Wheat yield prediction in Andalucía using MERIS Terrestrial Chlorophyll Index (MTCI) time series

[EN] There is a relationship between net primary production of wheat and vegetation indices obtained from satellite imaging. Most wheat production studies use the Normalised Difference Vegetation Index (NDVI) to estimate the production and yield of wheat and other crops. On the one hand, few studies use the MERIS Terrestrial Chlorophyll Index (MTCI) to determine crop yield and production on a regional level. This is possibly due to a lack of continuity of MERIS. On the other hand, the emergence of Sentinel 2 open new possibilities for the research and application of MTCI. This study has built two empirical models to estimate wheat production and yield in Andalusia. To this end, the study used the complete times series (weekly images from 2006–2011) of the MTCI vegetation index from the Medium Resolution Imaging Spectrometer (MERIS) sensor associated with the Andalusian yearbook for agricultural and fishing statistics (AEAP—Anuario de estadísticas agrarias y pesqueras de Andalucía). In order to build these models, the optimal development period for the plant needed to be identified, as did the time-based aggregation of MTCI values using said optimal period as a reference, and relation with the index, with direct observations of production and yield through spatial aggregation using coverage from the Geographic Information System for Agricultural Parcels (SIGPAC—Sistema de información geográfica de parcelas agrícolas) and requests for common agricultural policy (CAP) assistance. The obtained results indicate a significant association between the MTCI index and the production and yield data collected by AEAP at the 95% confidence level (R2 =0.81 and R2 =0.57, respectively).[ES] Existe una relación entre la producción primaria neta del trigo y los índices de vegetación obtenidos de imágenes de satélite. Con frecuencia se utiliza el NDVI (Normalized Difference Vegetation Index) para la estimación de producción y rendimiento de trigo y otros cultivos. Sin embargo, hay pocas investigaciones que utilicen el índice MTCI (MERIS Terrestrial Chlorophyll Index) para conocer el rendimiento y la producción de los cultivos a una escala regional posiblemente debido a la falta de continuidad del sensor MERIS. No obstante, la posibilidad del cálculo de MTCI a partir de Sentinel 2 abre nuevas oportunidades para su aplicación e investigación. En esta investigación se han generado dos modelos empíricos de estimación de producción y rendimiento de trigo en Andalucía. Para ello, se ha empleado la serie temporal completa (imágenes semanales de 2006 a 2011) del índice de vegetación MTCI del sensor satelital MERIS (Medium Resolution Imaging Spectrometer) asociada a los datos de producción y rendimiento del Anuario de estadísticas agrarias y pesqueras de Andalucía (AEAP). Para la creación de estos modelos ha sido necesaria la identificación del periodo óptimo del desarrollo de la planta, la agregación temporal de los valores MTCI usando ese momento óptimo como referencia, relacionar ese índice con observaciones directas de producción y rendimiento a través de agregaciones espaciales mediante la utilización de coberturas SIGPAC y las solicitudes de ayudas PAC, caracterizar la variación del índice en función del año de cultivo y relacionarlo con los datos estadísticos. Los resultados obtenidos indican una correlación estadísticamente significativa (p-valor < 0,05) entre el índice MTCI y los datos de producción y rendimiento recogidos por AEAP (R2=0,81 y 0,57, respectivamente).Agradecemos la financiación obtenida de MINECO (Proyectos BIA2013-43462-P, CSO2014-51994-P) y de la Junta de Andalucía (Grupo Investigación RNM177).Egea-Cobrero, V.; Rodriguez-Galiano, V.; Sánchez-Rodríguez, E.; García-Pérez, M. (2018). Estimación de la cosecha de trigo en Andalucía usando series temporales de MERIS Terrestrial Chlorophyll Index (MTCI). Revista de Teledetección. (51):99-112. https://doi.org/10.4995/raet.2018.8891SWORD9911251Ahmed, B.M., Tanakamaru, H., Tada, A. 2010. Application of remote sensing for estimating crop water requirements, yield and water productivity of wheat in the Gezira Scheme. International Journal of Remote Sensing, 31(16), 4281-4294. https://doi.org/10.1080/01431160903246733Arévalo-Barroso, A. 1992. Atlas Nacional de España. Sección II. Grupo 9. Recuperado el 5 de noviembre de 2016, a partir de http://www.ign.es/ane/ane1986-2008/Asseng, S., Ewert, F., Martre, P., Rötter, R.P., Lobell, D.B., Cammarano, D., … Zhu, Y. 2014. Rising temperatures reduce global wheat production. Nature Climate Change, 5(2), 143-147. https://doi.org/10.1038/nclimate2470Atanasova, N., Todorovski, L., Džeroski, S., Kompare, B. 2008. Application of automated model discovery from data and expert knowledge to a real-world domain: Lake Glumsø. Ecological Modelling, 212(1-2), 92- 98. https://doi.org/10.1016/j.ecolmodel.2007.10.032Atzberger, C. 2013. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs. Remote Sensing, 5(2), 949-981. https://doi.org/10.3390/rs5020949Becker-Reshef, I., Justice, C., Sullivan, M., Vermote, E., Tucker, C., Anyamba, A., … Doorn, B. 2010. Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project. Remote Sensing, 2(6), 1589-1609. https://doi.org/10.3390/rs2061589Boissard, P., Guérif, M., Pointel, J.G., Guinot, J.P. 1989. Application of SPOT data to wheat yield estimation. Advances in Space Research, 9(1), 143-154. https://doi.org/10.1016/0273-1177(89)90479-1Boken, V.K., Shaykewich, C.F. 2002. Improving an operational wheat yield model using phenological phase-based Normalized Difference Vegetation Index. International Journal of Remote Sensing, 23(20), 4155-4168. https://doi.org/10.1080/014311602320567955CAPDER. 2012. Seguimiento de los mercados de cereales y oleaginosas. Consejería de Agricultura, Pesca y Desarrollo Rural (CAPDER). Secretaría General Técnica. Servicio de Publicaciones y Divulgación. Junta de Andalucía.CAPDER. 2015. Anuario de estadísticas agrarias y pesqueras de Andalucía. Recuperado a partir dehttp://www.juntadeandalucia.es/organismos/agriculturapescaydesarrollorural/consejeria/sobreconsejeria/estadisticas/paginas/agrarias-anuario. html [Último acceso: junio de 2018].Chahbi, A., Zribi, M., Lili-Chabaane, Z., Duchemin, B., Shabou, M., Mougenot, B., Boulet, G. 2014. Estimation of the dynamics and yields of cereals in a semi-arid area using remote sensing and the SAFY growth model. International Journal of Remote Sensing, 35(3), 1004-1028. https://doi.org/10.1080/ 01431161.2013.875629Curtis, T., Halford, N. G. 2014. Food security: the challenge of increasing wheat yield and the importance of not compromising food safety. Annals of Applied Biology, 164(3), 354-372. https://doi.org/10.1111/aab.12108Dash, J., Curran, P. 2007. Relationship between the MERIS vegetation indices and crop yield for the state of South Dakota, USA. Proc. Envisat Symposium.Dash, J., Jeganathan, C., Atkinson, P.M. 2010. The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India. Remote Sensing of Environment, 114(7), 1388-1402. https://doi.org/10.1016/j. rse.2010.01.021Dempewolf, J., Adusei, B., Becker-Reshef, I., Hansen, M., Potapov, P., Khan, A., Barker, B. 2014. Wheat yield forecasting for Punjab Province from vegetation index time series and historic crop statistics. Remote Sensing, 6(10), 9653-9675. https://doi.org/10.3390/rs6109653Dente, L., Satalino, G., Mattia, F., Rinaldi, M. 2008. Assimilation of leaf area index derived from ASAR and MERIS data into CERESWheat model to map wheat yield. Remote Sensing of Environment, 112(4), 1395-1407. https://doi.org/10.1016/j.rse.2007.05.023Duncan, J.M.A., Dash, J., Atkinson, P.M. 2015. Elucidating the impact of temperature variability and extremes on cereal croplands through remote sensing. Global change biology, 21(4), 1541-51. https://doi.org/10.1111/gcb.12660FAOSTAT. 2013. Productos agrícolas. Recuperado 17 de agosto de 2016, a partir de http://ec.europa.eu/eurostat/statistics-explained/index.php/Agricultural_products/es#Fuente_de_los_datos_de_las_tablas_y_los_gr.C3.A1ficos_.28MS_Excel.29Foley, J.A., Ramankutty, N., Brauman, K.A., Cassidy, E.S., Gerber, J.S., Johnston, M., … Zaks, D.P.M. 2011. Solutions for a cultivated planet. Nature, 478(7369), 337-342. https://doi.org/10.1038/ nature10452Fontana, D.C., Potgieter, A.B., Apan, A. 2007. Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images. Applied GIS, 3(7).Huang, J., Sedano, F., Huang, Y., Ma, H., Li, X., Liang, S., … Wu, W. 2016. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agricultural and Forest Meteorology, 216, 188-202. https://doi.org/10.1016/j.agrformet.2015.10.013Huang, J., Tian, L., Liang, S., Ma, H., Becker-Reshef, I., Huang, Y., … Wu, W. 2015. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology, 204, 106-121. https://doi. org/10.1016/j.agrformet.2015.02.001Huang, Y., Zhu, Y., Li, W. L., Cao, W. X., & Tian, Y. C. 2013. Assimilating remotely sensed information with the wheatgrow model based on the ensemble square root filter for improving regional wheat yield forecasts. Plant Production Science, 16(4), 352-364. https://doi.org/10.1626/pps.16.352ITACyL, AEMET, Consejería de Agricultura y Ganadería de la Junta de Castilla y León. 2016. Boletín de predicción de cosechas de Castilla y León. Recuperado 25 de octubre de 2016, a partir de https://cosechas.itacyl.es/es/inicioJégo, G., Pattey, E., Liu, J. 2012. Using Leaf Area Index, retrieved from optical imagery, in the STICS crop model for predicting yield and biomass of field crops. Field Crops Research, 131, 63-74. https://doi. org/10.1016/j.fcr.2012.02.012Johnson, M.D., Hsieh, W.W., Cannon, A.J., Davidson, A., Bédard, F. 2016. Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods. Agricultural and Forest Meteorology, 218-219, 74-84. https://doi.org/10.1016/j.agrformet.2015.11.003Kouadio, L., Duveiller, G., Djaby, B., El Jarroudi, M., Defourny, P., Tychon, B. 2012. Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data. International Journal of Applied Earth Observation and Geoinformation, 18(1), 111- 118. https://doi.org/10.1016/j.jag.2012.01.009Kowalik, W., Dabrowska-Zielinska, K., Meroni, M., Raczka, T.U., de Wit, A. 2014. Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries. International Journal of Applied Earth Observation and Geoinformation, 32(1), 228-239. https://doi.org/10.1016/j.jag.2014.03.011Kumar, M. 2016. Impact of climate change on crop yield and role of model for achieving food security. Environmental Monitoring and Assessment, 188(8), 1-14. https://doi.org/10.1007/s10661-016-5472-3Lobell, D.B., Schlenker, W., Costa-Roberts, J. 2011. Climate trends and global crop production since 1980. Science, 333(6042), 616-20. https://doi.org/10.1126/science.1204531MAGRAMA. 2015. Encuesta sobre Superficies y Rendimientos Cultivos (ESYRCE) de 2004 a 2015. Recuperado a partir de http://www.magrama. gob.es/es/estadistica/temas/estadisticas-agrarias/ agricultura/esyrce/resultados-de-anos-anteriores/ default.aspxMika, J., Kerényi, J., Rimóczi-Paál, A., Merza, Á., Szinell, C., Csiszár, I. 2002. On correlation of maize and wheat yield with NDVI: Example of Hungary (1985-1998). Advances in Space Research, 30(11), 2399-2404. https://doi.org/10.1016/S0273- 1177(02)80288-5Padilla, F.L.M., Maas, S.J., González-Dugo, M.P., Mansilla, F., Rajan, N., Gavilán, P., Domínguez, J. 2012. Monitoring regional wheat yield in Southern Spain using the GRAMI model and satellite imagery. Field Crops Research, 130, 145-154. https://doi.org/10.1016/j.fcr.2012.02.025Rawson, H.M., Macpherson, H.G. 2001. Trigo regado: manejo del cultivo. FAO.Reeves, M.C., Zhao, M., Running, S.W. 2005. Usefulness and limits on MODIS GPP for estimating wheat yield. International Journal of Remote Sensing, 26(7), 1403-1421. http://doi.org/10.1080/ 01431160512331326567Rembold, F., Atzberger, C., Savin, I., Rojas, O. 2013. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sensing, 5(4), 1704-1733. https://doi.org/10.3390/rs5041704Ren, J., Chen, Z., Zhou, Q., Tang, H. 2008. Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation, 10(4), 403- 413. https://doi.org/10.1016/j.jag.2007.11.003Salazar, L., Kogan, F., Roytman, L. 2007. Use of remote sensing data for estimation of winter wheat yield in the United States. International Journal of Remote Sensing, 28(17), 3795-3811. https://doi.org/10.1080/01431160601050395Tadesse, T., Senay, G.B., Berhan, G., Regassa, T., Beyene, S. 2015. Evaluating a satellitebased seasonal evapotranspiration product and identifying its relationship with other satellitederived products and crop yield: A case study for Ethiopia. International Journal of Applied Earth Observation and Geoinformation, 40, 39-54. http://doi.org/10.1016/j.jag.2015.03.006Vazifedoust, M., van Dam, J.C., Bastiaanssen, W.G. M., Feddes, R.A. 2009. Assimilation of satellite data into agrohydrological models to improve crop yield forecasts. International Journal of Remote Sensing, 30(10), 2523-2545. https://doi.org/10.1080/01431160802552769Wall, L., Larocque, D., Léger, P.M. 2008. The early explanatory power of NDVI in crop yield modelling. International Journal of Remote Sensing, 29(8), 2211-2225. https://doi.org/ 10.1080/01431160701395252Wu, C., Niu, Z., Tang, Q., Huang, W., Rivard, B., Feng, J. 2009. Remote estimation of gross primary production in wheat using chlorophyllrelated vegetation indices. Agricultural and Forest Meteorology, 149(6), 1015-1021. https://doi.org/10.1016/j.agrformet.2008.12.007Zhang, S., Liu, L. 2014. The potential of the MERIS Terrestrial Chlorophyll Index for crop yield prediction. Remote Sensing Letters, 5(8), 733-742. https://doi.org/10.1080/2150704X.2014.96373

Automated underwriting in life insurance: Predictions and optimisation

© Springer International Publishing AG, part of Springer Nature 2018. Underwriting is an important stage in the life insurance process and is concerned with accepting individuals into an insurance fund and on what terms. It is a tedious and labour-intensive process for both the applicant and the underwriting team. An applicant must fill out a large survey containing thousands of questions about their life. The underwriting team must then process this application and assess the risks posed by the applicant and offer them insurance products as a result. Our work implements and evaluates classical data mining techniques to help automate some aspects of the process to ease the burden on the underwriting team as well as optimise the survey to improve the applicant experience. Logistic Regression, XGBoost and Recursive Feature Elimination are proposed as techniques for the prediction of underwriting outcomes. We conduct experiments on a dataset provided by a leading Australian life insurer and show that our early-stage results are promising and serve as a foundation for further work in this space

Inter-comparison of satellite sensor land surface phenology and ground phenology in Europe

Land surface phenology (LSP) and ground phenology (GP) are both important sources of information for monitoring terrestrial ecosystem responses to climate changes. Each measures different vegetation phenological stages and has different sources of uncertainties, which make comparison in absolute terms challenging, and therefore, there has been limited attempts to evaluate the complementary nature of both measures. However, both LSP and GP are climate driven and therefore should exhibit similar interannual variation. LSP obtained from the whole time series of Medium-Resolution Imaging Spectrometer data was compared to thousands of deciduous tree ground phenology records of the Pan European Phenology network (PEP725). Correlations observed between the interannual time series of the satellite sensor estimates of phenology and PEP725 records revealed a close agreement (especially for Betula Pendula and Fagus Sylvatica species). In particular, 90% of the statistically significant correlations between LSP and GP were positive (mean R2 = 0.77). A large spatiotemporal correlation was observed between the dates of the start of season (end of season) from space and leaf unfolding (autumn coloring) at the ground (pseudo R2 of 0.70 (0.71)) through the application of nonlinear multivariate models, providing, for the first time, the ability to predict accurately the date of leaf unfolding (autumn coloring) across Europe (root-mean-square error of 5.97 days (6.75 days) over 365 days)

Characterizing degradation of palm swamp peatlands from space and on the ground: an exploratory study in the Peruvian Amazon

Peru has the fourth largest area of peatlands in the Tropics. Its most representative land cover on peat is a Mauritia flexuosa dominated palm swamp (thereafter called dense PS), which has been under human pressure over decades due to the high demand for the M. flexuosa fruit often collected by cutting down the entire palm. Degradation of these carbon dense forests can substantially affect emissions of greenhouse gases and contribute to climate change. The first objective of this research was to assess the impact of dense PS degradation on forest structure and biomass carbon stocks. The second one was to explore the potential of mapping the distribution of dense PS with different degradation levels using remote sensing data and methods. Biomass stocks were measured in 0.25 ha plots established in areas of dense PS with low (n = 2 plots), medium (n = 2) and high degradation (n = 4). We combined field and remote sensing data from the satellites Landsat TM and ALOS/PALSAR to discriminate between areas typifying dense PS with low, medium and high degradation and terra firme, restinga and mixed PS (not M. flexuosa dominated) forests. For this we used a Random Forest machine learning classification algorithm. Results suggest a shift in forest composition from palm to woody tree dominated forest following degradation. We also found that human intervention in dense PS translates into significant reductions in tree carbon stocks with initial (above and below-ground) biomass stocks (135.4 ± 4.8 Mg C ha−1) decreased by 11 and 17% following medium and high degradation. The remote sensing analysis indicates a high separability between dense PS with low degradation from all other categories. Dense PS with medium and high degradation were highly separable from most categories except for restinga forests and mixed PS. Results also showed that data from both active and passive remote sensing sensors are important for the mapping of dense PS degradation. Overall land cover classification accuracy was high (91%). Results from this pilot analysis are encouraging to further explore the use of remote sensing data and methods for monitoring dense PS degradation at broader scales in the Peruvian Amazon. Providing precise estimates on the spatial extent of dense PS degradation and on biomass and peat derived emissions is required for assessing national emissions from forest degradation in Peru and is essential for supporting initiatives aiming at reducing degradation activities

Andalusian multidecadal beach erosion dynamics

This is the raw dataset that has been used in the paper entitled &quot;A machine-learning hybrid-classification method for stratification of multidecadal beach dynamics&quot;THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Land cover change analysis of a Mediterranean area in Spain using different sources of data: multi-seasonal landsat images, land surface temperature, digital terrain models and texture

The main objective of this study was to apply a method for the mapping and analysis of land cover changes in a Mediterranean area in southern Spain (Granada Province). The province of Granada is a complex and very heterogeneous area made up of numerous land covers that are difficult to map due to spectral similarities.The inherent difficulty of the mapping of areas with the abovementioned characteristics was addressed by choosing a supervised classification algorithm called random forest and, in addition, by obtaining and incorporating new variables that allowed an improved land cover characterization: multi-seasonal spectral variables corresponding to different stages of land cover phenological development, variables linked to environmental gradients (digital terrain models and land surface temperature) and spatial variability structure textural measures. The same level of accuracy was obtained from the combined use of satellite images with digital terrain models or textural measures. However, the inclusion of the land surface temperature had a more moderate effect and only improved the mapping of some of the land-covers.A post-classification change analysis was conducted by comparing two supervised classifications obtained from the application of the classifier to a selection of different spectral, terrain and textural variables of images from 1998 and 2004. The global accuracy of the change map was 86% (matching percentage). The applied method resulted in a difference in the mapping accuracy of 33% in relation to the method based on the traditional maximum-likelihood classifier and considering only the spectral variables from the Landsat TM-5 satellite sensor

Evaluating the BFAST method to detect and characterise changing trends in water time series: A case study on the impact of droughts on the Mediterranean climate

Mediterranean climate regions are facing increased aridity conditions and water scarcity, thus needing integrated management of water resources. Detecting and characterising changes in water resources over time is the natural first step towards identifying the drivers of these changes and understanding the mechanism of change. The aim of this study is to evaluate the potential of Breaks For Additive Seasonal and Trend (BFAST) method to identify gradual (trend) and abrupt (step- change) changes in the freshwater resources time series over a long-term period. This research shows an alternative to the Pettitt's test, LOESS (locally estimated scatterplot smoothing) filter, Mann¿Kendall trend test among other common methods for change detection in hydrological data, and paves the way for further scientific investigation related to climate variability and its influence on water resources. We used the monthly accumulated stored water in three reservoirs, the monthly groundwater levels of three hydrological settings and a standardized precipitation index to show BFAST performance. BFAST was successfully applied, enabling: (1) assessment of the suitability of past management decisions when tackling drought events; (2) detection of recovery and drawdown periods (duration and magnitude values) of accumulated stored water in reservoirs and groundwater bodies after wet and dry periods; 3) measurement of resilience to drought conditions; (4) establishment of similarities/differences in trends between different reservoirs and groundwater bodies with regard to drought events

Yield Estimation of Wheat Using Cropland Masks from European Common Agrarian Policy: Comparing the Performance of EVI2, NDVI, and MTCI in Spanish NUTS-2 Regions

Monitoring wheat yield and production is essential for ensuring global food security. Remote sensing can be used to achieve it due to its ability to provide global, comprehensive, synoptic, and repetitive information in near real-time. This study used the 2006–2016 Normalized Difference Vegetation Index (NVDI) and Enhanced Vegetation Index 2 (EVI2) time series at a 250 m spatial resolution and 2006–2011 MERIS Terrestrial Chlorophyll Index (MTCI) time series at a 300 m spatial resolution. The post-maximum period for pixels containing wheat was selected based on the EU’s Common Agrarian Policy (CAP) and Corine Land Cover (CLC) data. It was correlated with yield and production values from governmental statistics (GS) of the largest Nomenclature of Territorial Units for Statistics level 2 (NUTS-2) wheat producers in Spain and for Spain overall. The selection of wheat masks was crucial for the accuracy of the models, with CAP masks offering greater forecasting capability. Models using CLC produced R2 values between 0.45 and 0.7, while those using CAP outperformed the former with R2 values of 0.9 throughout Spain. Production models outperformed yield models, and MTCI was the vegetation index (VI) that provided the greatest R2 value of 0.94. However, model accuracy was heavily conditioned by the precision of input data, where anomalies were detected in some NUTS-2

Photoperiod controls vegetation phenology across Africa

Vegetation phenology is driven by environmental factors such as photoperiod, precipitation, temperature, insolation, and nutrient availability. However, across Africa, there’s ambiguity about these drivers, which can lead to uncertainty in the predictions of global warming impacts on terrestrial ecosystems and their representation in dynamic vegetation models. Using satellite data, we undertook a systematic analysis of the relationship between phenological parameters and these drivers. The analysis across different regions consistently revealed photoperiod as the dominant factor controlling the onset and end of vegetation growing season. Moreover, the results suggest that not one, but a combination of drivers control phenological events. Consequently, to enhance our predictions of climate change impacts, the role of photoperiod should be incorporated into vegetation-climate and ecosystem modelling. Furthermore, it is necessary to define clearly the responses of vegetation to interactions between a consistent photoperiod cue and inter-annual variation in other drivers, especially under a changing climate