We present a comprehensive model of martian pressure-temperature (P-T) phase space and compare it with that of Earth. Martian P-T conditions compatible with liquid water extend to a depth of ∼310 km. We use our phase space model of Mars and of terrestrial life to estimate the depths and extent of the water on Mars that is habitable for terrestrial life. We find an extensive overlap between inhabited terrestrial phase space and martian phase space. The lower martian surface temperatures and shallower martian geotherm suggest that, if there is a hot deep biosphere on Mars, it could extend 7 times deeper than the ∼5 km depth of the hot deep terrestrial biosphere in the crust inhabited by hyperthermophilic chemolithotrophs. This corresponds to ∼3.2% of the volume of present-day Mars being potentially habitable for terrestrial-like life. Key Words: Biosphere—Mars—Limits of life—Extremophiles—Water. Astrobiology 11, 1017–1033.

Terrestrial life is known to require liquid water, but not all terrestrial water is inhabited. Thus, liquid water is a necessary, but not sufficient, condition for life. To quantify the terrestrial limits on the habitability of water and help identify the factors that make some terrestrial water uninhabited, we present empirical pressure-temperature (P-T) phase diagrams of water, Earth, and terrestrial life. Eighty-eight percent of the volume of Earth where liquid water exists is not known to host life. This potentially uninhabited terrestrial liquid water includes (i) hot and deep regions of Earth where some combination of high temperature (T > 122 degrees C) and restrictions on pore space, nutrients, and energy is the limiting factor and (ii) cold and near-surface regions of Earth, such as brine inclusions and thin films in ice and permafrost (depths less than approximately 1 km), where low temperatures (T < -40 degrees C), low water activity (a(w) < 0.6), or both are the limiting factors. If the known limits of terrestrial life do not change significantly, these limits represent important constraints on our biosphere and, potentially, on others, since approximately 4 billion years of evolution have not allowed life to adapt to a large fraction of the volume of Earth where liquid water exists.

Precision viticulture benefits from the accurate detection of vineyard vegetation from remote sensing, without a priori knowledge of vine locations. Vineyard detection enables efficient, and potentially automated, derivation of spatial measures such as length and area of crop, and hence required volumes of water, fertilizer, and other resources. Machine learning techniques have provided significant advancements in recent years in the areas of image segmentation, classification, and object detection, with neural networks shown to perform well in the detection of vineyards and other crops. However, what has not been extensively quantitatively examined is the extent to which the initial choice of input imagery impacts detection/segmentation accuracy. Here, we use a standard deep convolutional neural network (CNN) to detect and segment vineyards across Australia using DigitalGlobe Worldview-2 images at ∼50 cm (panchromatic) and ∼2 m (multispectral) spatial resolution. A quantitative assessment of the variation in model performance with input parameters during model training is presented from a remote sensing perspective, with combinations of panchromatic, multispectral, pan-sharpened multispectral, and the spectral Normalised Difference Vegetation Index (NDVI) considered. The impact of image acquisition parameters—namely, the off-nadir angle and solar elevation angle—on the quality of pan-sharpening is also assessed. The results are synthesised into a ‘recipe’ for optimising the accuracy of vineyard segmentation, which can provide a guide to others aiming to implement or improve automated crop detection and classification.

A Tropical Peatland Combustion Algorithm (ToPeCAl) was first established from Landsat-8 images acquired in 2015, which were used to detect peatland combustion in flaming and smouldering stages. Detection of smouldering combustion from space remains a challenge due to its low temperature and generally small spatial extent. The ToPeCAl consists of the Shortwave Infrared Combustion Index based on reflectance (SICI_ρ), and Top of Atmosphere (TOA) reflectance in Shortwave Infrared band-7 (SWIR-2), TOA brightness temperature of Thermal Infrared band-10 (TIR-1), and TOA reflectance of band-1, the Landsat-8 aerosol band. The implementation of ToPeCAl was then validated using terrestrial and aerial images (helicopter and drone) collected during fieldwork in Central Kalimantan, Indonesia in the 2018 fire season, on the same day as Landsat-8 overpasses. The overall accuracy of ToPeCAl was found to be 82% with omission errors in a small area (less than 30 m × 30 m) from mixtures of smouldering and vegetation pixels, and commission errors (with minimum area of 30 m x 30 m) on high reflective building rooftops in urban areas. These errors were further reduced by masking and removing urban areas prior to analysis using landuse Geographic Information System (GIS) data; improving the overall mapping accuracy to 93%. For comparison, the day and night-time VIIRS (375 m) active fire product (VNP14IMG) was utilised, obtaining a lower probability of fire detection of 71% compared to ground truth, and 57–72% agreement in a buffer distance of 375 m to 1500 m when compared to the Landsat-8 ToPeCAl results. The night-time data of VNP14IMG was found to have a better correspondence with ToPeCAl results from Landsat 8 than day-time data. This finding could lead to a potential merger of ToPeCAl with VNP14IMG to fill the temporal gaps of peatland fire information when using Landsat. However, the VNP14IMG product exhibited overestimation compared with the results of ToPeCAl applied to Landsat-8.

This article presents our research in the prelaunch phase of the Kanyini mission, which aims to implement an energy-efficient, AI-based system onboard for early fire smoke detection using hyperspectral imagery. Our approach includes three key components: developing a diverse hyperspectral training dataset from VIIRS imagery, groundwork in band selection and AI model preparation, and developing an emulation system. We adapted and evaluated our previously developed lightweight convolutional neural network model, VIB_SD, to meet the computational constraints of satellite deployment. The emulation system tests various onboard AI tasks and processes. Our comprehensive experiments demonstrate the feasibility and benefits of employing onboard AI for fire smoke detection, significantly improving downlink efficiency, energy consumption, and detection speed.