scholarly journals The Effect of Droughts on Vegetation Condition in Germany: An Analysis Based on Two Decades of Satellite Earth Observation Time Series and Crop Yield Statistics

2019 ◽  
Vol 11 (15) ◽  
pp. 1783 ◽  
Author(s):  
Sophie Reinermann ◽  
Ursula Gessner ◽  
Sarah Asam ◽  
Claudia Kuenzer ◽  
Stefan Dech
Keyword(s):  
Time Series ◽  
Spatial Pattern ◽  
Crop Yield ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Earth Observation ◽  
Observation Data ◽  
Temporal Development ◽  
Vegetation Condition ◽  
Temporal And Spatial

Central Europe experienced several droughts in the recent past, such as in the year 2018, which was characterized by extremely low rainfall rates and high temperatures, resulting in substantial agricultural yield losses. Time series of satellite earth observation data enable the characterization of past drought events over large temporal and spatial scales. Within this study, Moderate Resolution Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) 250 m time series were investigated for the vegetation periods of 2000 to 2018. The spatial and temporal development of vegetation in 2018 was compared to other dry and hot years in Europe, like the drought year 2003. Temporal and spatial inter- and intra-annual patterns of EVI anomalies were analyzed for all of Germany and for its cropland, forest, and grassland areas individually. While vegetation development in spring 2018 was above average, the summer months of 2018 showed negative anomalies in a similar magnitude as in 2003, which was particularly apparent within grassland and cropland areas in Germany. In contrast, the year 2003 showed negative anomalies during the entire growing season. The spatial pattern of vegetation status in 2018 showed high regional variation, with north-eastern Germany mainly affected in June, north-western parts in July, and western Germany in August. The temporal pattern of satellite-derived EVI deviances within the study period 2000–2018 were in good agreement with crop yield statistics for Germany. The study shows that the EVI deviation of the summer months of 2018 were among the most extreme in the study period compared to other years. The spatial pattern and temporal development of vegetation condition between the drought years differ.

Joint multifractal approach to characterize nonlinear relationships of climate and cereal growth in semiarid

2021 ◽  
Author(s):  
David Rivas-Tabares ◽  
Ana María Tarquis Alfonso
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Complex Networks ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Observation Data ◽  
Central Spain ◽  
Nonlinear Relationships ◽  
Fractal Properties

<p>Rainfed crops as cereals in the semiarid are common and extensive land cover in which climate, soils and atmosphere interact trough nonlinear relationships. Earth Observations coupled to ground monitoring network allow to improve the understanding of these relationships during each cropping season. However, novel analysis is required to understand these relationships in larger periods to improve sustainability and suitability of the productive areas in the semiarid.</p><p>The aim of this work is to use a joint multifractal approach using vegetation indices, precipitation, and temperatures to analyze atmosphere-plant nonlinear relationships. For this, time series of 20 cropping seasons were used to characterize these relationships in central Spain. The Generalized Structure Function and the derived Generalized Hurst Exponent analysis were implemented to investigate precipitation, vegetation indices and temperature time series. For this, an exhaustive selection based on land use and a land cover change analysis was performed to detect plots in which cereal crop sequences are dedicated to barley and wheat over the period 2000 to 2020.</p><p>As a result, two agro zones were characterized by different multifractal properties. Precipitation series show antipersistent characteristics and fractal properties between zones while original vegetation indices show trending behavior but shifted between analyzed zones. Nonetheless, soils and rainfall events can vary interannual conditions in which the crop is developing. For vegetation indices long-term series the trend is persistent. Even so, the dynamics of vegetation indices also provide more information when annual patterns are extracted from the series, exhibiting fractal properties mainly from rainfall pattern of each zone. Finally, in this case, the joint multifractal analysis served to characterize agro zones using earth observation and climate data for extensive cereals in Central Spain.</p><p><strong>Reference</strong></p><p>Rivas-Tabares D., Tarquis A.M. (2021) Towards Understanding Complex Interactions of Normalized Difference Vegetation Index Measurements Network and Precipitation Gauges of Cereal Growth System. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_51</p><p><strong>Acknowledgements</strong></p><p>The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330 and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020.</p>

scholarly journals Earth Observation Data for Agricultural Drought Monitoring in the Pannonian Basin

2020 ◽  
Author(s):  
Laura Crocetti ◽  
Milan Fischer ◽  
Matthias Forkel ◽  
Aleš Grlj ◽  
Wai-Tim Ng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Pannonian Basin ◽  
Spatial Scales ◽  
European Space Agency ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Observation Data

<p>The Pannonian Basin is a region in the southeastern part of Central Europe that is heavily used for agricultural purposes. It is geomorphological defined as the plain area that is surrounded by the Alps in the west, the Dinaric Alps in the Southwest, and the Carpathian mountains in the North, East and Southeast. In recent decades, the Pannonian Basin has experienced several drought episodes, leading to severe impacts on the environment, society, and economy. Ongoing human-induced climate change, characterised by increasing temperature and potential evapotranspiration as well as changes in precipitation distribution will further exacerbate the frequency and intensity of extreme events. Therefore, it is important to monitor, model, and forecast droughts and their impact on the environment for a better adaption to the changing weather and climate extremes. The increasing availability of long-term Earth observation (EO) data with high-resolution, combined with the progress in machine learning algorithms and artificial intelligence, are expected to improve the drought monitoring and impact prediction capacities.</p><p>Here, we assess novel EO-based products with respect to drought processes in the Pannonian Basin. To identify meteorological and agricultural drought, the Standardized Precipitation-Evapotranspiration Index was computed from the ERA5 meteorological reanalysis and compared with drought indicators based on EO time series of soil moisture and vegetation like the Soil Water Index or the Normalized Difference Vegetation Index. We suggest that at resolution representing the ERA5 reanalysis (~0.25°) or coarser, both meteorological as well as EO data can identify drought events similarly well. However, at finer spatial scales (e.g. 1 km) the variability of biophysical properties between fields cannot be represented by meteorological data but can be captured by EO data. Furthermore, we analyse historical drought events and how they occur in different EO datasets. It is planned to enhance the forecasting of agricultural drought and estimating drought impacts on agriculture through exploiting the potential of EO soil moisture and vegetation data in a data-driven machine learning framework.</p><p>This study is funded by the DryPan project of the European Space Agency (https://www.eodc.eu/esa-drypan/).</p>

scholarly journals Correlating the Plant Height of Wheat with Above-Ground Biomass and Crop Yield Using Drone Imagery and Crop Surface Model, A Case Study from Nepal

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 28 ◽  
Author(s):  
Uma Shankar Panday ◽  
Nawaraj Shrestha ◽  
Shashish Maharjan ◽  
Arun Kumar Pratihast ◽  
Shahnawaz ◽  
...  
Keyword(s):  
Food Security ◽  
Plant Height ◽  
Crop Yield ◽  
Earth Observation ◽  
Wheat Crop ◽  
Surface Model ◽  
Observation Data ◽  
Land Resources ◽  
Above Ground Biomass ◽  
Ground Biomass

Food security is one of the burning issues in the 21st century, as a tremendous population growth over recent decades has increased demand for food production systems. However, agricultural production is constrained by the limited availability of arable land resources, whereas a significant part of these is already degraded due to overexploitation. In order to get optimum output from the available land resources, it is of prime importance that crops are monitored, analyzed, and mapped at various stages of growth so that the areas having underdeveloped/unhealthy plants can be treated appropriately as and when required. This type of monitoring can be performed using ultra-high-resolution earth observation data like the images captured through unmanned aerial vehicles (UAVs)/drones. The objective of this research is to estimate and analyze the above-ground biomass (AGB) of the wheat crop using a consumer-grade red-green-blue (RGB) camera mounted on a drone. AGB and yield of wheat were estimated from linear regression models involving plant height obtained from crop surface models (CSMs) derived from the images captured by the drone-mounted camera. This study estimated plant height in an integrated setting of UAV-derived images with a Mid-Western Terai topographic setting (67 to 300 m amsl) of Nepal. Plant height estimated from the drone images had an error of 5% to 11.9% with respect to direct field measurement. While R2 of 0.66 was found for AGB, that of 0.73 and 0.70 were found for spike and grain weights respectively. This statistical quality assurance contributes to crop yield estimation, and hence to develop efficient food security strategies using earth observation and geo-information.

scholarly journals Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

2014 ◽  
Vol 11 (16) ◽  
pp. 4305-4320 ◽  
Author(s):  
S. T. Klosterman ◽  
K. Hufkens ◽  
J. M. Gray ◽  
E. Melaas ◽  
O. Sonnentag ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Curve Fitting ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Spatial Scales ◽  
Visual Assessment ◽  
Statistical Uncertainty ◽  
Near Surface

Abstract. Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of fall, can be derived from sensor-based time series, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, and to perform digital image analysis for time-series-based estimation of phenophase transition dates. We then compare these results to remote sensing metrics of phenophase transition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to estimate phenology dates, and we quantify the statistical uncertainty of phenophase transition dates estimated using this method. Results show that the generalized sigmoid provides estimates of dates with less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing metrics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates derived from visual assessment of leaf-out, as well as satellite remote-sensing-derived estimates of the start of spring. However late spring and fall phenology metrics exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.

Improving the analysis of the soil-plant-atmosphere system thought earth observations in large monocrop cereal sequences

2020 ◽  
Author(s):  
David Rivas-Tabares ◽  
Juan J. Martín-Sotoca ◽  
Antonio Saa-Requejo ◽  
Ana María Tarquis
Keyword(s):  
Time Series ◽  
Soil Properties ◽  
Vegetation Index ◽  
Crop Yields ◽  
Observation Data ◽  
Precipitation Regime ◽  
Agricultural Insurance ◽  
Residual Time ◽  
Earth Observations ◽  
Atmosphere System

<p>Crop yields of rainfed cereal are highly dependent of the soil-plant-atmosphere system, especially referred to the weather conditions and soil properties. The study of this interaction is feasible through the earth observations of historical data. Remote sensing data and agricultural survey work together identifying and analyzing plots with monocrop cereal sequences. In this research, we investigate the relation of the Normalized Difference Vegetation Index (NDVI) residual time series behavior relative to soil classes from Self-Organizing Maps (SOM) and the precipitation residual time series.</p><p>The midlands of Eresma-Adaja watershed (Dueros’ River basin, Spain) is historically depicted to rainfed cereal agriculture, some evidence of monocropping sequences are worrisome the water availability in the area. Within this area, two contrasting soil properties sites were selected to assess plots with at least 20 years of rainfed monocropping sequences but under similar weather regime. This allows analyzing the effect and relationships of this practice by soil type in time. For this, we treat the NDVI and precipitation time residual series as signals. The use of the Generalized Structure Function applied to these residual time series and the Hurst exponent, serve to confirm the soil properties differences from SOM and to reinforce the scaling properties of soil-climate interaction in semiarid regions for cereals in monocrop. As a result, the NDVI and precipitation series present an antipersistence behavior supporting that precipitation regime is influencing as the same manner the NDVI residual time series among complimentary factors.</p><p><strong>ACKNOWLEDGEMENTS</strong></p><p>Finding for this work was partially provided by Boosting agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020. The authors also acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish <em>Ministerio de Ciencia Innovación y Universidades</em> of Spain. The data provided by ITACyL and AEMET is greatly appreciated.</p><p> </p>

scholarly journals Evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and other satellite-aided retrieval models

2017 ◽  
Author(s):  
Sina C. Truckenbrodt ◽  
Christiane C. Schmullius
Keyword(s):  
Reference Data ◽  
Spatial Scales ◽  
Test Site ◽  
Earth Observation ◽  
Reference Database ◽  
Observation Data ◽  
Monitoring Methods ◽  
Retrieval Models ◽  
In Situ Data

Abstract. Ground reference data are a prerequisite for the calibration, update and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was elaborated to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS). In situ data was collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data was gathered with an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT5, Landsat-7 and -8) with a cloud coverage ≤ 25 % is available for 10 and 16 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. In the article, the experimental design of the field campaigns, and methods employed in the determination of all parameters are described in detail. Insights into the database are provided and potential fields of application are discussed. We hope these data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (doi:10.1594/PANGAEA.874251).

scholarly journals Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of earth observation data

2015 ◽  
Vol 12 (11) ◽  
pp. 11847-11903 ◽  
Author(s):  
V. Heimhuber ◽  
M. G. Tulbure ◽  
M. Broich
Keyword(s):  
Time Series ◽  
Surface Water ◽  
River Basin ◽  
Earth Observation ◽  
Water Dynamics ◽  
Observation Data ◽  
Grid Cells ◽  
Modeling Framework ◽  
Basin Scale ◽  
Lag Times

Abstract. The usage of time series of earth observation (EO) data for analyzing and modeling surface water dynamics (SWD) across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWD from a unique validated Landsat-based time series (1986–2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray–Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWD as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET) and rainfall (P). To enable a consistent modeling approach across space, we modeled SWD separately for hydrologically distinct floodplain, floodplain-lake and non-floodplain areas within eco-hydrological zones and 10 km × 10 km grid cells. We applied this spatial modeling framework (SMF) to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWD. Based on these automatically quantified flow lag times and variable combinations, SWD on 233 (64 %) out of 363 floodplain grid cells were modeled with r2 ≥ 0.6. The contribution of P, ET and SM to the models' predictive performance differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The SMF presented here is suitable for modeling SWD on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.

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