A deep learning approach for automatic identification of ancient agricultural water harvesting systems.

Despite the harsh climatic conditions in the Central Negev Desert, Israel, thousands of dry stonewalls were built across ephemeral streams between the fourth and seventh centuries CE to sustain productive agricultural activity. Since 640 CE, many of these ancient terraces have remained untouched but buried by sediments, covered by natural vegetation, and partially destroyed. The main goal of the current research is to develop a procedure for the automatic recognition of ancient water harvesting systems by incorporating two remote sensing datasets (a high-resolution color orthophoto and LiDAR-derived topographic variables) and two advanced processing methods (an object-based image analysis (OBIA) and a deep convolutional neural networks (DCNN) model).

Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation.

Remote sensing estimation of evapotranspiration (ET) directly quantifies plant water consumption and provides essential information for irrigation scheduling, which is a pressing need for California vineyards as extreme droughts become more frequent. Many ET models take satellite-derived Leaf Area Index (LAI) as a major input, but how uncertainties of LAI estimations propagate to ET and the partitioning between evaporation and transpiration is poorly understood. Here we assessed six satellite-based LAI estimation approaches using Landsat and Sentinel-2 images against ground measurements from four vineyards in California and evaluated ET sensitivity to LAI in the thermal-based two-source energy balance (TSEB) model.

A novel approach for long-term spectral monitoring of desert shrubs affected by an oil spill.

Crude oil pollution is a global environmental concern since it persists in the environment longer than most conventional carbon sources. In December 2014, the hyper-arid Evrona Nature Reserve, Israel, experienced large-scale contamination when crude oil spilled. The overarching goal of the study was to investigate the possible changes, caused by an accidental crude oil spill, in the leaf reflectance and biochemical composition of four natural habitat desert shrubs.

A long-term spatiotemporal analysis of biocrusts across a diverse arid environment: The case of the Israeli-Egyptian sandfield.

Spatiotemporal data can be analyzed using spatial, time-series, and machine learning algorithms to extract regional biocrust trends. Analyzing the spatial trends of biocrusts through time, using satellite imagery, may improve the quantification and understanding of their change drivers. The current work strives to develop a unique framework for analyzing spatiotemporal trends of the spectral Crust Index (CI), thus identifying the drivers of the biocrusts' spatial and temporal patterns.

Estimating Daily PM and PM over Italy Using an Ensemble Model.

Spatiotemporally resolved particulate matter (PM) estimates are essential for reconstructing long and short-term exposures in epidemiological research. Improved estimates of PM and PM concentrations were produced over Italy for 2013-2015 using satellite remote-sensing data and an ensemble modeling approach. The following modeling stages were used: (1) missing values of the satellite-based aerosol optical depth (AOD) product were imputed using a spatiotemporal land-use random-forest (RF) model incorporating AOD data from atmospheric ensemble models; (2) daily PM estimations were produced using four modeling approaches: linear mixed effects, RF, extreme gradient boosting, and a chemical transport model, the flexible air quality regional model.

Source-sink relations of sunflower plants as affected by a parasite modifies carbon allocations and leaf traits.

Sunflower broomrape (Orobanche cumana) is a root holoparasitic plant causing major damage to sunflower (Helianthus annuus L.). Parasite infection initiates source-sink relations between the parasite (sink) and the host (source), allocating carbohydrates, water and nutrients to the parasite.

The potential of the spectral 'water balance index' (WABI) for crop irrigation scheduling.

Hyperspectral sensing can detect slight changes in plant physiology, and may offer a faster and nondestructive alternative for water status monitoring. This premise was tested in the current study using a narrow-band 'water balance index' (WABI), which is based on independent changes in leaf water content (1500 nm) and the efficiency of the nonphotochemical quenching (NPQ) photo-protective mechanism (531 nm). The hydraulic, photo-protective and spectral behaviors of five important crops - grapevine, corn, tomato, pea and sunflower - were evaluated under water deficit conditions in order to associate the differences in stress physiology with WABI suitability.

Recognition of Below-Ground Parasitism Through Physiological and Hyper Spectral Measurements in Sunflower ( L.).

Broomrape ( and spp.) parasitism is a severe problem in many crops worldwide, including in the Mediterranean basin. Most of the damage occurs during the sub-soil developmental stage of the parasite, by the time the parasite emerges from the ground, damage to the crop has already been done.

Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in (Palmer Amaranth).

Weed infestations in agricultural systems constitute a serious challenge to agricultural sustainability and food security worldwide. S. Watson (Palmer amaranth) is one of the most noxious weeds causing significant yield reductions in various crops.

Multiscale mapping of species diversity under changed land use using imaging spectroscopy.

Land use changes are one of the most important factors causing environmental transformations and species diversity alterations. The aim of the current study was to develop a geoinformatics-based framework to quantify alpha and beta diversity indices in two sites in Israel with different land uses, i.e.

Characterization of the biosolids composting process by hyperspectral analysis.

Composted biosolids are widely used as a soil supplement to improve soil quality. However, the application of immature or unstable compost can cause the opposite effect. To date, compost maturation determination is time consuming and cannot be done at the composting site.

An efficient approach for pixel decomposition to increase the spatial resolution of land surface temperature images from MODIS thermal infrared band data.

Land surface temperature (LST) images retrieved from the thermal infrared (TIR) band data of Moderate Resolution Imaging Spectroradiometer (MODIS) have much lower spatial resolution than the MODIS visible and near-infrared (VNIR) band data. The coarse pixel scale of MODIS LST images (1000 m under nadir) have limited their capability in applying to many studies required high spatial resolution in comparison of the MODIS VNIR band data with pixel scale of 250-500 m. In this paper we intend to develop an efficient approach for pixel decomposition to increase the spatial resolution of MODIS LST image using the VNIR band data as assistance.

Diurnal emissivity dynamics in bare versus biocrusted sand dunes.

Land surface emissivity (LSE) in the thermal infrared depends mainly on the ground cover and on changes in soil moisture. The LSE is a critical variable that affects the prediction accuracy of geophysical models requiring land surface temperature as an input, highlighting the need for an accurate derivation of LSE. The primary aim of this study was to test the hypothesis that diurnal changes in emissivity, as detected from space, are larger for areas mostly covered by biocrusts (composed mainly of cyanobacteria) than for bare sand areas.

Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm.

Land surface temperature (LST) is one of the most important variables measured by satellite remote sensing. Public domain data are available from the newly operational Landsat-8 Thermal Infrared Sensor (TIRS). This paper presents an adjustment of the split window algorithm (SWA) for TIRS that uses atmospheric transmittance and land surface emissivity (LSE) as inputs.

The effect of differential growth rates across plants on spectral predictions of physiological parameters.

Leaves of various ages and positions in a plant's canopy can present distinct physiological, morphological and anatomical characteristics, leading to complexities in selecting a single leaf for spectral representation of an entire plant. A fortiori, as growth rates between canopies differ, spectral-based comparisons across multiple plants--often based on leaves' position but not age--becomes an even more challenging mission. This study explores the effect of differential growth rates on the reflectance variability between leaves of different canopies, and its implication on physiological predictions made by widely-used spectral indices.

Comparison of UV-absorbing nets in pepper crops: spectral properties, effects on plants and pest control.

In horticultural crops, the use of screens to protect plants is the usual strategy in the Mediterranean area. Screen manufacturers offer a range of netting that vary in their UV-absorbing properties. We compared the photoeffects of seven different screens.

Natural vegetation phenology assessment by ground spectral measurements in two semi-arid environments.

Natural vegetation in semi-arid regions is characterized by three ground features, in addition to bare surfaces - biological soil crusts, annuals, and perennials. These three elements have distinguishable phenological cycles that can be detected by spectral ground measurements and by calculating the weighted normalized difference vegetation index (NDVI). The latter is the product of the derived NDVI of each ground feature and its respective areal cover.