Remote monitoring of agricultural systems using NDVI time series and machine learning methods: a tool for an adaptive agricultural policy

Lebrini, Youssef; Boudhar, Abdelghani; Htitiou, Abdelaziz; Hadria, Rachid; Lionboui, Hayat; Bounoua, Lahouari; Benabdelouahab, Tarik

doi:10.1007/s12517-020-05789-7

Cited by 20 publications

(16 citation statements)

References 64 publications

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“…The OER basin covers an area of 38,000 km 2 , while its administrative area is around 50,000 km 2 . The OER basin is made up of five geographical units from the Atlas Mountains, the foothill areas, the plain, the phosphate plateau, and the coastal area [4]. A combination of flat and mountain terrains generally characterize the topography of the basin.…”

Section: Study Areamentioning

confidence: 99%

“…Elevation ranges between 100 (e.g., in the western and coastal zones) and 3890 m (e.g., in the eastern zone) above sea level [42] ( Figure 1). The OER River sources are located in the mountainous upstream zones, and the river covers a distance of 550 km, overpassing the Tadla irrigated perimeter (TIP), the coastal areas, and the northern zone of the Doukkala irrigated perimeter (DIP), and flows into the Atlantic Ocean at Azemmour city [4]. The climate is variable from humid in coastal and mountainous zones to semi-arid in the plains, with cold winters and dry summers [42].…”

Section: Study Areamentioning

confidence: 99%

“…A dataset of 437 images covering the OER basin was downloaded for the period between 2000 and 2019 via the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LP-DAAC). The MOD13Q1 NDVI data are within a spatial resolution of 250 m and a temporal frequency of 16 days in sinusoidal projection [4]. Time series data were re-projected to UTM projection to be used further in this study.…”

Section: Multitemporal Modis and Chirps Data Acquisition And Derived mentioning

confidence: 99%

“…In this context, accurate management and monitoring of farming systems, particularly at a large scale, can substantially benefit from geo-spatial technologies and remote sensing data [3]. However, in semi-arid regions, phenological data are scarce and difficult to produce due to the lack of sufficient ground information and limited studies [4]. Therefore, the use of remotely sensed phenological metrics (PhM) may provide a viable alternative to costly and time-consuming field sampling [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data

et al. 2021

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Changing land use patterns is of great importance in environmental studies and critical for land use management decision making over farming systems in arid and semi-arid regions. Unfortunately, ground data scarcity or inadequacy in many regions can cause large uncertainties in the characterization of phenological changes in arid and semi-arid regions, which can hamper tailored decision making towards best agricultural management practices. Alternatively, state-of-the-art methods for phenological metrics’ extraction and long time-series analysis techniques of multispectral remote sensing imagery provide a viable solution. In this context, this study aims to characterize the changes over farming systems through trend analysis. To this end, four farming systems (fallow, rainfed, irrigated annual, and irrigated perennial) in arid areas of Morocco were studied based on four phenological metrics (PhM) (i.e., great integral, start, end, and length of the season). These were derived from large Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series using both a machine learning algorithm and a pixel-based change analysis method. Results showed that during the last twenty-year period (i.e., 2000–2019), a significant dynamism of the plant cover was linked to the behavior of farmers who tend to cultivate intensively and to invest in high-income crops. More specifically, a relevant variability in fallow and rainfed areas, closely linked to the weather conditions, was found. In addition, significant lag trends of the start (−6 days) and end (+3 days) were found, which indicate that the length of the season was related to the spatiotemporal variability of rainfall. This study has also highlighted the potential of multitemporal moderate spatial resolution data to accurately monitor agriculture and better manage land resources. In the meantime, for operationally implementing the use of such work in the field, we believe that it is essential consider the perceptions, opinions, and mutual benefits of farmers and stakeholders to improve strategies and synergies whilst ensuring food, welfare, and sustainability.

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Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Multitemporal Modis and Chirps Data Acquisition And Derived mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data

et al. 2021

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“…One of the methods that scientists and decision-makers rely on for effective tracking and monitoring of agricultural landscapes is detailed spatial information about crop growth (DeFries et al 2005). Currently, remote sensing satellite systems offer great potential to monitor and analyse agricultural crops at various spatial and temporal scales owing to its synoptic coverage and repetitive measurements (Benabdelouahab et al, 2019;Htitiou et al, 2020;Lebrini et al, 2020Lebrini et al, , 2019. A wide range of observation satellites can be used to produce vegetation indices such as the normalized difference vegetation index (NDVI) for monitoring crops, vegetation photosynthetic activities and biophysical properties by taking advantage of its high correlation with the leaf area index and leaf chlorophyll concentrations.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Reconstruction of Spatiotemporally Fused Satellite Images for Smart Agriculture Applications in a Heterogeneous Agricultural Region

Htitiou

Boudhar

Lebrini

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Remote sensing offers spatially explicit and temporally continuous observational data of various land surface parameters such as vegetation index, land surface temperature, soil moisture, leaf area index, and evapotranspiration, which can be widely leveraged for various applications at different scales and contexts. One of the main applications is agricultural monitoring, where a smart system based on precision agriculture requires a set of satellite images with a high resolution, both in time and space to capture the phenological stages and fine spatial details, especially in landscapes with various spatial heterogeneity and temporal variation. These requirements sometimes cannot be provided by a single sensor due to the trade-off required between spatial and temporal resolutions and/or the influence of cloud cover. The data availability of new generation multispectral sensors of Landsat-8 (L8) and Sentinel-2 (S2) satellites offers unprecedented options for such applications. Given this, the current study aims to display how the synergistic use of these optical sensors can efficiently support such an application. Herein, this study proposes a deep learning spatiotemporal data fusion method to fill the need for predicting a dense time series of vegetation index with fine spatial resolution. The results show that the developed method creates more accurate fused NDVI time-series data that were able to derive phenological stages and characteristics in single-crop fields, while keeps more spatial details in such a heterogeneous landscape.

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Digitalization and Agricultural Development: Evidence from Morocco

Lionboui¹,

Boudhar

Lebrini

et al. 2022

Food Security and Climate-Smart Food Systems

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Remote monitoring of agricultural systems using NDVI time series and machine learning methods: a tool for an adaptive agricultural policy

Cited by 20 publications

References 64 publications

Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data

Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data

Deep Learning-Based Reconstruction of Spatiotemporally Fused Satellite Images for Smart Agriculture Applications in a Heterogeneous Agricultural Region

Digitalization and Agricultural Development: Evidence from Morocco

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