Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements
Abstract
:1. Introduction and Scope
- Section 2 dealing with RS-related products used for direct or indirect estimation of crop water status, focusing in particular on approaches to model evapotranspiration and to support farmers’ decision for irrigation scheduling. The section also highlights the limits of these methods and tracks the evolution of some of these examples over time, with reference to the implementation of additional WS indices/functions borrowed from crop models’ formalism or conceptualization.
- Section 3 aggregates dynamic crop models by similar conceptualization of the plant–water relationships, describing the main approaches used to simulate plant growth and development, the state variable used as input for WS calculations and the processes targeted by WS, in order to provide readers with useful reference for investigating approaches to model WS and schedule irrigation. Considerations raised by model comparison and perspectives of integrating RS with crop models are also addressed.
2. Novel Technologies for Crop Water Requirements Identification
2.1. Radiation Wavelengths for Water Status Estimations
2.2. Modeling Evapotranspiration with Remote Sensing
2.3. Decision Support Systems for Irrigation Scheduling
2.4. Towards Greater Accuracy in Crop Water Stress and Water Requirements Estimations
- The surface energy balance SEBAL was originally conceived to be used with RS imagery at a landscape scale [32,33,34]. Afterwards, throughout the years, several researchers applied SEBAL in the field of agriculture and irrigation, proposing integrations or the use of various stress indices, such as the use of the evaporative fraction (the ratio of latent heat on the sum of latent plus sensible heat [40]) as an indicator of WS that can be considered a substitute of the ETa/ET0 ratio [41], the integration with a crop module for yield estimations [42], the calculation of lumped crop coefficients that incorporate crop and stress coefficients (i.e., Kc multiplied by Ks as in the FAO-56 single coefficient approach) [43], the use of the crop water stress index (CWSI, as in Bhattarai et al. [44]) and the comparison of SEBAL-derived Ks with FAO-56 Ks calculated using soil moisture sensors [45]. However, it is important to stress that SEBAL results are highly sensitive to the choice of the “wet” and “dry” pixels, that are assumed to be representative of fully contrasting hydrological conditions within the area over which the imagery was acquired [46]. Recently, Grosso et al. [47] pointed out that integration of SEBAL with field observations and soil–plant simulations can be particularly beneficial with precision irrigation practices.
- The two-source energy balance TSEB Has undergone subsequent improvements to increase spatial and temporal resolution, for example through data fusion of MODIS and Landsat disaggregated imagery [48]. Recently, Diarra et al. [49] suggested the use of a stress coefficient for monitoring WS with TSEB, based on the ratio between ETa and ETp, following Boulet et al. [50] and using another model for the calculation of the potential conditions. The coefficient ranges between 1 (totally stressed) and 0 (totally unstressed). Similarly, the ratio between actual and potential transpiration has long been used as an indicator of WS also in many crop models, such as DSSAT [51]. Diarra et al. [49] obtained optical imagery (to estimate LAI, vegetation cover and albedo) from SPOT-5 and thermal imagery (to estimate LST) from ASTER.
- FEST-WB (flash flood event based spatially distributed rainfall runoff transformation–water balance) was originally developed as a continuous water balance hydrological distributed model for simulating floods [52]. Later, the model was expanded by integrating energy balance components (FEST-EWB, where the additional E stands for energy) [53], including soil and plant parameters to calculate the water fluxes at the soil surface. The model was built to have a synergic use of RS data. A more recent implementation of FEST-EWB for managing irrigation [54] added crop-specific critical WS thresholds based on soil water content (as in FAO-56 method) to forecast irrigation timing and amount. Vegetation parameters (LAI, FCOVER and albedo) and LST were obtained from Landsat.
- The Italian IRRISAT is a service utilized since 2007 in Campania region (southern Italy), that uses satellite data to provide irrigation prescriptions directly to the farmers (for field level applications) and irrigation districts (for regional applications) [55]. IRRISAT methodology to estimate CWRs is based on the FAO-56 single coefficient direct approach, as detailed by D’Urso [56]. This approach derives crop albedo using weighting coefficient for Sentinel-2 (S2) bands and LAI using artificial neural networks S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor. The variables obtained by RS are used as inputs of the Penman–Monteith direct approach for the estimation of ETp. Fixed values for stomatal resistance (100 s m−2) and crop height (0.4 m for herbaceous crops) are assumed. Net precipitation is also calculated using the semi-empirical model by Braden [57], that accounts for the effect of canopy interception of rain. A simplified water balance is calculated in this way, and with the help of short-term weather forecasts, CWRs are calculated for each farmer’s field and irrigation district on the interval of 5–7 days. Irrigation prescriptions were originally delivered to the end users in the form of SMS and afterwards through maps and graphs available on more advanced informatic devices (smartphones, tablets etc.). Recently, Bonfante et al. [58] included IRRISAT in LCIS–DSS (low cost irrigation support–DSS). The DSS was built to include 3 different irrigation management tools: (i) W-Tens, a field tensiometer monitoring system with a set of soil water status sensors communicating to a software; (ii) IRRISAT and (iii) W-Mod, based on the SWAP model [59] to simulate crop growth and soil water balance. W-Mod is a physically based simulation model that simulates the vertical flow of water by solving Richards equation (used also in dynamic crop models, see Section 3.2). The equation is solved by applying the hydraulic conductivity relationship proposed by Van Genuchten [60], with the upper boundary condition set by ETp, irrigation and precipitation, with ETp that is partitioned using LAI as suggested by Ritchie [61]. Within W-Mod, the crop growth is simulated by a simple crop module where root growth derived from experimental data on root length and LAI development that follows the “Log Normal Model” of Su et al. [62], that uses maximum LAI and thermal time. Measured data on roots and LAI coming from field trials were used for data assimilation into W-Mod. After field testing, the authors indicated that the integration of W-Tens and IRRISAT can integrate predictions of timing and amount of irrigation, respectively. Integration (via data assimilation in this case) of W-Mod with RS data (instead of field measurements) was also suggested as a potential strategy.
- The Australian IRRISAT is another example of a long lasting DSS service [63,64,65] provided by the Australian research organization CSIRO (Commonwealth and Industrial Research Organization). The DSS uses RS data to estimate reflectance-based crop coefficients from NDVI, using a locally calibrated empirical linear relationships between NDVI and Kc (mainly for cotton, in this case). The Kc (single coefficient) is combined with on-ground ET0 from a net of weather stations to obtain ETc. A simple water balance is calculated using ETc and rainfall and irrigation to provide farmers indication of the amount of water that was used since the last irrigation [63]. Gaynor et al. [66] recently compared the performances of IRRISAT with the HYDROLOGIC crop model on the basis of observed soil moisture, finding greater overall accuracy with IRRISAT, but also better performances of HYDROLOGIC up until peak flowering stages of the crop, despite the limited agronomic information on which HYDROLOGIC was relying. The authors suggested that IRRISAT provided less accurate ET predictions at early stages of canopy growth. This seems to indicate that there is still some room for improving the DSS prediction.
- The SAFY-E (simple algorithm for yield estimation) model [67] was originally created to simulate crop yield with few crop growth and development equations. The availability of the open source Matlab code allows the users to integrate parameters or variables obtained by RS into the model. For example, Duchemin et al. [67] used handheld field measurements of green LAI and NDVI to establish an exponential relationship (potentially useful for RS data too) and calibrate the SAFY model for yield estimation. Subsequent work aimed at including a water balance and WS estimation at the expense of increased complexity. Battude et al. [68] estimated green LAI and FCOVER using high spatial and temporal resolution satellite optical images and integrated a water balance model based on FAO56 method. The satellites used were Formosat-2, SPOT, Landsat-8, Deimos-1 and SPOT4-Take5, all with sufficient spatial resolution for field scale estimates (<30 m). Green, red, NIR and SWIR bands were used for the inversion of the radiative transfer model PROSAIL via artificial neural networks to estimate LAI, FCOVER and fAPAR. LAI was also used as a proxy to estimate Kcb to be used for ET estimations in the FAO-56 method.
- Liu et al. [69] proposed to integrate RS data with a simple radiation-based approach for the calculation of biomass, depending on fAPAR and on RUE (see Section 3.1.2 for definitions and details). The authors used a compact airborne spectrographic imager (CASI) mounted on an airborne platform to obtain hyperspectral RS data during the growing season. In addition, Landsat 5 and Landsat 7 cloud-free images were employed. Surface reflectance from these sensors (visible and NIR range) was combined and used to calculate 2 VIs: NDVI and MTVI2 (modified triangular vegetation index 2). fAPAR was estimated from MTVI2. The canopy structure dynamics model (CSDM) was used to simulate fAPAR dynamics over the season depending on temperature. CSDM was fitted to fAPAR estimated from RS, to derive fAPAR seasonal trend used for the seasonal estimation of biomass accumulation through the radiation driven Monteith approach. In addition, a crop stress index varying between 0 and 1 was added to act as a modifier of the biomass-radiation equation, nullifying the equation when the crop is completely stressed and leaving it unchanged when the crop is unstressed. This approach integrated RS data with simulation of crop cover dynamics, using the radiation-biomass relationship for estimating biomass and yield and a water stress index to limit crop growth, constituting an upgrade of methods based directly on RS data only.
- Campos et al. [70] used VIs obtained from RS data to estimate fAPAR and Kt, a transpiration coefficient similar to Kcb, in order to use these as inputs of simple radiation-driven and water-driven models, respectively (details on the two approaches are provided in Section 3.1). fAPAR and Kt were obtained from relationships with VIs, but the authors contemplated that other analytical approaches could be used to obtain the two biophysical parameters. NDVI obtained by Landsat 5, 7 and 8 were interpolated to obtain daily values to use in the estimation of fAPAR and Kt. Biomass accumulation was then simulated using the radiation-driven and water-driven models.
- Olivera-Guerra et al. [71] developed a method to retrieve irrigation timing and amount at field scale from RS data. Landsat 7 and 8 images with <30% of cloud cover were used over 4 cropping seasons, with an average of 20 images per season. Optical and thermal data from these sensors were used to estimate LST and FCOVER. LST was estimated using the revised single channel algorithm [72], using the thermal band of Landsat, the atmospheric water vapor content from Modis 6.0 and the spectral surface emissivity estimated using the NDVI threshold method proposed by Sobrino et al. [73], that uses FCOVER to weight emissivity from soil and vegetation. Soil emissivity was acquired from ASTER-GED using bands 13 and 14. Vegetation cover was estimated linearly based on minimum and maximum NDVI, following Duchemin et al. [74], and NDVI was obtained from Landsat red and infrared bands. A first guess of root zone soil moisture was obtained for each Landsat overpass date using LST and FCOVER, with a partitioning method based on the LST-FCOVER feature space (e.g., as in Jiang and Islam [75]). The FAO-56 dual coefficient method water balance was applied from the last soil moisture guess, backwards, in a daily time step (recursive mode). The first time that soil moisture reached field capacity (FC) was considered as the day after irrigation was applied, and using the water balance in forward mode from the previous Landsat overpass it was possible to estimate the soil moisture before irrigation started. Since irrigation was assumed to bring soil moisture at FC, irrigation amount could be calculated. In this way, it was possible to reconstruct the soil water and irrigation dynamics (timing and amount) of the entire season of specific fields (by pixel aggregation), providing estimates of water consumption and of crop water requirements when field data are scarce or not available.
3. Crop Water Stress in Dynamic Crop Models
3.1. Water- and Radiation-Driven Crop Growth and Crop Development
3.1.1. Water-Driven Models
3.1.2. Radiation-Driven Models
3.1.3. Hybrid Models
3.1.4. Crop Development
3.2. Soil Water Content and Transpiration Deficit as Inputs of Water Stress Functions
3.2.1. Soil Water Content
3.2.2. Transpiration Deficit
3.2.3. WS Functions Used in Crop Models
3.3. Plant Processes Targeted by Water Stress
3.3.1. WS Effect on Leaf Expansion
3.3.2. WS Effect on Transpiration/Photosynthesis
3.3.3. WS Effect on Senescence
3.3.4. WS Effect on Other Processes
3.4. Comparison of Dynamic Crop Models
3.5. Integration of RS Data and Crop Models
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Abbreviation | Full Name | Description |
---|---|---|
APAR | absorbed photosynthetically active radiation | Amount of PAR actually absorbed by the canopy, excluding the PAR reflected by the canopy and including the PAR reflected from soil into the canopy. |
CWR | Crop water requirement | Amount of water required by a crop to grow without transpiration/photosynthesis stress. |
CWSI | Crop water stress index | Normalized index using canopy temperature to quantify crop water status [141]. |
DSS | decision support system | Software to support decision-making. |
DUL | drain upper limit | See FC (Field Capacity). Described in Ritchie [51]. |
ET0 | Reference evapotranspiration | Evapotranspiration (transpiration + evaporation) of a hypothetical well-watered grass surface with 0.12 m height, albedo 0.23 and LAI 2.88. See FAO-56 paper [5]. |
ETa | Actual evapotranspiration | Effective evapotranspiration of a field with a specific crop when stresses and restrictions are accounted for. See FAO-56 paper [5]. |
ETp | Potential evapotranspiration | Evapotranspiration (potential transpiration + potential evaporation) of a field with a specific crop without limitations from soil water and salinity stress, crop density, pest and disease, weed infestation or low fertility. For simplicity, only water stress can be considered. See FAO-56 paper [5]. |
FAO-56 | FAO-56 approach | Method to calculate ET presented in FAO Irrigation and Drainage paper no. 56 [5]. |
FAO-66 | FAO-66 approach | Method used to simulate plant–water relationship in AQUACROP presented in FAO Irrigation and Drainage paper no. 66 [88]. |
fAPAR | Fraction of active photosynthetic radiation | APAR/PAR |
FCOVER | Fractional vegetation cover | Fraction of ground covered by green vegetation (also called canopy cover). Values vary between 0 (no cover) and 1 (complete cover). |
FC | Field capacity | Soil water content at which water stops draining down. The remaining water can (ideally) be removed only by ET. See FAO-66 paper [88]. |
GDD | growing degree days | Unit of measurement of thermal time, given by the difference of actual temperature and crop-specific base temperature. |
HI | Harvest index | Ratio of dry yield over total aboveground dry biomass. |
IPAR | intercepted photosintetically active radiation | Amount of PAR caught by canopy layers as PAR travels down through the canopy to the ground. |
Kc | Crop coefficient | Single crop coefficient in the FAO-56 single coefficient approach. It is the ratio between ETp and ET0. See [5]. |
Kcb | Basal crop coefficient | Crop coefficient in the FAO-56 dual coefficient approach, where Kc is the sum of Kcb and Ke. It is used to separate plant transpiration from soil evaporation. See [5]. |
Ke | Soil water evaporation coefficient | Soil evaporation coefficient in the FAO-56 dual coefficient approach, where Kc is the sum of Kcb and Ke. It is used to separate soil evaporation from plant transpiration. See [5]. |
Ks | Water stress coefficient | Generic coefficient of water stress (range 0–1). |
LAI | Leaf area index | One-sided green leaf area per unit of ground surface area. |
LAD | Leaf area duration | Integral of LAI curve over time. |
LL | Lower limit (of plant water availability) | See PWP (Plant Wilting Point). Described in Ritchie [51]. |
LUE | Light use efficiency | See RUE. |
MTVI2 | Modified triangular vegetation index 2 | Reflectance-based vegetation index originally developed by Haboudane et al. [142], improves MTVI1 by adding a soil adjustment factor. |
NDVI | Normalized difference vegetation index | Vegetation Index obtained from red and near infrared (NIR) wavelengths of surface reflectance [143]. |
NIR | Near infrared | Electromagnetic radiation with wavelength ranging from 0.7 to 1 μm circa. |
PAR | photosynthetically active radiation | Solar radiation between the 400–700 nm interval, used for photosynthesis. |
PWP | Plant wilting point | Soil water content at which water stops being absorbed by roots. Critical threshold for plant survivability. See FAO-66 paper [88]. |
RS | Remote sensing | Technical and scientific discipline to acquire information from an object without making physical contact with it. |
RUE | Radiation use efficiency | Slope of the relationship between dry biomass accumulation and absorbed or intercepted PAR (dry matter produced per each unit of radiation). |
RWUp | Root water uptake (potential) | Amount of water supplied by the soil that can be absorbed by roots. |
SAVI | Soil adjusted vegetation index | Reflectance-based vegetation index originally developed by Huete [144], includes adjustment for canopy background (soil). |
SEB (approach) | Surface energy balance | Model/approach to estimate energy fluxes between Earth’s surface and atmosphere. |
SWIR | Shortwave infrared | Electromagnetic radiation with wavelength ranging from 0.7 to 2.5–3 μm circa. |
Ta | Actual transpiration | Effective transpiration of a specific crop when stresses and restrictions are accounted for. |
TAW | Total available water | Amount of water stored in a specified soil layer between FC (also called DUL) and PWP (also called LL). It is the water that can be uptaken by the plant. |
Tp | Potential transpiration | Potential transpiration of a specific crop when transpiration and photosynthesis are not reduced by stresses and restrictions. |
VI | Vegetation Index | Index obtained from crop/cropping field reflectance that can be related to crop physiological parameters. |
VPD | Vapor pressure deficit | Difference between the amount of moisture present in the air and the amount of moisture air can hold when saturated. Calculated from temperature and humidity. |
WS | crop Water Stress | Suboptimal condition of plant water status, when the plant is not able to fully sustain all its processes due to water shortage. WS affects several plant processes, with different sensitivity. |
Dynamic crop model/crop model | Model simulating the dynamics of a cropping system over time (see [78]). | |
Penman–Monteith | FAO standard method for estimating evapotranspiration [5]. Requires measurements or estimations of altitude, latitude, temperature, humidity, solar radiation and wind speed. | |
Priestley–Taylor | Method for estimating evapotranspiration (from Priestley and Taylor [124]). Requires only radiation and temperature data, but an appropriate value of the Priestley-Taylor constant should be provided. | |
State variable | Variable describing the dynamics of relevant features of the soil–plant–atmosphere continuum in a crop model. It is usually an input or output of the model (e.g., temperature, LAI, biomass, soil moisture, etc.) | |
Radiation-driven (of a crop model) | Approach based on the concept of linear increase in dry biomass production with increasing absorbed/intercepted radiation (from Monteith [103]) | |
Shuttleworth-Wallace | Method for estimating evapotranspiration (from Shuttleworth and Wallace [125]). It is a combination equation of Penman–Monteith formulas for bare soil and crop. | |
Water-driven (of a crop model) | Approach based on the concept of linear increase in biomass production with increasing normalized transpiration (from Tanner and Sinclair [93]). |
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APSIM | Website | https://www.apsim.info/ [80] |
Main developer | CSIRO, State of Queensland, University of Queensland | |
AQUACROP | Website | http://www.fao.org/aquacrop/en/ [81] |
Main developer | FAO | |
CROPSYST | Website | http://modeling.bsyse.wsu.edu/CS_Suite_4/CropSyst/index.html [82] |
Main developer | Washington State University | |
DAISY | Website | https://daisy.ku.dk/about-daisy/ [83] |
Main developer | University of Copenhagen | |
DSSAT | Website | https://dssat.net/ [84] |
Main developer | University of Florida | |
EPIC | Website | https://epicapex.tamu.edu/epic/ [85] |
Main developer | Texas A&M University | |
RZWQM2 | Website | https://data.nal.usda.gov/dataset/rzwqm2 [86] |
Main developer | USDA | |
STICS | Website | https://www6.paca.inrae.fr/stics_eng/ [87] |
Main developer | INRA |
Plant Processes Directly Affected by WS: | ||||||
---|---|---|---|---|---|---|
Model | Biomass Accumulation | Canopy Expansion | WS Function Input Variable | Leaf Expansion | Transpiration-Photosynthesis | Senescence |
APSIM (plant module) | R–W | LAI | avW-Tdef | X | X | X |
AQUACROP | W | CC (FCOVER) | avW | X | X | X |
CROPSYST | R–W | LAI | Tdef | X | X | X |
DAISY | R | LAI | Tdef | X | ||
DSSAT (CERES) | R | LAI | Tdef | X | X | |
EPIC | R | LAI | Tdef | X | X | |
RZWQM2 | R | LAI | Tdef | X | ||
STICS | R | LAI | avW | X | X | X |
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Tolomio, M.; Casa, R. Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements. Remote Sens. 2020, 12, 3945. https://doi.org/10.3390/rs12233945
Tolomio M, Casa R. Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements. Remote Sensing. 2020; 12(23):3945. https://doi.org/10.3390/rs12233945
Chicago/Turabian StyleTolomio, Massimo, and Raffaele Casa. 2020. "Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements" Remote Sensing 12, no. 23: 3945. https://doi.org/10.3390/rs12233945
APA StyleTolomio, M., & Casa, R. (2020). Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements. Remote Sensing, 12(23), 3945. https://doi.org/10.3390/rs12233945