In California, water is a perennial concern. As competition for water resources increases due to growth in population, California’s tree nut farmers are committed to improving the efficiency of water used for food production. There is an imminent need to have reliable methods that provide information about the temporal and spatial variability of crop water requirements, which allow farmers to make irrigation decisions at field scale. This study focuses on estimating the actual evapotranspiration and crop coefficients of an almond and pistachio orchard located in Central Valley (California) during an entire growing season by combining a simple crop evapotranspiration model with remote sensing data. A dataset of the vegetation index NDVI derived from Landsat-8 was used to facilitate the estimation of the basal crop coefficient (K_cb), or potential crop water use. The soil water evaporation coefficient (K_e) was measured from microlysimeters. The water stress coefficient (K_s) was derived from airborne remotely sensed canopy thermal-based methods, using seasonal regressions between the crop water stress index (CWSI) and stem water potential (Ψ_stem). These regressions were statistically-significant for both crops, indicating clear seasonal differences in pistachios, but not in almonds. In almonds, the estimated maximum K_cb values ranged between 1.05 to 0.90, while for pistachios, it ranged between 0.89 to 0.80. The model indicated a difference of 97 mm in transpiration over the season between both crops. Soil evaporation accounted for an average of 16% and 13% of the total actual evapotranspiration for almonds and pistachios, respectively. Verification of the model-based daily crop evapotranspiration estimates was done using eddy-covariance and surface renewal data collected in the same orchards, yielding an R² ≥ 0.7 and average root mean square errors (RMSE) of 0.74 and 0.91 mm·day⁻¹ for almond and pistachio, respectively. It is concluded that the combination of crop evapotranspiration models with remotely-sensed data is helpful for upscaling irrigation information from plant to field scale and thus may be used by farmers for making day-to-day irrigation management decisions.

Leaf pigment contents, such as chlorophylls a and b content (C ${}_{ab}$ ) or carotenoid content (Car), and the leaf area index (LAI) are recognized indicators of plants’ and forests’ health status that can be estimated through hyperspectral imagery. Their measurement on a seasonal and yearly basis is critical to monitor plant response and adaptation to stress, such as droughts. While extensively done over dense canopies, estimation of these variables over tree-grass ecosystems with very low overstory LAI (mean site LAI < 1 m ${}^2$ /m ${}^2$ ), such as woodland savannas, is lacking. We investigated the use of look-up table (LUT)-based inversion of a radiative transfer model to retrieve LAI and leaf C ${}_{ab}$ and Car from AVIRIS images at an 18 m spatial resolution at multiple dates over a broadleaved woodland savanna during the California drought. We compared the performances of different cost functions in the inversion step. We demonstrated the spatial consistency of our LAI, C ${}_{ab}$ , and Car estimations using validation data from low and high canopy cover parts of the site, and their temporal consistency by qualitatively confronting their variations over two years with those that would be expected. We concluded that LUT-based inversions of medium-resolution hyperspectral images, achieved with a simple geometric representation of the canopy within a 3D radiative transfer model (RTM), are a valid means of monitoring woodland savannas and more generally sparse forests, although for maximum applicability, the inversion cost functions should be selected using validation data from multiple dates. Validation revealed that for monitoring use: The normalized difference vegetation index (NDVI) outperformed other indices for LAI estimations (root mean square error (RMSE) = 0.22 m ${}^2$ /m ${}^2$ , $R^2$ = 0.81); the band ratio $\frac{{\rho }_{0.750\mathsf{\mu }\mathrm{m}}}{{\rho }_{0.550\mathsf{\mu }\mathrm{m}}}$ retrieved C ${}_{ab}$ more accurately than other chlorophylls indices (RMSE = 5.21 $\mathsf{\mu }$ g/cm ${}^2$ , $R^2$ = 0.73); RMSE over the 0.5–0.55 $\mathsf{\mu }$ m interval showed encouraging results for Car estimations.

This article presents a novel methodology for the characterization of tree vegetation phenology, based on vegetation indices time series reconstruction and adapted to urban areas. The methodology is based on a pixel by pixel curve fitting classification, together with a subsequent Savitzky–Golay filtering of raw phenological curves from pixels classified as vegetation. Moreover, the new method is conceived to face specificities of urban environments such as: the high heterogeneity of impervious/natural elements, the 3D structure of the city inducing shadows, the restricted spatial extent of individual tree crowns and the strong biodiversity of urban vegetation. Three vegetation indices have been studied: Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index 1 (NDRE1), which are mainly linked to chlorophyll content and leaf density and Normalized Burn Ratio (NBR) mostly correlated to water content and leaf density. The methodology has been designed to allow the analysis of annual and intra-annual vegetation phenological dynamics. Then, different annual and intra-annual criteria for phenology characterization are proposed and criticized. To show the applicability of the methodology, this article focuses on Sentinel-2 (S-2) imagery covering 2018 and the study of groups of London planes in an alignment structure in the French city of Toulouse. Results showed that the new method allows the ability to 1) describe the heterogeneity of phenologies from London planes exposed to different environmental conditions (urban canyons, proximity with a source of water) and 2) to detect intra-annual phenological dynamics linked to changes in meteorological conditions.

Clay minerals play an important role in shrinking–swelling of soils and off–road vehicle mobility mainly due to the presence of smectites including montmorillonites. Since soils are composed of different minerals intimately mixed, an accurate estimation of its abundance is challenging. Imaging spectroscopy in the short wave infrared spectral region (SWIR) combined with unmixing methods is a good candidate to estimate clay mineral abundance. However, the performance of unmixing methods is mineral-dependent and may be enhanced by using appropriate spectral preprocessings. The objective of this paper is to carry out a comparative study in order to determine the best couple spectral preprocessing/unmixing method to quantify montmorillonite in intimate mixtures with clays, such as montmorillonite, kaolinite and illite, and no-clay minerals, such as calcite and quartz. To this end, a spectral database is built with laboratory hyperspectral imagery from 51 dry pure mineral samples and intimate mineral mixtures of controlled abundances. Six spectral preprocessings, standard normal variate (SNV), continuum removal (CR), continuous wavelet transform (CWT), Hapke model, first derivative (1st SGD) and pseudo–absorbance (Log(1/R)), are applied and compared with reflectance spectra. Two linear unmixing methods, fully constrained least square method (FCLS) and multiple endmember spectral mixture analysis (MESMA), and two non-linear unmixing methods, generalized bilinear method (GBM) and multi-linear model (MLM), are compared. Global results showed that the benefit of spectral preprocessings occurs when spectral absorption features of minerals overlap for SNV, CR, CWT and 1st SGD, whereas the use of reflectance spectra performs the best when no overlap is present. With one mineral having no spectral feature (quartz), montmorillonite abundance estimation is difficult and gives RMSE higher than 50%. For the other mixtures, performances of linear and non-linear unmixing methods are similar. Consequently, the recommended couple spectral preprocessing/unmixing method based on the trade-off between its simplicity and performance is 1st SGD/FCLS for clay binary and ternary mixtures (RMSE of 9.2% for montmorillonite–illite mixtures, 13.9% for montmorillonite–kaolinite mixtures and 10.8% for montmorillonite–illite–kaolinite mixtures) and reflectance/FCLS for binary mixtures with calcite (RMSE of 8.8% for montmorillonite–calcite mixtures). These performances open the way to improve the classification of expansive soils.

The CAMCATT-AI4GEO extensive field experiment took place in Toulouse, a city in the southwest of France, from 14^th to 25^th June 2021 (with complementary measurements performed on the 6 September 2021). Its main objective was the acquisition of a new reference dataset on an urban site to support the development and validation of data products from the future thermal infrared (TIR) satellite missions such as TRISHNA (CNES/ISRO), LSTM (ESA) and SBG (NASA). With their high spatial (between 30-60m) and temporal (2-3 days) resolutions, the future TIR satellite data will allow a better investigation of the urban climate at the neighbourhood scale. However, in order to validate the future products of these missions such as LST, air temperature, comfort index and Urban Heat Island (UHI), there is a need to accurately characterise the organisation of the city in terms of 3D geometry, spectral optical properties and both land surface temperature and emissivity (LST and LSE) at several scales. In this context, the CAMCATT-AI4GEO field campaign provides a set of airborne VISNIR-SWIR (Visible Near InfraRed - ShortWave InfraRed) hyperspectral imagery, multispectral thermal infrared (TIR) imagery and 3D LiDAR acquisitions, together with a variety of ground data collected, for some of them, simultaneously to the flight. The ground dataset includes surface reflectance measured spectrally with ASD spectroradiometers and in six spectral bands spreading from shortwave to thermal infrared and for two viewing angles with a SOC410-DHR handheld reflectometer. It is completed with LST and LSE retrieved from thermal infrared radiance acquired in six spectral bands with CIMEL radiometers. It also includes meteorological data coming from four radio soundings (one of which was taken during the flight), data routinely collected at the Blagnac airport reference station as well as air temperature and humidity acquired using instrumented cars following two different itineraries. In addition, a link is provided to access the data routinely collected by the network of weather stations set up by Toulouse Metropole in the city and its surroundings. This data paper describes this new reference urban dataset which can be useful for many applications such as calibration/validation of at-surface radiance, LST and LSE data products as well as higher level products such as air temperature or comfort index. It also provides valuable opportunities for other applications in urban climate studies, such as supporting the validation of microclimate models.