Special Issue "Remote Sensing for Crop Water Stress Detection and Irrigation Management"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 30 April 2021.

Special Issue Editors

Dr. Yafit Cohen
Website
Guest Editor
Agricultural Engineering Institute, Agrcultural Research Organization (ARO), Volcani Center, Israel
Interests: Precision agriculture: (a) Fusion of thermal and hyperspectral imagery for monitoring and mapping water status and nitrogen level in various crops and orchards; (b) Spatio-spectral analysis of thermal and hyperspectral imagery for management zones delineation; Spatio-temporal analysis of pests and diseases dispersion: Medfly, Olive fly, Heliothis, leaf-roll virus in grapevine and soil-borne diseases in potato; Development of spatial decision support tools and systems for pest control
Prof. George Vellidis
Website
Guest Editor
Crop and Soil Sciences Department, University of Georgia, USA
Interests: Applying principles of engineering and the sciences to measure model, and manage the interaction between agricultural production systems and the environment. Under this umbrella, he has developed two areas of emphasis – precision agriculture and water resources. Often these two areas blend.
Dr. Carlos Ballester Lurbe
Website
Guest Editor
Irrigation Research group, Centre for Regional and Rural Futures (CeRRF), Deakin University, Australia
Interests: Evaluation of crop water requirements and on-farm irrigation management. Crop water stress detection for the implementation of deficit irrigation strategies. Remote sensing tools for measuring and managing water and nitrogen status variability in crop production.

Special Issue Information

Dear Colleagues,

Due to population growth and increasing food demands, irrigated agriculture will increasingly take place under water scarcity. Thus, management techniques that can produce ‘more crop per drop’ will assume increased importance. Remote sensing data can be used to assess crop water status in the field, to estimate evapotranspiration, to delineate homogeneous management zones, and ultimately characterize and analyze them to produce application or prescription maps for variable rate irrigation. Remote sensing data provides a wide range of use levels, from mapping crop variability to measuring and mapping plant water status that supports irrigation actions that would have positive influence on irrigation water productivity and/or harvest outcome.

 

Dr. Yafit Cohen
Prof. George Vellidis
Dr. Carlos Ballester Lurbe
Guest Editor

Keywords

  • Canopy water status
  • Canopy water content
  • Evapotranspiration
  • Thermal imaging
  • Time-series spectral indices
  • Water stress indices
  • In-field water status variability
  • Irrigation decision making
  • Precision irrigation
  • Decision support tools
  • Water use efficiency

Published Papers (3 papers)

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Research

Open AccessArticle
Modeling Directional Brightness Temperature (DBT) over Crop Canopy with Effects of Intra-Row Heterogeneity
Remote Sens. 2020, 12(17), 2667; https://doi.org/10.3390/rs12172667 - 19 Aug 2020
Abstract
In order to improve the simulation accuracy of directional brightness temperature (DBT) and the retrieval accuracy of component temperature, a model considering intra-row heterogeneity to simulate the DBT angular distribution over crop canopy is proposed. At individual scale, the probability of leaf appearance [...] Read more.
In order to improve the simulation accuracy of directional brightness temperature (DBT) and the retrieval accuracy of component temperature, a model considering intra-row heterogeneity to simulate the DBT angular distribution over crop canopy is proposed. At individual scale, the probability of leaf appearance is inversely proportional to the distance from central stem. On the basis of this assumption, we formulated leaf area volume density (LAVD) spatial distribution at three hierarchical scales: individual scale, row scale, and scene scale. The equations for directional gap probability and bi-directional gap probability were modified to adapt the heterogeneity of row structure. Afterwards, a straightforward radiative transfer model was built based on the gap probabilities. A set of simulated data was generated by the thermal radiosity-graphics combined model (TRGM) as the benchmark to evaluate both forward simulation and inversion ability of the new model; we compared the new DBT model against an existing model assuming row as homogeneous box. With the growth of crops, the canopy structure of row crops will gradually change from row structure to continuous canopy. The new DBT model agreed with the TRGM model much better than the homogeneous row model at the middle stage of the crop growth season. The new model and the homogeneous row model achieve similar accuracy at early stage and end stage. At the middle growth stage, the new model can improve the accuracy of soil temperature retrieval. We recommend the new DBT model as an option to improve the DBT simulation and component temperature retrieval for row-planted crop canopy. In particular, the more accurate component temperatures during the middle growth stage are fundamentally important in characterizing crop water status, evapotranspiration, and soil moisture, which are subsequently critical for predicting crop productivity. Full article
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Open AccessArticle
Using Satellite Thermal-Based Evapotranspiration Time Series for Defining Management Zones and Spatial Association to Local Attributes in a Vineyard
Remote Sens. 2020, 12(15), 2436; https://doi.org/10.3390/rs12152436 - 29 Jul 2020
Abstract
A well-planned irrigation management strategy is crucial for successful wine grape production and is highly dependent on accurate assessments of water stress. Precision irrigation practices may benefit from the quantification of within-field spatial variability and temporal patterns of evapotranspiration (ET). A spatiotemporal modeling [...] Read more.
A well-planned irrigation management strategy is crucial for successful wine grape production and is highly dependent on accurate assessments of water stress. Precision irrigation practices may benefit from the quantification of within-field spatial variability and temporal patterns of evapotranspiration (ET). A spatiotemporal modeling framework is proposed to delineate the vineyard into homogeneous areas (i.e., management zones) according to their ET patterns. The dataset for this study relied on ET retrievals from multiple satellite platforms, generating estimates at high spatial (30 m) and temporal (daily) resolutions for a Vitis vinifera Pinot noir vineyard in the Central Valley of California during the growing seasons of 2015-2018. Time-series decomposition was used to deconstruct the time series of each pixel into three components: long-term trend, seasonality, and remainder, which indicates daily fluctuations. For each time-series component, a time-series clustering (TSC) algorithm was applied to partition the time series of all pixels into homogeneous groups and generate TSC maps. The TSC maps were compared for spatial similarities using the V-measure statistic. A random forest (RF) classification algorithm was used for each TSC map against six environmental variables (elevation, slope, northness, lithology, topographic wetness index, and soil type) to check for spatial association between ET-TSC maps and the local characteristics in the vineyard. Finally, the TSC maps were used as independent variables against yield (ton ha-1) using analysis of variance (ANOVA) to assess whether the TSC maps explained yield variability. The trend and seasonality TSC maps had a moderate spatial association (V = 0.49), while the remainder showed dissimilar spatial patterns to seasonality and trend. The RF model showed high error matrix-based prediction accuracy levels ranging between 86% and 90%. For the trend and seasonality models, the most important predictor was soil type, followed by elevation, while the remainder TSC was strongly linked with northness spatial variability. The yield levels corresponding to the two clusters in all TSC were significantly different. These findings enabled spatial quantification of ET time series at different temporal scales that may benefit within-season decision-making regarding the amounts, timing, intervals, and location of irrigation. The proposed framework may be applicable to other cases in both agricultural systems and environmental modeling. Full article
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Open AccessArticle
A Hyperspectral-Physiological Phenomics System: Measuring Diurnal Transpiration Rates and Diurnal Reflectance
Remote Sens. 2020, 12(9), 1493; https://doi.org/10.3390/rs12091493 - 08 May 2020
Cited by 1
Abstract
A novel hyperspectral-physiological system that monitors plants dynamic response to abiotic alterations was developed. The system is a sensor-to-plant platform which can determine the optimal time of day during which physiological traits can be successfully identified via spectral means. The directly measured traits [...] Read more.
A novel hyperspectral-physiological system that monitors plants dynamic response to abiotic alterations was developed. The system is a sensor-to-plant platform which can determine the optimal time of day during which physiological traits can be successfully identified via spectral means. The directly measured traits include momentary and daily transpiration rates throughout the daytime and daily and periodical plant weight loss and gain. The system monitored and evaluated pepper plants response to varying levels of potassium fertilization. Significant momentary transpiration rates differences were found between the treatments during 07:00–10:00 and 14:00–17:00. The simultaneous frequently measured high-resolution spectral data provided the means to correlate the two measured data sets. Significant correlation coefficients between the spectra and momentary transpiration rates resulted with a selection of three bands (ρ523, ρ697 and ρ818nm) that were used to capture transpiration rate differences using a normalized difference formula during the morning, noon and the afternoon. These differences also indicated that the best results are not always obtained when spectral (remote or proximal) measurements are typically preformed around noon (when solar illumination is the highest). Valuable information can be obtained when the spectral measurements are timed according to the plants’ dynamic physiological status throughout the day, which may vary among plant species and should be considered when planning remote sensing data acquisition. Full article
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