An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data

AbstractWe propose a simple, spatially invariant and probabilistic year-round Empirical Standardized Soil Moisture Index (ESSMI) that is designed to classify soil moisture anomalies from harmonized multi-satellite surface data into categories of agricultural drought intensity. The ESSMI is computed by fitting a nonparametric empirical probability density function (ePDF) to historical time-series of soil moisture observations and then transforming it into a normal distribution with a mean of zero and standard deviation of one. Negative standard normal values indicate dry soil conditions, whereas positive values indicate wet soil conditions. Drought intensity is defined as the number of negative standard deviations between the observed soil moisture value and the respective normal climatological conditions. To evaluate the performance of the ESSMI, we fitted the ePDF to the Essential Climate Variable Soil Moisture (ECV SM) v02.0 data values collected in the period between January 1981 and December 2010 at South–Central America, and compared the root-mean-square-errors (RMSE) of residuals with those of beta and normal probability density functions (bPDF and nPDF, respectively). Goodness-of-fit results attained with time-series of ECV SM values averaged at monthly, seasonal, half-yearly and yearly timescales suggest that the ePDF provides triggers of agricultural drought onset and intensity that are more accurate and precise than the bPDF and nPDF. Furthermore, by accurately mapping the occurrence of major drought events over the last three decades, the ESSMI proved to be spatio-temporal consistent and the ECV SM data to provide a well calibrated and homogenized soil moisture climatology for the region. Maize, soybean and wheat crop yields in the region are highly correlated (r>0.82) with cumulative ESSMI values computed during the months of critical crop growing, indicating that the nonparametric index of soil moisture anomalies can be used for agricultural drought assessment

Detección de estrés hídrico en olivar mediante datos hiperespectrales y térmicos del sensor AHS

El sensor hiperespectral AHS (Airborrne Hyperspectal Scanner) fue utilizado para obtener imágenes de 2.5 m de resolución espacial en el espectro visible, infrarrojo cercano y térmico en una parcela de olivar en Córdoba (España) con el fin de estudiar la variabilidad espacial y temporal del estrés hídrico. Los datos térmicos del AHS permitieron obtener imágenes de temperatura de superficie de la parcela a las 7:30, 9:30 y 12:30 GMT el 25 de julio de 2004. EL diseño experimental en bloques aleatorios consistió en aplicar tres dosis diferentes de riego durante julio, agosto y septiembre, realizando medidas semanales de potencial hídrico, fotosíntesis y conductancia para estudiar los efectos del estrés hídrico en el cultivo. Los sensores de infrarrojo IRT permitieron la realización de medidas continuas de temperatura sobre las copas de los árboles, facilitando la validación de las imágenes térmicas. Los resultados de este estudio son presentados, destacando la aplicabilidad en la agricultura de precisión de la teledetección térmica e hiperespectral de alta resolución espacial para el estudio del suministro y la dosificación del riego.The Airborne Hyperspectral Scanner (AHS) was used to acquire images with 2.5 m spatiala resolution in the visible, near infrared and thermal spectral regions over an olive orchard in Cordoba (Spain) to study the spatial and temporal variability of water stress. The AHS thermal information enabled obtaining surface temperature images of the orchard at 7:30, 9:30 and 12:30 GMT in 25 july 2004. The experimental design consisted of applying three different irrigation treatments in randomly selected blocks during july, august and septemper, acquiring measurements of leaf water potential, stomatal conductance and photosynthesis to study the water stress effects on the trees. Infrared sensors IRT placed on top of the trees allowed to obtain continuously temperature measurements, providing validation data for the airborne thermal imagery. Results of this study are presented, suggesting that hyperspectral and high resolution remote sensing methods have important applicability in precision agriculture for management of controlled deficit irrigation method

Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2

Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10–20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the “Sentinel­2 for Agriculture” project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of “Sentinel-2 like” time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the “Joint Experiment for Crop Assessment and Monitoring” network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future “Sentinel­2 for Agriculture” system.Instituto de Clima y AguaFil: Bontemps, Sophie. Université Catholique de Louvain. Earth and Life Institute; BélgicaFil: Arias, Marcela. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Cara, Cosmin. CS Romania S.A.; RumaniaFil: Dedieu, Gérard. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Guzzonato, Eric. CS Systèmes d’Information; FranciaFil: Hagolle, Olivier. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Inglada, Jordi. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Matton, Nicolas. Université Catholique de Louvain. Earth and Life Institute; BélgicaFil: Morin, David. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Popescu, Ramona. CS Romania S.A.; RumaniaFil: Rabaute, Thierry. CS Systèmes d’Information; FranciaFil: Savinaud, Mickael. CS Systèmes d’Information; FranciaFil: Sepulcre, Guadalupe. Université Catholique de Louvain. Earth and Life Institute; BélgicaFil: Valero, Silvia. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Ahmad, Ijaz. Pakistan Space and Upper Atmosphere Research Commission. Space Applications Research Complex. National Agriculture Information Center Directorate; PakistánFil: Bégué, Agnès. Centre de Coopération Internationale en Recherche Agronomique pour le Développerment; FranciaFil: Wu, Bingfang. Chinese Academy of Sciences. Institute of Remote Sensing and Digital Earth; República de ChinaFil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Diarra, Alhousseine. Université Cadi Ayyad. Faculté des Sciences Semlalia; MarruecosFil: Dupuy, Stéphane. Centre de Coopération Internationale en Recherche Agronomique pour le Développerment; FranciaFil: French, Andrew. United States Department of Agriculture. Agricultural Research Service. Arid Land Agricultural Research Center; ArgentinaFil: Akhtar, Ibrar ul Hassan. Pakistan Space and Upper Atmosphere Research Commission. Space Applications Research Complex. National Agriculture Information Center Directorate; PakistánFil: Kussul, Nataliia. National Academy of Sciences of Ukraine. Space Research Institute and State Space Agency of Ukraine; UcraniaFil: Lebourgeois, Valentine. Centre de Coopération Internationale en Recherche Agronomique pour le Développerment; FranciaFil: Le Page, Michel. Université Cadi Ayyad. Faculté des Sciences Semlalia. Laboratoire Mixte International TREMA; Marruecos. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Newby, Terrence. Agricultural Research Council; SudáfricaFil: Savin, Igor. V.V. Dokuchaev Soil Science Institute; RusiaFil: Verón, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Koetz, Benjamin. European Space Agency. European Space Research Institute; ItaliaFil: Defourny, Pierre. Université Catholique de Louvain. Earth and Life Institute; Bélgic

Automated annual cropland mapping using knowledge-based temporal features

Global, timely, accurate and cost-effective cropland mapping is a prerequisite for reliable crop condition monitoring. This article presented a simple and comprehensive methodology capable to meet the requirements of operational cropland mapping by proposing (1) five knowledge-based temporal features that remain stable over time, (2) a cleaning method that discards misleading pixels from a baseline land cover map and (3) a classifier that delivers high accuracy cropland maps (>80%). This was demonstrated over four contrasted agrosystems in Argentina, Belgium, China and Ukraine. It was found that the quality and accuracy of the baseline impact more the certainty of the classification rather than the classification output itself. In addition, it was shown that interpolation of the knowledge-based features increases the stability of the classifier allowing for its re-use from year to year without recalibration. Hence, the method shows potential for application at larger scale as well as for delivering cropland map in near real time

Agricultural Drought Assessment in Latin America Based on a Standardized Soil Moisture Index

We propose a relatively simple, spatially invariant and probabilistic year-round Standardized Soil Moisture Index (SSMI) that is designed to estimate drought conditions from satellite imagery data. The SSMI is based on soil moisture content alone and is defined as the number of standard deviations that the observed moisture at a given location and timescale deviates from the longterm normal conditions. Specifically, the SSMI is computed by fitting a non-parametric probability distribution function to historical soil moisture records and then transforming it into a normal distribution with a mean of zero and standard deviation of one. Negative standard normal values indicate dry conditions and positive values indicate wet conditions. To evaluate the applicability of the SSMI, we fitted empirical and normal cumulative distribution functions (ECDF and nCDF) to 32-years of averaged soil moisture amounts derived from the Essential Climate Variable (ECV) Soil Moisture (SM) dataset, and compared the root-mean-squared errors of residuals. SM climatology was calculated on a 0:25 grid over Latin America at timescales of 1, 3, 6, and 12 months for the long-term period of 1979-2010. Results show that the ECDF fits better the soil moisture data than the nCDF at all timescales and that the negative SSMI values computed with the non-parametric estimator accurately identified the temporal and geographic distribution of major drought events that occurred in the study area.JRC.H.7-Climate Risk Managemen

Assessment of the EUMETSAT LSA-SAF evapotranspiration product for drought monitoring in Europe

tEvapotranspiration is a key parameter for water stress assessment as it is directly related to the moisturestatus of the soil-vegetation system and describes the moisture transfer from the surface to the atmo-sphere. With the launch of the Meteosat Second Generation geostationary satellites and the setup ofthe Satellite Application Facilities, it became possible to operationally produce evapotranspiration datawith high spatial and temporal evolution over the entire continents of Europe and Africa. In the frameof this study we present an evaluation of the potential of the evapotranspiration (ET) product from theEUMETSAT Satellite Application Facility on Land Surface Analysis (LSA-SAF) for drought assessment andmonitoring in Europe.To assess the potential of this product, the LSA-SAF ET was used as input for the ratio of ET to referenceevapotranspiration (ET0), the latter estimated from the ECMWF interim reanalysis. In the analysis twocase studies were considered corresponding to the drought episodes of spring/summer 2007 and 2011.For these case studies, the ratio ET/ET0was compared with meteorological drought indices (SPI, SPEI andSc-PDSI for 2007 and SPI for 2011) as well as with the anomalies of the fraction of absorbed photosyn-thetic active radiation (fAPAR) derived from remote sensing data. The meteorological and remote sensingindicators were taken from the European Drought Observatory (EDO) and the CARPATCLIM climatologicalatlas.Results show the potential of ET/ET0to characterize soil moisture variability, and to give additionalinformation to fAPAR and to precipitation distribution for drought assessment. The main limitations ofthe proposed ratio for drought characterization are discussed, including options to overcome them. Theseoptions include the use of filters to discriminate areas with a low percentage vegetation cover or areasthat are not in their growing period and the use of evapotranspiration without water restriction (ETwwr),obtained as output of the LSA-SAF model instead of ET0. The ET/ETwwrratio was tested by comparingits accumulated values per growing period with the winter wheat yield values per country published byEurostat. The results point to the potential of using the remote sensing based LSA-SAF evapotranspirationand the ET/ETwwrratio for vegetation monitoring at large scale, especially in areas where data is generallylackin

Assessment of the EUMETSAT LSA-SAF evapotranspiration product for drought monitoring in Europe

Evapotranspiration is a key parameter for water stress assessment as it is directly related to the moisture status of the soil-vegetation system and describes the moisture transfer from the surface to the atmosphere. With the launch of the Meteosat Second Generation geostationary satellites and the setup of the Satellite Application Facilities, it became possible to operationally produce evapotranspiration data with high spatial and temporal evolution over the entire continents of Europe and Africa. In the frame of this study we present an evaluation of the potential of the evapotranspiration (ET) product from the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA-SAF) for drought assessment and monitoring in Europe. To assess the potential of this product, the LSA-SAF ET was used as input for the ratio of ET to reference evapotranspiration (ET0), the latter estimated from the ECMWF interim reanalysis. In the analysis two case studies were considered corresponding to the drought episodes of spring/summer 2007 and 2011. For these case studies, the ratio ET/ET0 was compared with meteorological drought indices (SPI, SPEI and Sc-PDSI for 2007 and SPI for 2011) as well as with the anomalies of the fraction of absorbed photosynthetic active radiation (fAPAR) derived from remote sensing data. The meteorological and remote sensing indicators were taken from the European Drought Observatory (EDO) and the CARPATCLIM climatological atlas. Results show the potential of ET/ET0 to characterize soil moisture variability, and to give additional information to fAPAR and to precipitation distribution for drought assessment. The main limitations of the proposed ratio for drought characterization are discussed, including options to overcome them. These options include the use of filters to discriminate areas with a low percentage vegetation cover or areas that are not in their growing period and the use of evapotranspiration without water restriction (ETwwr), obtained as output of the LSA-SAF model instead of ET0. The ET/ ETwwr ratio was tested by comparing its accumulated values per growing period with the winter wheat yield values per country published by Eurostat. The results point to the potential of using the remote sensing based LSA-SAF evapotranspiration and the ET/ ETwwr ratio for vegetation monitoring at large scale, especially in areas where data is generally lacking.JRC.H.7-Climate Risk Managemen

A multitemporal and non-parametric approach for assessing the impacts of drought on vegetation greenness: A case study for Latin America

This study evaluates the relationship between the frequency and duration of meteorological droughts and the subsequent temporal changes on the quantity of actively photosynthesizing biomass (greenness) estimated from satellite imagery on rainfed croplands in Latin America. An innovative non-parametric and non-supervised approach, based on the Fisher-Jenks optimal classification algorithm, is used to identify multi-scale meteorological droughts on the basis of empirical cumulative distributions of 1, 3, 6, and 12-monthly precipitation totals. As input data for the classifier, we use the gridded GPCC Full Data Reanalysis precipitation time-series product, which ranges from January 1901 to December 2010 and is interpolated at the spatial resolution of 1° (decimal degree, DD). Vegetation greenness composites are derived from 10-daily SPOT-VEGETATION images at the spatial resolution of 1/112° DD for the period between 1998 and 2010. The time-series analysis of vegetation greenness is performed during the growing season with a non-parametric method, namely the seasonal Relative Greenness (RG) of spatially accumulated fAPAR. The Global Land Cover map of 2000 and the GlobCover maps of 2005/2006 and 2009 are used as reference data to select study cases only on geographic areas that did not undergo land cover changes during the analysis period. The multi-scale information is integrated at the lowest spatial resolution available, i.e. 1° DD, and the impacts of meteorological drought episodes on seasonal greenness of rainfed crops are assessed at the regional scale. Final results suggest that the agricultural cycle at the regional scale is more correlated with long-standing and uninterrupted small timescale drought conditions that occur prior to vegetation growing season than with isolated and short long-term timescale drought events.JRC.H.7-Climate Risk Managemen

Combining remote sensing imagery of both fine and coarse spatial resolution to estimate crop evapotranspiration and quantifying its influence on crop growth monitoring

peer reviewedThis study has been carried out in the framework of the GLOBAM -Global Agricultural Monitoring system by integration of earth observation and modeling techniques- project whose objective is to fill the methodological gap between the state of the art of local crop monitoring and the operational requirements of the global monitoring system programs. To achieve this goal, the research aims to develop an integrated approach using remote sensing and crop growth modeling. This paper concerns the use of MSG geostationnary satellite data for the calculation of Actual Evapotranspiration and its integration into a crop growth model