Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems.

Canopy and aerodynamic conductances ( g _C and g _A ) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration ( λ E _T ) and evaporation ( λ E _E ) flux components of the terrestrial latent heat flux ( λ E ), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature ( T _R ) into an integrated framework of the Penman–Monteith and Shuttleworth–Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on λ E _T and λ E _E over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a T _R -driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between g _C , λ E _T , and atmospheric vapor pressure deficit ( D _A ), without using any leaf-scale empirical parameterizations for the modeling. The T _R -based model shows minor biophysical control on λ E _T during the wet (rainy) seasons where λ E _T becomes predominantly radiation driven and net radiation ( R _N ) determines 75 to 80 % of the variances of λ E _T . However, biophysical control on λ E _T is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65 % of the variances of λ E _T , and indicates λ E _T to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in g _A between forests and pastures, very similar canopy–atmosphere "coupling" was found in these two biomes due to soil moisture-induced decrease in g _C in the pasture. This revealed the pragmatic aspect of the T _R -driven model behavior that exhibits a high sensitivity of g _C to per unit change in wetness as opposed to g _A that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between λ E _T and g _C during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by g _A for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of g _C and g _A to changes in atmospheric radiation, D _A , and surface radiometric temperature, and thus appears to be promising for the improvement of existing land–surface–atmosphere exchange parameterizations across a range of spatial scales.

Here we demonstrate a novel method to physically integrate radiometric surface temperature (T_R) into the Penman‐Monteith (PM) formulation for estimating the terrestrial sensible and latent heat fluxes (H and λE) in the framework of a modified Surface Temperature Initiated Closure (STIC). It combines T_R data with standard energy balance closure models for deriving a hybrid scheme that does not require parameterization of the surface (or stomatal) and aerodynamic conductances (g_S and g_B). STIC is formed by the simultaneous solution of four state equations and it uses T_R as an additional data source for retrieving the “near surface” moisture availability (M) and the Priestley‐Taylor coefficient (α). The performance of STIC is tested using high‐temporal resolution T_R observations collected from different international surface energy flux experiments in conjunction with corresponding net radiation (R_N), ground heat flux (G), air temperature (T_A), and relative humidity (R_H) measurements. A comparison of the STIC outputs with the eddy covariance measurements of λE and H revealed RMSDs of 7–16% and 40–74% in half‐hourly λE and H estimates. These statistics were 5–13% and 10–44% in daily λE and H. The errors and uncertainties in both surface fluxes are comparable to the models that typically use land surface parameterizations for determining the unobserved components (g_S and g_B) of the surface energy balance models. However, the scheme is simpler, has the capabilities for generating spatially explicit surface energy fluxes and independent of submodels for boundary layer developments.

Vegetation regulates the exchange of water, energy, and carbon fluxes between the land and the atmosphere. This regulation of surface fluxes differs with vegetation type and climate, but the effect of vegetation on surface fluxes is not well understood. A better knowledge of how and when vegetation influences surface fluxes could improve climate models and the extrapolation of ground-based water, energy, and carbon fluxes. We aim to study the link between vegetation and surface fluxes by combining the yearly average MODIS leaf area index (LAI) with flux tower measurements of water (latent heat), energy (sensible heat), and carbon (gross primary productivity and net ecosystem exchange). We show that the correlation of the LAI with water and energy fluxes depends on the vegetation type and aridity. Under water-limited conditions, the link between the LAI and the water and energy fluxes is strong, which is in line with a strong stomatal or vegetation control found in earlier studies. In energy-limited forest we found no link between the LAI and water and energy fluxes. In contrast to water and energy fluxes, we found a strong spatial correlation between the LAI and gross primary productivity that was independent of vegetation type and aridity. This study provides insight into the link between vegetation and surface fluxes. It indicates that for modelling or extrapolating surface fluxes, the LAI can be useful in savanna and grassland, but it is only of limited use in deciduous broadleaf forest and evergreen needleleaf forest to model variability in water and energy fluxes.

Thermal infrared sensing of evapotranspiration (E) through surface energy balance (SEB) models is challenging due to uncertainties in determining the aerodynamic conductance (g_A) and due to inequalities between radiometric (T_R) and aerodynamic temperatures (T₀). We evaluated a novel analytical model, the Surface Temperature Initiated Closure (STIC1.2), that physically integrates T_R observations into a combined Penman‐Monteith Shuttleworth‐Wallace (PM‐SW) framework for directly estimating E, and overcoming the uncertainties associated with T₀ and g_A determination. An evaluation of STIC1.2 against high temporal frequency SEB flux measurements across an aridity gradient in Australia revealed a systematic error of 10–52% in E from mesic to arid ecosystem, and low systematic error in sensible heat fluxes (H) (12–25%) in all ecosystems. Uncertainty in T_R versus moisture availability relationship, stationarity assumption in surface emissivity, and SEB closure corrections in E were predominantly responsible for systematic E errors in arid and semi‐arid ecosystems. A discrete correlation (r) of the model errors with observed soil moisture variance (r = 0.33–0.43), evaporative index (r = 0.77–0.90), and climatological dryness (r = 0.60–0.77) explained a strong association between ecohydrological extremes and T_R in determining the error structure of STIC1.2 predicted fluxes. Being independent of any leaf‐scale biophysical parameterization, the model might be an important value addition in working group (WG2) of the Australian Energy and Water Exchange (OzEWEX) research initiative which focuses on observations to evaluate and compare biophysical models of energy and water cycle components.

Land surface temperature (LST) is crucial for understanding surface energy budgets, hydrological cycling, and land–atmosphere interactions. However, cloud cover leads to numerous data gaps in existing remote sensing thermal infrared (TIR) LST products, seriously restricting their applications. This article provides a comprehensive review concerning both LST recovery methodologies and 26 emerging all-weather products derived from polar-orbiting and geostationary (GEO) satellites. Clarifying product distinctions will enable end users to select suitable options for diverse research. Methodologies are categorized into spatiotemporal interpolation, surface energy balance (SEB)-based physical estimation, passive microwave (PMW)-based methods, and simulated temperature-based approaches. Historical research trajectories, strengths, limitations, and potential research directions of the methodologies and products are discussed. The review reports that existing all-weather LST products generally exhibit root-mean-square errors (RMSEs) of <4 (2.5) K at instantaneous (daily mean) scales based on extensive ground measurements, comparable to clear sky retrievals. Deep learning (DL) models prominently feature in state-of-the-art interpolation and fusion approaches [e.g., long short-term memory (LSTM) and extreme gradient boosting (XGBoost)]. Product intercomparisons in various application scenarios reveal that interpolation-based products offer better texture details; however, noticeable biases exist compared to fusion-based products, especially in arid and semiarid regions, despite the high availability of clear sky samples. The bias shifts to negative at higher latitudes, due to ignored cloud radiative effects. The review emphasizes the underexplored recovery of diurnal temperature cycles (DTCs) from GEO satellites. This focus will benefit heat exposure monitoring for public health, understanding circadian rhythm responses of ecosystems to environmental changes, and harmonizing existing and forthcoming high-resolution TIR missions.