Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco

Bouras, Elhoussaine; Jarlan, Lionel; Er‐Raki, Salah; Albergel, Clément; Richard, Bastien; Balaghi, Riad; Khabba, Saïd

doi:10.3390/rs12244018

Cited by 29 publications

(29 citation statements)

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“…Our specific objective is to develop empirical models that can forecast cereal yield early in the crop season (up to 4 months before harvest). More specifically, this study builds on previous work carried out in Morocco that highlighted biophysically sound linkages between wheat yields and weather data and climate indices (Jarlan et al [11]) and between wheat yields and drought indices (Bouras et al [10]). It also aimed to go further than Balaghi et al [12] by analyzing the potential of climate and drought indices information to forecast yields earlier in the season and at a finer spatial scale than Lehmann et al [45].…”

Section: Introductionmentioning

confidence: 73%

“…Within this context, achieving food security, one of the key points of the Sustainable Development Goals [8], relies on a reliable monitoring system of wheat production [2]. An early and reliable forecast of the pre-harvest cereal yield in large areas would assist decision-makers in order to anticipate important needs, especially in countries such as Morocco that are not always self-sufficient [9][10][11][12]. It would also help to identify yield gaps and to better understand the wheat response to local climatic and edaphic conditions [9,13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, the performance of the models was lower for some districts, which may be due to other variables influencing yields such as the soil type or the practical management. More recently, many research works have focused on establishing a relationship between remote sensing indices and observed crop yield [10,[32][33][34]. The main advantage of using remote sensing observations in crop yield forecasting is that they allow the obtaining of information on a large scale, independent of territorial boundaries.…”

Section: Introductionmentioning

confidence: 99%

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Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco

et al. 2021

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Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting.

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Section: Introductionmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco

et al. 2021

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“…Peña-Gallardo et al [18] studied the impact of drought on cereal productivity in Spain using several meteorological drought indices. Bouras et al [19] evaluated the impact of agricultural drought on cereal yields in Morocco using remote sensing indices.…”

Section: Introductionmentioning

confidence: 99%

Agricultural Drought Trends on the Iberian Peninsula: An Analysis Using Modeled and Reanalysis Soil Moisture Products

et al. 2021

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Drought has a great impact on agriculture and food security, and climate change is increasing its frequency and exacerbating its intensity. Given the enormous interest in studying the recent drought evolution, in this work, agricultural drought trends over the past four decades on the Iberian Peninsula (IP) were analyzed. A trend analysis was performed with soil moisture based on the study of the evolution of anomalies and the Soil Water Deficit Index (SWDI). Two soil moisture databases (Lisflood and ERA5-Land) were used and the analysis was performed at daily and weekly temporal scales. Climate characteristics and soil properties were also considered to detect whether a trend spatial pattern could be identified. The results have shown a clear predominance of negative trends. A marked temporal pattern with negative trends was obtained over a 10-month period that coincided with the growing season of most of the crops on the IP, while a positive trend was observed over 2 months. No differences were found based on the climatic zone or soil characteristics. However, negative trends were observed to decrease as the clay content increased. These results can provide useful information for better water management and agriculture of the IP and other Mediterranean areas.

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“…Promising tools like machine learning approaches have demonstrated their ability to retrieve many environmental variables and solve complex non-linear problems. Machine learning approaches were used for monitoring a normalized difference vegetation index using Sentinel-1 data [60], while other works used machine leaning approaches for spatial downscaling of land surface tempera-ture [61,62], mapping soil moisture [63,64], gap-filling of high-resolution soil moisture [65], evapotranspiration [66], and crop yield forecasting [67].…”

Section: Introductionmentioning

confidence: 99%

Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach

Amazirh¹,

Bouras

Olivera-Guerra

et al. 2021

Remote Sensing

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Monitoring agricultural crops is of paramount importance for preserving water resources and increasing water efficiency over semi-arid areas. This can be achieved by modelling the water resources all along the growing season through the coupled water–surface energy balance. Surface albedo is a key land surface variable to constrain the surface radiation budget and hence the coupled water–surface energy balance. In order to capture the hydric status changes over the growing season, optical remote sensing becomes impractical due to cloud cover in some periods, especially over irrigated winter crops in semi-arid regions. To fill the gap, this paper aims to generate cloudless surface albedo product from Sentinel-1 data that offers a source of high spatio-temporal resolution images. This can help to better capture the vegetation development along the growth season through the surface radiation budget. Random Forest (RF) algorithm was implemented using Sentinel-1 backscatters as input. The approach was tested over an irrigated semi-arid zone in Morocco, which is known by its heterogeneity in term of soil conditions and crop types. The obtained results are evaluated against Landsat-derived albedo with quasi-concurrent Landsat/Sentinel-1 overpasses (up to one day offset), while a further validation was investigated using in situ field scale albedo data. The best model-hyperparameters selection was dependent on two validation approaches (K-fold cross-validation ‘k = 10’, and holdout). The more robust and accurate model parameters are those that represent the best statistical metrics (root mean square error ‘RMSE’, bias and correlation coefficient ‘R’). Coefficient values ranging from 0.70 to 0.79 and a RMSE value between 0.0002 and 0.00048 were obtained comparing Landsat and predicted albedo by RF method. The relative error ratio equals 4.5, which is acceptable to predict surface albedo.

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Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco

Cited by 29 publications

References 125 publications

Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco

Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco

Agricultural Drought Trends on the Iberian Peninsula: An Analysis Using Modeled and Reanalysis Soil Moisture Products

Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach

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