In this study, a novel 2D method for measuring soil surface suction, leveraging infrared thermal imaging technology is presented. The main principle of this method is the establishment of a correlation between soil surface water content and a normalized interfacial temperature difference. Subsequently, we link unsaturated soil surface suction to the normalized interfacial temperature difference through the soil-water characteristic curve. To validate the proposed method, an in-situ calibration test was conducted to ascertain the requisite parameters. Then, the method was tested under varying meteorological conditions at two distinct in-situ sites using the same test protocol as the calibration phase. The results demonstrate a strong agreement compared to measured values, affirming the feasibility and robustness of the proposed approach. This method offers several noteworthy advantages, including rapidity, non-contact operation, non-destructiveness, and robustness to environmental fluctuations. It holds promise for advancing investigation of the spatial and temporal evolution of hydro-mechanical properties of in-situ soil under the influence of climate change.

Understanding the distribution of plant moisture during the seedling stage of greenhouse crops is challenge in developing scientific irrigation strategies and proposing effective cooling methods. This study investigated the effects of different soil moisture contents [W1: 25-35 % (severe drought), W2: 35-45 % (mild drought), W3: 45-55 % (suitable moisture), and W4: 55-65 % (excess moisture)] on tomato seedlings under summer greenhouse thermic extremes. Furthermore, thermal infrared imaging and chlorophyll fluorescence multi-dimensional digital image sensors were used to determine differences in tomato seedling morphology and plant physiology. The increase in canopy area under W1 and W4 soil moisture content was smaller than that of W2 and W3, and the canopy area of the W1 group decreased as the high temperature condition continued. The average canopy temperature of each treatment generally increased first, and then plateaued. From high to low, average temperatures were 28.15 degrees C in W1, 27.73 degrees C in W4, 26.67 degrees C in W2, and 25.72 degrees C in W3. The canopy temperature gradually decreased from the middle to the edge of the leaf (stem temperature > leaf base temperature > leaf vein temperature > leaf edge temperature). The F-v/F-m ratio in the chlorophyll fluorescence index qualitatively expresses the degree of water stress. phi PSII, non-photochemical quenching (NPQ), and qP values were used as indicators to quantitatively analyze stress in leaves of different maturity. A preliminary mathematical relationship between the canopy and NPQ was established. This study quantitatively characterized the morphological and physiological changes of tomato seedlings in the greenhouse during summer, visualized the process of canopy temperature changes, and provided a theoretical basis for mitigating heat-induced damage.

TRISHNA is a thermal mission led in collaboration by CNES (Centre National d'Etudes Spatiales) and ISRO (Indian Space Research Organisation). It will collect optical and thermal datasets globally at a spatial resolution of 57m. The revisit period of 3 times per 8 days at low latitudes, with additional acquisitions towards the poles, and, suggests a high field of view (HFOV) around 35 degrees to fill mission objectives. This will infer angular effects on the time series of measurements. TRISHNA overpass is 12:30 local time, which is highly prone to capture the hot spot phenomena corresponding to a radiometric peak when the view and sun geometries coincide. Previous studies (Lacaze et al.,2000 [1]; Roujean, 2000 [2]; Duffour et al., 2016 [3]) showed that the maximum directional effect arises in the hot spot geometry, thus adding significance to anisotropy effects in LST (Land Surface Temperature) measurements beside HFOV. Hot spot features can lead to LST measurement errors up to 10K. This uncertainty propagates in the estimation of evapotranspiration, which is a significant deliverable of the TRISHNA mission. Therefore, paying attention to the observations that are impacted by the hot spot is mandatory, either for discarding them or to perform an angular correction. Such a decision depends on the width of the hot spot peak and its intensity. On the one hand, data located very near the hot spot geometry may not be processed as hotspot characteristics are susceptible to the architecture of the canopy, and such information will not be available globally. On the other hand, regarding the cloudiness in certain regions, clear data, even in the hot spot, should be harnessed. Therefore, acquiring knowledge through measurements and simulations is mandatory for preparing the TRISHNA mission to be launched in 2026 in the best conditions. Hitherto, several studies about the impact of the hot spot phenomenon on radiometry were broadly concentrated in the optical domain and inadequately in the thermal infrared (TIR) range. The reason for that is the difficulty to accumulate information for TIR over time as the TIR signal is ephemeral and depends on environmental factors (wind, soil wetness, atmospheric humidity) in addition to the structure of the medium, which is the only driver for optical. Directional effects, either thermal or optical, can be resumed by the BRDF (Bidirectional Reflectance Distribution Function) (Julien et al., 2023 [4]). The starting point is collecting directional measurements for various environmental conditions and canopy types. This sustained the implementation of the TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope) project.

High-latitude areas are very sensitive to global warming, which has significant impacts on soil temperatures and associated processes governing permafrost evolution. This study aims to improve first-layer soil temperature retrievals during winter. This key surface state variable is strongly affected by snow's geophysical properties and their associated uncertainties (e.g., thermal conductivity) in land surface climate models. We used infrared MODIS land-surface temperatures (LST) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) brightness temperatures (Tb) at 10.7 and 18.7 GHz to constrain the Canadian Land Surface Scheme (CLASS), driven by meteorological reanalysis data and coupled with a simple radiative transfer model. The Tb polarization ratio (horizontal/vertical) at 10.7 GHz was selected to improve snowpack density, which is linked to the thermal conductivity representation in the model. Referencing meteorological station soil temperature measurements, we validated the approach at four different sites in the North American tundra over a period of up to 8 years. Results show that the proposed method improves simulations of the soil temperature under snow (Tg) by 64% when using remote sensing (RS) data to constrain the model, compared to model outputs without satellite data information. The root mean square error (RMSE) between measured and simulated Tg under the snow ranges from 1.8 to 3.5 K when using RS data. Improved temporal monitoring of the soil thermal state, along with changes in snow properties, will improve our understanding of the various processes governing soil biological, hydrological, and permafrost evolution.

The distribution of shallow frozen ground is paramount to research in cold regions, and is subject to temporal and spatial changes influenced by climate, landscape disturbance and ecosystem succession. Remote sensing from airborne and satellite platforms is increasing our understanding of landscape-scale permafrost distribution, but typically lacks the resolution to characterise finer-scale processes and phenomena, which are better captured by integrated surface geophysical methods. Here, we demonstrate the use of electrical resistivity imaging (ERI), electromagnetic induction (EMI), ground penetrating radar (GPR) and infrared imaging over multiple summer field seasons around the highly dynamic Twelvemile Lake, Yukon Flats, central Alaska, USA. Twelvemile Lake has generally receded in the past 30yr, allowing permafrost aggradation in the receded margins, resulting in a mosaic of transient frozen ground adjacent to thick, older permafrost outside the original lakebed. ERI and EMI best evaluated the thickness of shallow, thin permafrost aggradation, which was not clear from frost probing or GPR surveys. GPR most precisely estimated the depth of the active layer, which forward electrical resistivity modelling indicated to be a difficult target for electrical methods, but could be more tractable in time-lapse mode. Infrared imaging of freshly dug soil pit walls captured active-layer thermal gradients at unprecedented resolution, which may be useful in calibrating emerging numerical models. GPR and EMI were able to cover landscape scales (several kilometres) efficiently, and new analysis software showcased here yields calibrated EMI data that reveal the complicated distribution of shallow permafrost in a transitional landscape. Copyright (c) 2016 John Wiley & Sons, Ltd.