Crop Mapping and assessment of Water-Energy Nexus consumption by irrigation
Hicham Mhamdi *, Mohamed Ahticha, Omar Kerrou, Azeddine Frimane , Mohammed Bakraoui , Mohammed Aggour
Laboratory of Electronic Systems, Information Processing, Mechanics and Energetics, Faculty of Sciences Kenitra, University Ibn Tofail Kenitra, Kenitra, Morocco
*Mail: [email protected]
Postal address: Ibn Tofail University, Faculty of Sciences, University campus, BP: 133, Kenitra, Morocco ABSTRACT:
Crop identification is vital to make an inventory of the crops grown in a given area and their cultivation period. The Remote sensing (RS) techniques can provide information on the distribution of cultivated land, crop types, and areas for the agricultural sector's effective management.
In remote sensing, various vegetation indices (VI) can analyze and evaluate multiple phenomena and themes.
The Normalized Difference Vegetation Index (NDVI) is an essential and highly significant remote measurement widely used in agriculture for phenological monitoring and crop health (Ray and Dadhwal, 2001).
In this work, we present a methodology for the contribution of NDVI from Landsat 7 (TM) and (ETM+) images to crop mapping in the Gharb region using a classification based on the pixel approach and estimating crop coefficient from NDVI.
The classification results concern six main types of crops planted in this region (beet, maize, sugar cane, market gardening, cereals, and rice). The classification map showed differences in agricultural practices adopted by farmers in crop spatial distribution. The classification results showed the ability of this methodology to discriminate between crops. The precision of classification for all classes is 90%, and the kappa indicator index is 0.88, which indicates a good classification.
Crop coefficients were deduced from the NDVI extracted from the images. Due to meteorological data collected from the meteorological station TCSC of SK Tlet, the estimation of the reference evapotranspiration was made and subsequently the potential evapotranspiration of each crop during the agricultural season 2019-2020.
The highest values for ETC were obtained when the crop was in its full development when water was mainly lost through transpiration after a slight decrease in the ratio values observed during the phase of the vegetative cycle (maturity). The water requirements (daily, monthly and annual) for the crops were determined and their electrical energy consumption.
KEYWORDS
CROP, REMOTE SENSING, EVAPOTRANSPIRATION, NDVI, CROP COEFFICIENT, LANDSAT
1. Introduction
Agriculture is a vital and crucial element in the economy of the Gharb region. To ensure efficient management of agriculture, geospatial data and statistics are indispensable. However, traditional data collection techniques are expensive and unsuitable for monitoring seasonal crop development. The RS allows the mapping of crop types by monitoring their seasonal development using multidate images covering the crop growing cycle.
The use of RS for crop identification in semi-arid areas is significant and useful [5].
Several research projects have been conducted to monitor the evolution of the crop growing cycle and evaluate crop yields using RS combined with modeling approaches (Bastiaansen and al. i 2003; Doraiswamy and al.. 2004; Inoue and al..2008, etc.). [6]
These models simulate the entire crop cycle's biophysical processes, considering as many components of the soil, the atmosphere, etc. description of crop growing (Doraiswamy et al.
2004). [9]
Research has shown a good simulation of crop coefficients derived from VI from multispectral images (Hunsaker et al., 2003). [15]Several researchers (Allen and al.. (2011)), Hunsaker and al.. (2005), Gonzalez-Dugo and al.. (2009) have studied and defined the correlation and the best possible relationship between the crop coefficients of multispectral NDVI images based on the vegetation surface's reflectance. The VI allows bio-physical parameters to emerge from multispectral images using empirical equations. The VI can be used to define and monitor different crop parameters. [20]
Different formulae were developed to estimate evapotranspiration; there are formulae based on energy balance (Penman, 1948; Allen and al.., 1998b; Nouri and al.., 2013) [18], formulas referring to the air temperature (Thornthwaite, 1948; Blaney, 1952) [19].
The FAO-56 PM equation is more accurate in ETO estimation because it uses many parameters and is the most widely used formula for estimating evapotranspiration in agriculture (Allen and al.., 1998b; Allen, 2000).
Many research studies have been carried out on estimating Kc values for vegetable crops (peas, onions, and tomatoes) [26, 22].
Researchers have revealed that VI extracted from satellite images can be used to estimate crop coefficients. [24-25-29].
2. Materials and methods
2.1 The region of study
Figure 1 : The region of Gharb
The region of Gharb constitutes one of Morocco's most important agricultural perimeters, with a surface area of 116 000 ha. The perimeter is experiencing a real dynamic concerning the transition to drip irrigation.
2.2 Meteorological data
All meteorological data were collected from the TCSC weather station located in SA TAZI.
The data are temperature (Tmax, Tmin, Taverage ), wind speed, net radiation, relative humidity, and reference evapotranspiration during the season 2019-2020.
2.3 Penman Monteith equation
The FAO-56 PM formula is expressed by the following equation : [1]
ETO=0.408Δ( Rn− G) + γ Cn
T + 273 U2(es− ea) Δ + γ(1 + CdU2)
Where :
ETO = reference evapotranspiration (mm/day) Rn = crop’s net radiation surface (MJ/m2/day),
G = soil heat flux density (MJ/m2/day),
T = mean daily air temperature ( 2m height ), u2 = wind speed at 2m height (m/s),
ea = actual vapor pressure (kPa),
Δ = slope vapor pressure curve (kPa/C),
γ =psychrometric constant (kPa/C).
es = saturation vapor pressure (kPa),
All the meteorological data were recorded at the 2 m elevation point.
[Eq.1]
[2]
The FAO-56 PM equation requires standard climatological data of solar radiation, humidity, air temperature, and wind speed. [16].
Figure 2 : THE METHODOLOGY FLOWCHART
2.4 Estimation of crop evapotranspiration : The following formula expresses crop evapotranspiration:
𝑬𝑻𝑪 = 𝑬𝑻𝒐 × 𝑲𝒄
ETC is the potential evapotranspiration of the crop, and Kc is the crop coefficient; it is a parameter that depends on the crop's growth stage. The crop coefficient indicates various environmental factors and the influence of the crop on evapotranspiration. Multiple comparisons of ETO and Kc measurements have been provided for different locations under differing conditions. [21]
Mapping of crop types Calculation of NDVI and
Layer Stacking series Multi-temporal profiles Radiometric Calibration
and Atmospheric
Correction
LANDSAT Satellite images
Gapfilling of images
Identification of cultural phenological profiles
Validation and
of
Field experimentaion Metoerological
data
KC(NDVI)
field data Pre- proces sing
Electricital consumption (
kwh)
Water need for crops (mm)
Photovoltaic power( kwh ETO ( FAO-56
Penman Monteith )
ETC ( mm/days)
[Eq.2]
[3]
The NDVI is derived from LANDSAT 7 satellite images (TM and ETM+) for six different crop stages according to the following equation :
Monitoring the evolution of the growing cycle of crops and agricultural production is often carried out using VI, particularly the NDVI.
The NDVI is the parameter that allows monitoring the evolution of the spectral response of the crop to applied irrigation rates while giving the different quality statuses of the crop (Bell and al.., 2009) [10]. The NDVI is an indicator of the chlorophyll activity in vegetation. [4]
Researches were made by ( Doorenbos and Pruitt, 1977; Allen and al.., 1998) to elaborate the Kc values of different crops during their growing cycles. [11] Kc is estimated from NDVI due to the strong relationship between the NDVI and the Kc (Ray and Dadhwal, 2001) [12].
Due to the relationship between NDVI and KC, NDVI has always been considered as a parameter for monitoring and control of vegetation during its growth cycle (Justice and Townshend, 2002) [13]. High photosynthetic activity leads to high NDVI values; in contrast, high temperatures lead to low NDVI values (Boegh et al. 1999). [14]
Efficient and rational management of irrigation water requires a reliable quantitative estimate of evapotranspiration.
2.5 Data collection
The data used to validate classification results are the field data of the types of crops existing in the region of Gharb at the date of 14/04/2019. The data distinguishes six types of crops in addition to 3 land uses. The data presenting the crop types in plots have been divided to distinguish the data to validate the results and the characterization during the classification.
𝑵𝑫𝑽𝑰 = (
𝑃𝑁𝐼𝑅−
𝑃𝑟𝑒𝑑)
(
𝑃𝑁𝐼𝑅+
𝑃𝑟𝑒𝑑)
[Eq.3]Figure 3 : Field Data
The crop identification based on phenology requires temporal monitoring during the stages of crop evolution. Crop mapping in our study was carried out for the agricultural season 2019- 2020 according to field data availability.
Table 1 . The series of images used in the study
Image sensor Date
1 ETM+ 21-10-2019
2 ETM+ 06-11-2019
3 TM 18-02-2020
4 TM 06-03-2020
5 TM 22-03-2020
6 ETM+ 15-04-2020
7 TM 09-05-2020
8 ETM+ 18-06-2020
9 TM 28-07-2020
10 TM 14-09-2020
11 TM 16-10-2020
Figure 4 : Result of the SVM classification based on pixel 3.RESULTS AND DISCUSSION
The results concern six main types of crops planted in the study area (beetroot, maize, sugar cane, market gardening, cereals, and rice). The classification map showed the differences in crops sown by farmers in corp's spatial distribution. The rice crop is practiced in the SA TAZI on both banks of the Oued Sebou; market gardening is widespread in the north-western part of the study area, while the sugar cane is generally located in the central part of the study area.
Cereal and beet crops are generally scattered throughout the region. The classification also allowed the characterization of the forest in the south of our study area, where the el-Maamoura forest is located.
3.1 WATER NEED FOR CROPS :
The ETC varies according to the season and the crop during a given period. ETC determines the amount of water to be used for irrigation. ETC is defined as the process by which water is lost by evapotranspiration. (Doorenbos, 1984; Running and al.., 2017). [7]
The two processes of transpiration and evaporation are simultaneous and closely related (Ding et al., 2013). [8]
Sept Oct. Nov. Dec Jan Fev Mars Avr Mai Juin Juily Août -- 0
20 40 60 80 100 120 140
average precipitation P (mm) Effective rain (0.65*P/Nday) (mm/Day)
Moonth
average precipitation P (mm)
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Effective rain (0.65*P/Nday) (mm/Day)
Figure 5 : Effective rain and average precipitation for the 2019-2020 compaign
SEP OCT NOV DEC JAN FEB MAR APR MAI JUN JUL AUG -- 10
12 14 16 18 20 22 24
Temperature (C°) Wind speed U(m/s) relative humidity (%)
Month
2.2 2.4 2.6 2.8 3.0 3.2 3.4
70 72 74 76 78 80 82
Figure 6: climatic data during the 2019-2020 compaign
Sep Oct Nov Dec Jan Feb Mar Apr Mai Jun Jul Aug
Month
Sep Oct Nov Dec Jan Feb Mar Apr Mai Jun Jul Aug
Month
Sept Oct. Nov. Dec Jan Fev Mars Avr Mai Juin Juil Août -- 1
2 3 4 5 6
ETo(mm/day) ETo (mm/mois)
Month
ETo(mm/day)
40 60 80 100 120 140 160 180 200
ETo ( mm / month)
Figure 7 : The reference evapotranspiration during the 2019-2020 compaign
3.2 ESTIMATION CROP COEFFICIENT DERIVED FROM NDVI :
Sept Oct. Nov. Dec Jan Fev Mars Avr Mai Juin Juil Août -- 0.6
0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4
Crops coefficients
Month Sugar cane
Rice Cereals Beet Corn
Figure 8 : The evolution of corps coefficients during the growing cycle (2019-2020 compaign )
Sep Oct Nov Dec Jan Feb Mar Apr Mai Jun Jul Aug Month
Sep Oct Nov Dec Jan Feb Mar Apr Mai Jun Jul Aug Month
Sept Oct. Nov. Dec Jan Fev Mars Avr Mai Juin Juil Août -- 0
5 10 15 20 25
ETC ( mm / Day)
Month Sugar cane Rice Cereals Beet Corn
total water need
Figure 9: The daily ETC of crops during the growing cycle (2019-2020 compaign )
Sept Oct. Nov. Dec Jan Fev Mars Avr Mai Juin Juil Août -- 0
100 200 300 400 500 600 700 800
ETC (mm/month )
Month Sugar cane Rice Cereals Beet Corn
total water need
Figure 10: The monthly ETC of crops during the growing cycle (2019-2020 compaign )
3.3 Load energy assessment
The case study consists of six man crops supplied by irrigation water by pumping station connected to the electricity grid. Fig. 12 shows the distribution of the monthly electricity consumption throughout the year 2019-2020 for the six man crops on the Gharb region . A maximum of monthly consumption is seen over the months of July and August with an average value of 1891.2 kWh and the minimum in January with the average of 1140.12 kWh.
Sep Oct Nov Dec Jan Feb Mar Apr Mai Jun Jul Aug Month
Sep Oct Nov Dec Jan Feb Mar Apr Mai Jun Jul Aug Month
Figure 11 : Distribution of electricity consumption by sector.
Sept Oct. Nov. Dec Jan Feb Mars Apr Mai June July Aug 0
200 400 600 800 1000 1200 1400 1600 1800 2000
Electricity consumption (kwh )
Monthly average load
Figure 12 : Monthly average load for irrigation compaign saison 2019-2020
3.3.1 Energy performance
The energy performance of irrigation systems must therefore be analysed by distinguishing two aspects : In order to irrigate agricultural areas, it has often been necessary to fetch water from further away, and sometimes from deeper ground: in the first instance, it is therefore pumping and transporting water that are responsible for the high energy consumption for irrigation.
However, the development of irrigation has been accompanied by the appearance of new techniques that often consume a lot of energy.
- The energy required to mobilise the water resource and bring it to the plot (the pumping station and the supply network);
Sep Oct Nov Dec Jan Feb Mar Apr Mai Jun Jul Aug
Monthly average load
- energy needed to apply the water and distribute it over the whole crop (the irrigation equipment at the plot).
3.3.2 Energy for resource mobilisation
In order to bring the volumes needed by the plant to the cultivated plot, the topography imposes energy consumption:
a) The difference in level between the resource and the plot requires the water to be supplied with gravitational potential energy: the quantity required corresponds to the product of the volume of water and the difference in level converted into units of pressure.
b) The distance from the water resource to the plot causes head losses. Of course, hydraulic engineers express these losses in units of pressure, but it is interesting to give orders of magnitude in units of energy: for example, a steel pipe of 120 mm in diameter carrying a flow of 80 m3/h causes an energy loss of 340 joules per metre for each m3 of water transported.
This quantity will be negligible if irrigating from a borehole close to the plot, but can reach significant values in the case of collective networks, where the length of the pipe is measured in kilometres.
The power consumed is expressed in kW, whatever the source of energy: it is the power demanded at the pump unit, the power consumed is partly dissipated at the pumping station, another part in the form of load losses in the distribution network and the rest at the level of the equipment at the plot.
We speak of "final energy" efficiency when we are interested in the energy available at the end of the distribution chain, i.e. in this case upstream of the pumping station: this is the energy that is invoiced to the irrigator. But if we want to reason more globally and compare different energy use strategies, we must consider the "primary energy" consumed, which includes not only the final energy, but also all the losses and consumption of producers and transformers throughout the energy production chain ( Jacques Granier and all 2013 ).
4. Conclusion
The monitoring of agricultural fields in terms of production and irrigation water management efficiency at the regional level can be based on the extraction of information from satellite images through the temporal dynamics of biophysical variables that affect crop photosynthesis.
The annual NDVI LANDSAT series has been processed according to the classification based on the pixel approach.
The results showed the ability of this methodology to discriminate between cultures. The distribution of the monthly electricity consumption throughout the year 2019-2020 for the six man crops on the Gharb region . A maximum of monthly consumption is seen over the months of July and August with an average value of 1891.2 kWh and the minimum in January with the average of 1140.12 kWh.
The energy performance of irrigation systems must therefore be analysed by distinguishing two aspects :
- The energy required to mobilise the water resource and bring it to the plot (the pumping station and the supply network);
- The energy needed to apply the water and distribute it over the whole crop (the irrigation equipment at the plot).
Reference
[1] Allen and al., 2006. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. Irrigation and DrainagePaper 56. FAO, Rome, Italy.
[2] Allen, R.G., Pereira, L.S., Howell, T.A., Jensen, M.E., 2011. Evapotranspiration information reporting: I: factors governing measurements accuracy. Review.Agric.
Water Manag. 98, 899– 920.
[3] Quarmby and al., 1993. The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Int. J. Remote Sens. 14 (2), 199–210.
https://doi.org/10.1080/01431169308904332
[4] Rouse and al. The 3rd earth resources technology satellite-1 symposium, Greenbelt, MD, pp. 309– 317. Santin-Janin, H., Garel, M., Chapuis, J.L., Pontier, D., 2009.
Assessing the performance of NDVI as a proxy for plant biomass using non-linear models: a case study on the Kerguelen archipelago. Polar Biol. 32 (6), 861–871.
[5] Bharathkumar and Mohammed-Aslam 2015. INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015) Crop Pattern Mapping of Tumkur Taluk using NDVI Technique: A Remote Sensing and GIS Approach
[6] Kuenzer, Claudia; Knauer, Kim (2013). Remote sensing of rice crop areas. International Journal of Remote Sensing, 34, 2101-2139. Dx.doi.org/10.1080/01431161.2012.738946 [7] Doorenbos, J., 1984. Guidelines for Predicting Crop Water Requirement Irrigation and drainage paper 24. Food and Agriculture Organization of the United Nations, Rome.
[8] Ding, R., Kang, S., Zhang, Y., Hao, X., Tong, L., Du, T., 2013. Partitioning evapotranspiration into soil evaporation and transpiration using a modified dual crop coefficient model in irrigated maize field with ground-mulching. Agric. Water Manag.
https://doi.org/10.1016/j.agwat.2013.05.018[9] Doraiswamy et al., September 2004.
Crop condition and yield simulations using Landsat and MODIS, Remote Sensing of Environment 92(4), DOI: 10.1016/j.rse.2004.05.017
[10] Bell, G.E., Martin, D.L., Koh, K., Han, H.R., 2009. Comparison of turfgrass visual quality ratings with ratings determined using a handheld optical sensor.
HortTechnology 19 (2), 309–316.https://doi.org/10.1016/j.agwat.2020.106586 [11] Doorenbos, J., Pruitt, W.O., 1977. Crop Water Requirements, FAO Irrigation and Drainage Paper No. 24. Food and Agriculture Organization, Rome, Italy.
[12] Ray, S.S., Dadhwal, V.K., 2001. Estimation of crop evapotranspiration of irrigation command area using remote sensing and GIS. Agric. Water Manage. 49, 239–249.
Rosenstein, O., Haymann, N., Kaplan, G., Tanny, J., 2018. Estimating cotton water consumption using a time series of Sentinel-2 imagery. Agric. Water Manage. 207, 44–
52.
[13] Justice, C.O., Townshend, J.R.G., 2002. Special issue on the Moderate Resolution Imaging Spectroradiometer (MODIS): a new generation of land surface monitoring.
Remote Sens. Environ. 83 (1), 1–2
[14] Boegh, E., Søgaard, H., Hanan, N., Kabat, P., Lesch, L., 1999. A remote sensing study of the NDVI–Ts relationship and the transpiration from sparse vegetation in the Sahel based on high- resolution satellite data. Remote Sens. Environ. 69 (3), 224–240 [15] Hunsaker, D.J., Barnes, E.M., Clarke, T.R., Fitzgerald, G.J., Pinter, P.J., 2005. Cotton irrigation scheduling using remotely sensed and FAO-56 basal crop coefficients.
Trans. ASAE 48, 1395– 1407
[16] Batchelor C.H., Roberts J., Evaporation from the irrigation water, foliage and panicles of paddy rice in north-east Sri Lanka, Agric. Meteorol. 29 (1983) 11–26.
[18] Penman, H.L., 1948. Natural evaporation from open water, bare soil, and grass.
Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. The Royal Society. pp. 120–145
[19] Thornthwaite, C.W., 1948. An approach toward a rational classification of climate.
Geogr. Rev. 38, 55–94.
[20] Nouri, H., Glenn, E.P., Beecham, S., Chavoshi Boroujeni, S., Sutton, P., Alaghmand, S., Noori, B., Nagler, P., 2016. Comparing three evapotranspiration estimation approaches in mixed urban vegetation: field-based, remote sensing-based, and observational-based methods. Remote Sens. (Basel) 8, 492.
[21] Irmak, S., Odhiambo, L.O., Specht, J.E., Djaman, K., 2013. Hourly and daily single and basal evapotranspiration crop coefficients as a function of growing degree days after emergence, leaf area index, fractional green canopy
[22] L.S. Pereira, Prediction of crop coefficients from the fraction of ground cover and height. Background and validation using ground and remote sensing data.
https://doi.org/10.1016/j.agwat.2020.106197
[23] E. Farg and al., Estimation of Evapotranspiration ETc and Crop Coefficient Kc of Wheat, in south Nile Delta of Egypt Using integrated FAO-56 approach and remote sensing data, 2012, 15, 83-89.k http://dx.doi.org/10.1016/j.ejrs.2012.02.001
[24] Hun saker, D.J.; Pinter, P.J.; Jr.; Kimball, B.A. Wheat basal crop coefficients determined by normalized difference vegetation index. Irrig. Sci. 2005, 24, 1–14.
[25] Bausch, W.C.; Neale, C.M.U. Spectral inputs improve Maize crop coefficients and irrigation scheduling. Trans. ASAE 1989, 32, 1901–1908.
[26] Irmak, S. Nebraska water and energy flux measurement, modeling, and research network (NEBFLUX). Trans. ASABE 2010, 53,
