (close to 1.0). Experiments comparing TES and NEM were performed using simulated observations from spectral library data, and with actual data from two different landscapes-- one in central Oklahoma, USA, and another in southern New Mexico, USA. The simulation results suggest that TES's empirical relationship is more realistic than NEM's assumed maximum emissivity, and therefore TES temperature estimates are more accurate than NEM estimates. But when using remote sensing data, TES estimates of maximum emissivities are lower than expected, thus causing overestimated temperatures. Work in progress will determine the significance of this overestimation by comparing ground level measurements against the remote sensing observations.
The Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER) has acquired more than a dozen clear sky scenes over the Jornada Experimental Range in New Mexico since the launch of NASA's Terra satellite in December, 1999. To support the ASTER overpasses there were simultaneous field campaigns for the 5/09/00, 5/12/01, 9/17/01 and 5/15/02 scenes. Also, data from an airborne simulator, MASTER, were obtained for the 5/12/01 and 5/15/02 scenes to provide high resolution (3 m) data roughly coincident with ASTER. The Jornada Experimental Range is a long term ecological reserve (LTER) site located at the northern end of the Chihuahuan desert. The site is typical of a desert grassland where the main vegetation components are grass and shrubs. The White Sands National Monument is also within several of the scenes. ASTER has 5 channels in the 8 to 12 micrometer wave band with 90 meter resolution and thus is able to provide information on both the surface temperature and emissivity. The Temperature Emissivity Separation (TES) algorithm was used to extract emissivity values from the ASTER data for 5 sites on the Jornada and for the gypsum sand at White Sands. The results are in good agreement with values calculated from the lab spectra for gypsum and with each other. The results for sites in the Jornada show reasonable agreement with the lab results when the mixed pixel problem is taken into account. These results indicate ASTER and TES are working very well. The surface brightness temperatures from ASTER were in reasonable agreement with measurements made on the ground during the field campaigns.
The recent availability of multi-band thermal infrared imagery from the Advanced Spaceborne Thermal Emission & Reflection radiometer (ASTER) on NASA's Terra satellite has made feasible the estimation of evapotranspiration at 90 meter resolution. One critical variable in evapotranspiration models is surface temperature. With ASTER the temperature can be reliably determined over a wide range of vegetative conditions. The requirements for accurate temperature measurement include minimization of atmospheric effects, correction for surface emissivity variations and sufficient resolution for the type of vegetative cover. When ASTER imagery are combined with meteorological observations, these requirements are usually met and result in surface temperatures accurate within 1-2 C. ASTER-based evapotranspiration estimates were made during September 2000 over a sub-humid regions at the USDA/ARS Grazinglands research laboratory near El Reno in central Oklahoma. Daily evapotranspiration was estimated by applying instantaneous ASTER surface temperatures, as well as ASTER-based vegetation indices from visible-near infrared bands, to a two-source energy flux model and combining the result with separately acquired hourly solar radiation data. The estimates of surface fluxes show reasonable agreement (within 50-100 W/m2) with ground-based Bowen Ratio Energy Balance measurements and illustrate how ASTER measurements can be applied to heterogeneous terrain. There are some significant discrepancies, however, and these may in part be due to difficulty quantifying fractional cover of senescent vegetation.
On several days in 2000 & 2001 the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) on the Terra satellite obtained data over the Jornada Experimental Range test site along the Rio Grande river and the White Sand National Monument in New Mexico. ASTER has 14 channels from the visible (VNIR) through the thermal infrared (TIR) with 15 m resolution in the VNIR and 90 m in the TIR. The overpass time is approximately 11 AM (MST). With 5 channels between 8 and 12 micrometers these multispectral TIR data from ASTER provide the opportunity to separate the temperature and emissivity effects observed in the thermal emission from the land surface. Ground measurements during these overflights included surface temperature, vegetation type and condition and limited surface emissivity measurements. Preliminary results indicate good agreement between ASTER emissivities and ground measures. Analysis of earlier aircraft data has shown that the multispectral TIR data are very effective for estimating both the surface temperature and emissivity. These results will be compared with those obtained from the ASTER data for this site. With multispectral thermal infrared observations provided by ASTER it is possible for the first time to estimate the spectral emissivity variation for these surfaces on a global basis at high spatial resolution.