Prediction of topsoil properties at field-scale by using C-band SAR data

Designing and validating digital soil mapping (DSM) techniques can facilitate precision agriculture implementation. This study generates and validates a technique for the spatial prediction of soil properties based on C-band radar data. To this end, (i) we focused on working at farm-field scale and conditions, a fact scarcely reported; (ii) we validated the usefulness of Random Forest regression (RF) to predict soil properties based on C-band radar data; (iii) we validated the prediction accuracy of C-band radar data according to the coverage condition (for example: crop or fallow); and (iv) we aimed to find spatial relationship between soil apparent electrical conductivity and C-band radar. The experiment was conducted on two agricultural fields in the southern Argentine Pampas. Fifty one Sentinel 1 Level-1 GRD (Grid) products of C-band frequency (5.36 GHz) were processed. VH and VV polarizations and the dual polarization SAR vegetation index (DPSVI) were estimated. Soil information was obtained through regular-grid sample scheme and apparent soil electrical conductivity (ECa) measurements. Soil properties predicted were: texture, effective soil depth, ECa at 0-0.3m depth and ECa at 0-0.9m depth. The effect of water, vegetation and soil on the depolarization from SAR backscattering was analyzed. Complementary, spatial predictions of all soil properties from ordinary cokriging and Conditioned Latin hypercube sampling (cLHS) were evaluated using six different soil sample sizes: 20, 40, 60, 80, 100 and the total of the grid sampling scheme. The results demonstrate that the prediction accuracy of C-band SAR data for most of the soil properties evaluated varies considerably and is closely dependent on the coverage type and weather dynamics. The polarizations with high prediction accuracy of all soil properties showed low values of σVVo and σVHo, while those with low prediction accuracy showed high values of σVVo and low values of σVHo. The spatial patterns among maps of all soil properties using all samples and all sample sizes were similar. In conditions when summer crops demand large amount of water and there is soil water deficit backscattering showed higher prediction accuracy for most soil properties. During the fallow season, the prediction accuracy decreased and the spatial prediction accuracy was closely dependent on the number of validation samples. The findings of this study corroborates that DSM techniques at field scale can be achieved by using C-band SAR data. Extrapolation y applicability of this study to other areas remain to be tested.EEA BalcarceFil: Domenech, Marisa. Universidad Nacional del Sur. Departamento de Agronomía; Argentina.Fil: Amiottia, Nilda. Universidad Nacional del Sur. Departamento de Agronomía; Argentina.Fil: Amiottia, Nilda. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Costa, José Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.Fil: Castro-Franco, Mauricio. Centro de Investigaciones de la Caña de Azúcar de Colombia. Estación Experimental Estación Experimental vía Cali-Florida; Colombia

Evaluation of Multiorbital SAR and Multisensor Optical Data for Empirical Estimation of Rapeseed Biophysical Parameters

This article aims to evaluate the potential of multitemporal and multiorbital remote sensing data acquired both in the microwave and optical domain to derive rapeseed biophysical parameters (crop height, dry mass, fresh mass, and plant water content). Dense temporal series of 98 Landsat-8 and Sentinel-2 images were used to derive normalized difference vegetation index (NDVI), green fraction cover (fCover), and green area index (GAI), while backscattering coefficients and radar vegetation index (RVI) were obtained from 231 mages acquired by synthetic aperture radar (SAR) onboard Sentinel-1 platform. Temporal signatures of these remote sensing indicators (RSI) were physically interpreted, compared with each other to ground measurements of biophysical parameters acquired over 14 winter rapeseed fields throughout the 2017–2018 crop season. We introduced new indicators based on the cumulative sum of each RSI that showed a significant improvement in their predictive power. Results particularly reveal the complementarity of SAR and optical data for rapeseed crop monitoring throughout its phenological cycle. They highlight the potential of the newly introduced indicator based on the VH polarized backscatter coefficient to estimate height (R2 = 0.87), plant water content (R2 = 0.77, from flowering to harvest), and fresh mass (R2 = 0.73) and RVI to estimate dry mass (R2 = 0.82). Results also demonstrate that multiorbital SAR data can be merged without significantly degrading the performance of SAR-based relationships while strongly increasing the temporal sampling of the monitoring. These results are promising in view of assimilating optical and SAR data into crop models for finer rapeseed monitoring

Combining Crop Models and Remote Sensing for Yield Prediction: Concepts, Applications and Challenges for Heterogeneous Smallholder Environments

JRC and CCAFS jointly organized a workshop on June 13-14, 2012 in Ispra, Italy with the aim to advance the state-of-knowledge of data assimilation for crop yield forecasting in general, to address challenges and needs for successful applications of data assimilation in forecasting crop yields in heterogeneous, smallholder environments, and to enhance collaboration and exchange of knowledge among data assimilation and crop forecasting groups. The workshop showed that advances made in crop science are widely applicable to crop forecasting. The presentations of the participants approached the challenge from many sides, leading to ideas for improvement that can be implemented in real-time, operational crop yield forecasting. When applied, this knowledge has the potential to benefit the livelihoods of smallholder farmers in the developing world.JRC.H.4-Monitoring Agricultural Resource

Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model

To predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China’s Hebei Province. To reduce cloud contamination, we applied Savitzky–Golay (S–G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model’s state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution–University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions (R2 = 0.48; RMSE= 151.92 kg ha−1) compared with the unassimilated results (R2 = 0.23;RMSE= 373.6 kg ha−1) and the TM LAI results (R2 = 0.27; RMSE= 191.6 kg ha−1). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates

Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes

The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery which can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge. In this work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a machine learning technique that learns automatically the statistical relationships among multisensor time series, to detect vegetated areas over which the synergy between SAR-optical imageries is profitable. For this purpose, we use the Sentinel-1 Radar Vegetation Index (RVI) and Sentinel-2 Leaf Area Index (LAI) time series over a study area in north west of the Iberian peninsula. Through a physical interpretation of MOGP trained models, we show its ability to provide estimations of LAI even over cloudy periods using the information shared with RVI, which guarantees the solution keeps always tied to real measurements. Results demonstrate the advantage of MOGP especially for long data gaps, where optical-based methods notoriously fail. The leave-one-image-out assessment technique applied to the whole vegetation cover shows MOGP predictions improve standard GP estimations over short-time gaps (R 2 of 74% vs 68%, RMSE of 0.4 vs 0.44 [m 2 m −2 ]) and especially over long-time gaps (R 2 of 33% vs 12%, RMSE of 0.5 vs 1.09 [m 2 m −2 ])

Satellite remote sensing priorities for better assimilation in crop growth models : winter wheat LAI and grassland mowing dates case studies

In a context of markets globalization, early, reliable and timely estimations of crop yields at regional to global scale are clearly needed for managing large agricultural lands, determining food pricing and trading policies but also for early warning of harvest shortfalls. Crop growth models are often used to estimate yields within the growing season. The uncertainties associated with these models contribute to the uncertainty of crop yield estimations and forecasts. Satellite remote sensing, through its ability to provide synoptic information on growth conditions over large geographic extents and in near real-time, is a key data source used to reduce uncertainties in biophysical models through data assimilation methods. This thesis aims at assessing possible improvements for the assimilation of remotely-sensed biophysical variables in crop growth models and to estimate their related errors reduction on modelled yield estimates. Assimilated observations are winter wheat leaf area index (LAI) and grassland mowing dates derived respectively from optical (MODIS) and microwave (ERS-2) data. These observations have been assimilated in WOFOST and LINGRA growth models. Observing System Simulation Experiments (OSSE) allowed assessing errors reduction on yield estimates after assimilation for different situations of accuracy and frequency of remotely-sensed estimates and for different assimilation strategies, indicating expected improvements with the currently available and forthcoming sensors; it supports also guidelines for future satellite missions. A main finding is the fact that yield estimate improvements are only possible for assimilation strategies able to correct the possible phenological discrepancies between the remotely-sensed and the simulated data. These phenological shifts arise mainly from uncertainties on the parameters and initial states driving the phenological stages in the models but are also due, in some situations, to lack of pixel purity because of the medium resolution of sensors such as MODIS. This thesis also identifies some of the main sources of uncertainties and assesses their impact on assimilation performances. The impact of water content and biomass on SAR backscattering of grasslands is specifically assessed. The backscattering of grasslands increases with the increases of water content and decreases with the biomass in dry conditions. Based on these results, methodologies to classify grasslands according to land use (mowing or grazing) and to detect mowings are designed and demonstrated. A good classification accuracy is observed (overall accuracy around 80%). Results for mowings detection are a bit lower as half of the mowings are correctly identified but the methodology can be considered as promising in particular in the perspective of very dense SAR time series as currently acquired operationally by Sentinel-1.(AGRO - Sciences agronomiques et ingénierie biologique) -- UCL, 201

Agricultural Meteorology and Climatology

Agricultural Meteorology and Climatology is an introductory textbook for meteorology and climatology courses at faculties of agriculture and for agrometeorology and agroclimatology courses at faculties whose curricula include these subjects. Additionally, this book may be a useful source of information for practicing agronomists and all those interested in different aspects of weather and climate impacts on agriculture. In times when scientific knowledge and practical experience increase exponentially, it is not a simple matter to prepare a textbook. Therefore we decided not to constrain Agricultural Meteorology and Climatology by its binding pages. Only a part of it is a conventional textbook. The other part includes numerical examples (easy-to-edit worksheets) and recommended additional reading available on-line in digital form. To keep the reader's attention, the book is divided into three sections: Basics, Applications and Agrometeorological Measurements with Numerical Examples

An exploratory study to improve the predictive capacity of the crop growth monitoring system as applied by the European Commission

The European Union (EU), through its Common Agricultural Policy (CAP), attempts to regulate the common agricultural market to, among others, secure food supplies and provide consumers with food at reasonable prices. Implementation and control of these CAP regulations is executed by the Directorate General for Agriculture (DG VI) of the EU. To manage this common market, to evaluate the consequences of these regulations and to estimate and control the subsidies to be paid, DG VI requires detailed information on planted area, crop yield and production volume.Information on land use, interannual land use changes and yields is routinely collected by the national statistical services, which convey this information to the statistical office of the European Commission, EUROSTAT. Collection and compilation of these agricultural statistics however, is time consuming and laborious; it often takes up to one or two years before this information is available in the EUROSTAT databases. At this late stage, these statistics are of limited use for evaluating policy or to estimate the amount of subsidies to be paid. Hence, more timely and accurate information is needed. To assist DG VI and EUROSTAT to collect this information, the MARS project was initiated, with the aim to develop methods to produce timely statistics on land use, planted area and production volumes for various crops within the EU.The MARS project applies remote sensing imagery and ground surveys to estimate the planted area. Since no proven methods to relate satellite imagery to quantitative crop yields were available at the beginning of the MARS project, a crop growth monitoring system (CGMS) based on the WOFOST crop growth simulation model was developed.In this thesis several variants of the current standard operational version of CGMS are explored. The standard CGMS version assumes that yield per unit area and planted area are independent of each other. In this thesis total production volume instead of yield per unit area is considered, hypothesizing that the annually planted area and the yield per unit area are mutually dependent and should therefore be analyzed simultaneously. It is assumed that weather and economic factors affect production volume variation. However, for two of the major wheat producing countries the analysis fails to demonstrate a relation between the soft wheat production volume and selling or intervention price. Furthermore, for soft wheat, for 5 out of the 10 investigated countries, and for durum wheat, for 3 out of the 4 investigated countries, the expenditure on crop protection agents is not significantly associated with the production volume. These results suggest that these parameters are not generally applicable and should therefore not be applied for production volume prediction. As an alternative to economic factors, the fertilizer application per unit area is examined. The analysis shows that this factor can account for the trend and production volume variation.Next, production volumes of soft and durum wheat are predicted and two types of prediction models were examined. The first type included the planted area in the prediction model, and production volume was predicted in one step. The second type predicted the production volume in two steps: first, yield per unit area was predicted and subsequently, this value was multiplied by an estimate for the planted area. Furthermore, two functions to describe the trend in yield and production volume series were tested: a linear function of time and a linear function of the fertilizer application. A hypothetical and an operational situation were studied. The hypothetical situation assumes that current year's information on planted area and fertilizer consumption is available, whereas the operational situation assumes that these two variables are not available and consequently have to be estimated.Comparison of the results from the one-step model with those from the two-step model demonstrates that in the operational situation in 14 out of 16 crop-country combinations the one-step model predicted more accurately when a linear time trend was applied. When fertilizer application was applied the one-step model in 10 out of 16 crop-country combinations provided more accurate results. Furthermore, when two-step prediction models were applied, crop simulation results were significant in approximately 30% of the cases (5% t-test). However, when models of the one-step type were used, this number increased to more than 80%.Although these results cannot be viewed as a proof that one-step models are really superior, they still give an indication and provide a direction for further research. It corroborates the assumption that variation in planted area and yield per unit area are not independent and therefore variation in production volume should be analyzed using models of the one-step type.Comparison among the one-step model results in the operational situation shows that in 50% of the investigated crop-country combinations the model that applied simulation results plus either a linear time trend or fertilizer application, predicted more accurately than the model that did not apply simulation results. In the hypothetical situation the two-step model that uses the fertilizer application provided the most accurate results. However, analysis also demonstrates that in the operational situation this model yielded the least accurate results. In this situation, the one-step models provided the most accurate results since they are less sensitive to errors in the planted area estimates.Although the prediction results obtained with simulation results are not always more accurate when compared to results derived from trend extrapolations or simple averages, the use of simulation results in combination with a trend function certainly holds a promise for further improvement.Next, a method to estimate daily global radiation was developed and tested. This method uses cloud cover and the temperature range as input. It provides less accurate results than the Ångström-Prescott equation, but the differences are small. This method may be used as an alternative for the Ångström-Prescott method when sunshine duration observations are not available. A hierarchical method is proposed to introduce global radiation in CGMS. If observed global radiation is available it will be used, if only sunshine duration is available the Ångström-Prescott method will be used, if neither radiation nor sunshine is available, the method developed here may be applied. This method was tested and the prediction results were slightly more accurate than the results obtained with the standard operational version of CGMS.Furthermore, an additive and a multiplicative model are compared. An additive model assumes that variation in production volume as a result of weather variation is similar under high production systems and low production systems. The multiplicative model assumes that variation in production volume over the years is proportional to the mean production level. Wheat production volumes for France were predicted at subregional, regional and national level. The predictions at subregional and regional level were aggregated to national values.The results suggest that more accurate predictions of total national production volume can be obtained when predictions executed at regional or subregional level are aggregated into a national value instead of estimating this value in one step. This may be the result of the applied aggregation procedure. Presumably, local weather effects are obscured in the aggregated values. Another explanation could be that errors in the production volumes of the individual regions or subregions compensate each other when summed to a total national value. These results also provide some evidence that aggregated predictions derived from the multiplicative model are more accurate than those derived from the additive model, suggesting that effects of weather on crop growth depend on the magnitude of the annual mean yield.Finally, data obtained from the field surveys executed in the framework of the MARS are analyzed with the aim to increase insight in sowing strategies of rainfed barley in semi-arid regions. The hypothesis is that in CGMS sowing date variation should be accounted for: CGMS assumes per crop and per region one sowing and one flowering date, hypothesizing that sowing and flowering date variation have limited effects on the regional production volume. The results, at least for barley grown under rainfed conditions, support this hypothesis: no association could be demonstrated between (i) sowing date variation and yield per unit area; (ii) sowing date variation and the precipitation amount; (iii) flowering date variation and yield per unit area. Farmers may base their sowing strategy on the fact that sowing at the presumed beginning of the rainy season will give higher yields than when sowing is delayed, provided rainfall during the growing season is sufficient. In dry years, when available water is the main yield-limiting factor, effects of sowing date variation on yield are not noticeable. The need to synchronize seasonal rainfall and phenology of the selected barley cultivars may also limit the possibilities to postpone sowing.EvaluationThe principal objective of this study was to explore possibilities to improve CGMS in such a way that it may be applied for quantitative yield prediction for all EU member states. Various options have been explored. Although some interesting results have been obtained, only two concrete suggestions for such an improvement can be given: (i) predictions should be executed at lower administrative level and subsequently aggregated to national values, (ii) planted area should be included in the analysis and prediction model. More research is needed to identify tangible points for improvements in CGMS.</p

Apport des données satellitaires Sentinel-1 et Sentinel-2 pour la détection des surfaces irriguées et l'estimation des besoins et des consommations en eau des cultures d'été dans les zones tempérées

L'eau est une ressource naturelle, qui depuis des millions d'années participe au cycle de la vie. Mais depuis peu, le changement climatique et les activités humaines remettent en cause l'équilibre du cycle de l'eau. Pour préserver cette ressource, il est nécessaire d'améliorer la connaissance sur les surfaces irriguées ainsi que les besoins et consommations en eau des cultures sur de grandes surfaces, mais elle n'est pas simple à appréhender à cause de la forte variabilité spatiale des sols, du climat et des pratiques agricoles. La télédétection a un rôle fondamental à jouer et plus particulièrement les données Sentinel. Ces travaux de thèse ont vocation à fournir des outils de diagnostics pour assurer une gestion optimale de la ressource en eau à l'échelle des bassins versants. Pour cela, une approche de cartographie des surfaces irriguées en zones tempérées à partir d'images Sentinel-1 & 2 a été développée. Elle a permis de cartographier les cultures d'été irriguées et pluviales sur les bassins versants Adour amont et Tarn aval en fin de saison. Nous nous sommes intéressés à la modélisation des besoins et des consommations en eau du maïs irrigué à partir du modèle agro-météorologique SAMIR utilisant des images d'indice de végétation (NDVI et FCover). Il a été appliqué à différentes échelles spatiales et sur différents jeux de données de validation. Les résultats montrent que le modèle est capable de reproduire de façon satisfaisante les consommations en eau des parcelles des partenaires. Nous avons également évalué l'impact de différentes données pédologiques pour estimer la réserve utile (RU). Les résultats illustrent la nécessité d'une bonne estimation de la RU et cela à une échelle compatible avec une modélisation à la parcelle pour pouvoir estimer correctement les irrigations saisonnières, ainsi que les volumes.Water is a natural resource that has been part of the life cycle for millions of years. But recently, climate change and human activities have challenged the balance of the water cycle. To preserve this resource, it is necessary to improve knowledge of irrigated areas and the water needs and consumption of crops over large areas, but this is not easy to understand because of the high spatial variability of soils, climate and cultivation practices. Remote sensing has a fundamental role to play and more particularly Sentinel data. The aim of this thesis is to provide diagnostic tools to ensure optimal management of water resources at the catchment scale. To this end, an approach for mapping irrigated areas in temperate zones based on monthly Sentinel-1 & 2 images was developed. It was used to map irrigated and rain-fed summer crops in the Adour amont and Tarn aval catchments during and at the end of the season. In parallel with these results, the method was developed for operational purposes. We focused on modelling the water requirements and consumption of irrigated maize using the SAMIR agro-meteorological model using vegetation index images (NDVI and FCover). It was applied at different spatial scales and on different validation data sets. The results show that the model is able to reproduce satisfactorily the water consumption of the partners plots. We also evaluated the impact of different soil data to estimate the maximum useful reserve. The results illustrate the need for a good estimation of the AWC at a scale compatible with plot modelling in order to be able to correctly estimate seasonal irrigations and volumes