Remote Sensing Based Yield Estimation in a Stochastic Framework – Case Study of Durum Wheat in Tunisia

Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in a framework constrained by the information availability, remote sensing data to yield conversion parameters are to be estimated. Statistical models are suitable for this purpose given their ability to deal with statistical errors. This paper explores the performance in yield estimation of various remote sensing indicators based on varying degrees of bio-physical insight, in interaction with statistical methods (linear regressions) that rely on different hypotheses. Jackknifed results (leave one year out) are presented for the case of wheat yield regional estimation in Tunisia using the SPOT-VEGETATION instrument.JRC.H.4-Monitoring Agricultural Resource

Model de estimare a producției de orz pe baza imaginilor satelitare

The present study aimed to analyze and evaluate the barley crops, and also to estimate production by analyzing satellite images. The study was conducted at Didactic and Experimental Resort (DER) of BUASVM Timisoara, Timis County, Romania. The PlanetScope platform was used for the study, with a spatial resolution of 3 m. The satellite images were taken in the PlanetScope Remote Sensing System, at 7 different moments, between 27 March and 27 June 2020. Based on spectral data, MSAVI2 and NDVI indices were calculated. Four plots cultivated with barley were studied (B/ A75, B/A80, B/A82 and B/A84). The variation of the MSAVI2 and NDVI indices in relation to the time factor (T, days), over the study interval, was described by the polynomial models of 2nd degree, in statistical accuracy conditions. Based on the regression analysis, the estimation of the production based on the MSAVI2 and NDVI indices was possible under the conditions of R2=0.998, P<0.05 for plot B/A75, R2=0.999, P<0.05 for plot B/A80, R2=0.997, P=0.0577 for plot B/ A82 and respectively, R2=0.999, P<0.05 for plot B/A84. From the calculation of the RMSEP index, for the estimated productions, the values were obtained: RMSEP=80.8162 for YB/A75, RMSEP=50.1633 for YB/A80, RMSEP=192.3947 for YB/ A82 and respectively RMSEP=112.2899 for YB/A84. The value of RMSEP=50.1633 for YB/A80 confirms that for the plot B/ A80 the production estimate was made with the highest precision.Prezentul studiu a avut ca scop analiza și evaluarea culturilor de orz, precum și estimarea producției prin analiza imaginilor satelitare. Studiul a fost realizat la Statiunea Didactica si Experimentala (SDE) a BUASVM Timisoara, Judetul Timis, Romania. Pentru studiu a fost utilizată platforma PlanetScope, cu o rezoluție spațială de 3 m. Imaginile satelitare au fost realizate în Sistemul de Teledetecție PlanetScope, în 7 momente diferite, în perioada 27 March and 27 June 2020. Pe baza datelor spectrale, au fost calculați indicii MSAVI2 și NDVI. Au fost studiate patru parcele cultivate cu orz (B/A75, B/A80, B/A82 si B/A84). Variația indicilor MSAVI2 și NDVI în raport cu factorul timp (T, zile), pe intervalul de studiu, a fost descrisă de modelele polinomiale de gradul II, în condiții de acuratețe statistică. Pe baza analizei de regresie, estimarea producției pe baza indicilor MSAVI2 și NDVI a fost posibilă în condițiile R2=0,998, P<0,05 pentru parcela B/ A75, R2 =0,999, P<0,05 pentru parcela B/A80, R2=0,997, P=0,0577 pentru parcela B/A82 și respectiv, R2 =0,999, P<0.05 pentru parcela B/A84. Din calculul indicelui RMSEP, pentru producțiile estimate, s-au obținut valorile: RMSEP =80,8162 pentru YB/A75, RMSEP =50,1633 pentru YB/A80, RMSEP=192,3947 pentru YB/A82 și respectiv RMSEP =112.2899 pentru YB/A84. Valoarea RMSEP =50,1633 pentru YB/A80 confirmă faptul că pentru parcela B/A80 estimarea producției a fost făcută cu cea mai mare precizie

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the availability of long time series as needed by the majority of the yield prediction methods used today.JRC.H.4-Monitoring Agricultural Resource

Hyperspectral refrectace as a basis to discriminate olive varieties - a tool for sustainable crop management

Worldwide sustainable development is threatened by current agricultural land change trends, particularly by the increasing rural farmland abandonment and agricultural intensification phenomena. In Mediterranean countries, these processes are a ecting especially traditional olive groves with enormous socio-economic costs to rural areas, endangering environmental sustainability and biodiversity. Traditional olive groves abandonment and intensification are clearly related to the reduction of olive oil production income, leading to reduced economic viability. Most promising strategies to boost traditional groves competitiveness—such as olive oil di erentiation through adoption of protected denomination of origin labels and development of value-added olive products—rely on knowledge of the olive varieties and its specific properties that confer their uniqueness and authenticity. Given the lack of information about olive varieties on traditional groves, a feasible and inexpensive method of variety identification is required. We analyzed leaf spectral information of ten Portuguese olive varieties with a powerful data-mining approach in order to verify the ability of satellite’s hyperspectral sensors to provide an accurate olive variety identification. Our results show that these olive varieties are distinguishable by leaf reflectance information and suggest that even satellite open-source data could be“Integrated protection of the Alentejo olive grove. Contributions to its innovation and improvement against its key enemies” with the reference ALT20-03-0145-FEDER-000029. co-financed by the European Union through the European Regional Development Fund. under the ALENTEJO 2020 (Regional Operational Program of the Alentejo).PTDC/ASP-PLA/30650/2017 (“Fundação para a Ciência e Tecnologia”. FCT Portugal).National Funds through FCT-Foundation for Science and Technology under the Project UIDB/05183/202

Regional equivalent water thickness modeling from remote sensing across a tree cover/lai gradient in mediterranean forests of northern Tunisia

The performance of vegetation indexes derived from moderate resolution imaging spectroradiometer (MODIS) sensors is explored for drought monitoring in the forests of Northern Tunisia; representing a transition zone between the Mediterranean Sea and the Sahara Desert. We investigated the suitability of biomass and moisture vegetation indexes for vegetation water content expressed by the equivalent water thickness (EWT) in a Mediterranean forest ecosystem with contrasted water budgets and desiccation rates. We proposed a revised EWT at canopy level (EWTCAN) based on weekly field measurements of fuel moisture in seven species during the 2010 dry period, considering the mixture of plant functional types for water use (trees, shrubs and herbaceous layers) and a varying vegetation cover. MODIS vegetation indexes computed and smoothed over the dry season were highly correlated with the EWTCAN. The performances of moisture indexes Normalized Difference Infrared Index (NDII6 and NDII7) and Global Moisture Vegetation Index (GVMI6 and GVMI7) were comparable, whereas for biomass vegetation indexes, Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI) and Adjusted Normalized Difference Vegetation Index (ANDVI) performed better than Enhanced Vegetation Index (EVI) and Soil Adjusted Vegetation Index (SAVI). We also identified the effect of Leaf Area Index (LAI) on EWTCAN monitoring at the regional scale under the tree cover/LAI gradient of the region from relatively dense to open forest. Statistical analysis revealed a significant decreasing linear relationship; indicating that for LAI less than two, the greater the LAI, the less responsive are the vegetation indexes to changes in EWTCAN; whereas for higher LAI, its influence becomes less significant and was not considered in the inversion models based on vegetation indexes. The EWTCAN time-course from LAI-adapted inversion models based on significantly-related vegetation indexes to EWTCAN showed close profiles resulting from the inversion models using NDVI, ANDVI, MSAVI and NDII6 applied during the dry season. The developed EWTCAN model from MODIS vegetation indexes for the study region was finally tested for its ability to capture the topo-climatic effects on the seasonal and the spatial patterns of desiccation/rewetting for keystone periods of Mediterranean vegetation functioning. Implications for further use in scientific developments or management are discussed

Besoin en eau et rendements des céréales en Méditerranée du Sud : observation, prévision saisonnière et impact du changement climatique

Le secteur agricole est l'un des piliers de l'économie marocaine. En plus de contribuer à 15% au Produit Intérieur Brut (PIB) et de fournir 35% des opportunités d'emploi, il a un impact sur les taux de croissance. Ces dernières sont affectées négativement ou positivement par les conditions climatiques et la pluviométrie en particulier. Lors des années de sécheresse, caractérisées par une baisse de la production agricole, en particulier celle des céréales, la croissance de l'économie marocaine a été sévèrement affectée et les importations alimentaires du royaume ont augmenté de manière significative. Dans ce contexte, il est important d'évaluer l'impact de la sécheresse agricole sur les rendements céréaliers et de développer des modèles de prévision précoce des rendements, ainsi que de déterminer l'impact futur du changement climatique sur le rendement du blé et leurs besoins en eau. Le but de ce travail est, premièrement, d'approfondir la compréhension de la relation entre le rendement des céréales et la sécheresse agricole au Maroc. Afin de détecter la sécheresse, nous avons utilisé des indices de sécheresse agricole provenant de différentes données satellitaires. En outre, nous avons utilisé les sorties du système d'assimilation des données terrestres (LDAS). Deuxièmement, nous avons développé des modèles empiriques de la prévision précoce des rendements des céréales à l'échelle provinciale. Pour atteindre cet objectif, nous avons construit des modèles de prévision en utilisant des données multi-sources comme prédicteurs, y compris des indices basés sur la télédétection, des données météorologiques et des indices climatiques régionaux. Pour construire ces modèles, nous nous sommes appuyés sur des algorithmes de machine learning tels que : Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) et eXtreme Gradient Boost (XGBoost). Enfin, nous avons évalué l'impact du changement climatique sur le rendement du blé et ses besoins en eau. Pour ce faire, nous nous sommes appuyés sur cinq modèles climatiques régionaux disponibles dans la base de données Med-CORDEX sous deux scénarios RCP4.5 et RCP8.5, ainsi que sur le modèle AquaCrop et nous nous sommes basés sur trois périodes, la période de référence 1991-2010, la deuxième période 2041-2060 et la troisième période 2081-2100. Les résultats ont montré qu'il y a une corrélation étroite entre le rendement des céréales et les indices de sécheresse liés à l'état de végétation pendant le stade d'épiaison (mars et avril) et qui sont liés à la température de surface pendant le stade de développement en janvier-février, et qui sont liés à l'humidité du sol pendant le stade d'émergence en novembre-décembre. Les résultats ont également montré que les sorties du LDAS sont capables de suivre avec précision la sécheresse agricole. En ce qui concerne la prévision du rendement, les résultats ont montré que la combinaison des données provenant de sources multiples a donné des meilleurs résultats que les modèles basés sur une seule source. Dans ce contexte, le modèle XGBoost a été capable de prévoir le rendement des céréales dès le mois de janvier (environ quatre mois avant la récolte) avec des métriques statistiques satisfaisants (R² = 0.88 et RMSE = 0.22 t. ha^-1). En ce qui concerne l'impact du changement climatique sur le rendement et les besoins en eau du blé, les résultats ont montré que l'augmentation de la température de l'air entraînera un raccourcissement du cycle de croissance du blé d'environ 50 jours. Les résultats ont également montré une diminution du rendement du blé jusqu'à 30% si l'augmentation du CO2 n'est pas prise en compte. Cependant, l'effet de la fertilisation au CO2 peut compenser les pertes du rendement, et ce dernier peut augmenter jusqu'à 27%. Finalement, les besoins en eau devraient diminuer de 13 à 42%, et cette diminution est associée à une modification de calendrier d'irrigation, le pic des besoins arrivant deux mois plus tôt que dans les conditions actuelles.The agricultural sector is one of the pillars of the Moroccan economy. In addition to contributing 15% in GDP and providing 35% of employment opportunities, it has an impact on growth rates that are negatively or positively affected by climatic conditions and rainfall in particular. During drought years characterized by a decline in agricultural production and in particular cereal production, the growth of the Moroccan economy was severely affected and the kingdom's food imports increased significantly. In this context, it's important to assess the impact of agricultural drought on cereal yields and to develop early yield prediction models, as well as to determine the future impact of climate change on wheat yield and water requirements. The aim of this work is, firstly to further understand the linkage between cereal yield and agricultural drought in Morocco. In order to identify this drought, we used agricultural drought indices from remotely sensed satellite data. In addition, we used the outputs of Land Data Assimilation System (LDAS). Secondly, to develop empirical models for early prediction of cereal yields at provincial scale. To achieve this goal, we built forecasting models using multi-source data as predictors, including remote sensing-based indices, weather data and regional climate indices. And to build these models, we relied on machine learning algorithms such as Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boost (XGBoost). Finally, to evaluate the impact of climate change on the wheat yield its water requirements. To do this, we relied on five regional climate models available in the Med-CORDEX database under two scenarios RCP4.5 and RCP8.5, as well as the AquaCrop model and we based on three periods, the reference period 1991-2010, the second period 2041-2060 and the third period 2081-2100. The results showed that there is a close correlation between cereals yield and drought indices related to canopy condition during the heading stage (March and April) and which are related to surface temperature during the development stage in January -February, and which are related to soil moisture during the emergence stage in November -December. The results also showed that the outputs of LDAS are able to accurately monitor agricultural drought. Concerning, cereal yield forecasting, the results showed that combining data from multiple sources outperformed models based on one data set only. In this context, the XGBoost was able to predict cereal yield as early as January (about four months before harvest) with satisfactory statistical metrics (R² = 0.88 and RMSE = 0.22 t. ha^-1). Regarding the impact of climate change on wheat yield and water requirements, the results showed that the increase in air temperature will result in a shortening of the wheat growth cycle by about 50 days. The results also showed a decrease in wheat yield up to 30% if the rising in CO2 was not taken into account. The effect of fertilizing of CO2 can offset the yield losses, and yield can increase up to 27 %. Finally, water requirements are expected to decrease by 13 to 42%, and this decrease is associated with a change in temporal patterns, with the requirement peak coming two months earlier than under current conditions

Hyperspectral Reflectance as a Basis to Discriminate Olive Varieties—A Tool for Sustainable Crop Management

Worldwide sustainable development is threatened by current agricultural land change trends, particularly by the increasing rural farmland abandonment and agricultural intensification phenomena. In Mediterranean countries, these processes are affecting especially traditional olive groves with enormous socio-economic costs to rural areas, endangering environmental sustainability and biodiversity. Traditional olive groves abandonment and intensification are clearly related to the reduction of olive oil production income, leading to reduced economic viability. Most promising strategies to boost traditional groves competitiveness—such as olive oil differentiation through adoption of protected denomination of origin labels and development of value-added olive products—rely on knowledge of the olive varieties and its specific properties that confer their uniqueness and authenticity. Given the lack of information about olive varieties on traditional groves, a feasible and inexpensive method of variety identification is required. We analyzed leaf spectral information of ten Portuguese olive varieties with a powerful data-mining approach in order to verify the ability of satellite’s hyperspectral sensors to provide an accurate olive variety identification. Our results show that these olive varieties are distinguishable by leaf reflectance information and suggest that even satellite open-source data could be used to map them. Additional advantages of olive varieties mapping were further discussed

A study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time

Within-season forecasting of crop yields is of great economic, geo-strategic and humanitarian interest. Satellite Earth Observation now constitutes a valuable and innovative way to provide spatio-temporal information to assist such yield forecasts. This study explores different configurations of remote sensing time series to estimate of winter wheat yield using either spatially finer but temporally sparser time series (5daily at 100 m spatial resolution) or spatially coarser but denser (300 m and 1 km at daily frequency) time series. Furthermore, we hypothesised that better yield estimations could be made using thermal time, which is closer to the crop physiological development. Time series of NDVI from the PROBA-V instrument, which has delivered images at a spatial resolution of 100 m, 300 m and 1 km since 2013, were extracted for 39fields for field and 56fields for regional level analysis across Northern France during the growing season 2014-2015. An asymmetric double sigmoid model was fitted on the NDVI series of the central pixel of the field. The fitted model was subsequently integrated either over thermal time or over calendar time, using different baseline NDVI thresholds to mark the start and end of the cropping season. These integrated values were used as a predictor for yield using a simple linear regression and yield observations at field level. The dependency of this relationship on the spatial pixel purity was analysed for the 100 m, 300 m and 1 km spatial resolution. At field level, depending on the spatial resolution and the NDVI threshold, the adjustedR²ranged from 0.20 to 0.74; jackknifed–leave-one-field-outcross validation–RMSE ranged from 0.6 to 1.07 t/ha and MAE ranged between 0.46 and 0.90 t/ha for thermal time analysis. The best results for yield estimation (adjustedR²= 0.74, RMSE =0.6 t/ha and MAE =0.46 t/ha)were obtained from the integration over thermal time of 100 m pixel resolution using a baseline NDVI threshold of 0.2 and without any selection based on pixel purity. The field scale yield estimation was aggregated to the regional scale using 56fields. At the regional level, there was a difference of 0.0012 t/ha between thermal and calendar time for average yield estimations. The standard error of mean results showed that the error was larger for a higher spatial resolution with no pixel purity and smaller when purity increased. These results suggest that, for winter wheat, a finer spatial resolution rather than a higher revisit frequency and an increasing pixel purity enable more accurate yield estimations when integrated over thermal time at the field scale and at the regional scale only if higher pixel purity levels are considered. This method can be extended to larger regions, other crops, and other regions in the world, although site and crop-specific adjustments will have to include other threshold temperatures to reflect the boundaries of phenological activity. In general, however, this methodological approach should be applicable to yield estimation at the parcel and regional scales across the world