Grégory Duveiller

Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data

Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. At the field level, the close correlation between green leaf area (GLA) during maturation and grain yield in wheat revealed that the onset and rate of senescence appeared to be important factors for determining wheat grain yield. Our study sought to explore a simple approach for wheat yield forecasting at the regional level, based on metrics derived from the senescence phase of the green area index (GAI) retrieved from remote sensing data. This study took advantage of recent methodological improvements in which imagery with high revisit frequency but coarse spatial resolution can be exploited to derive crop-specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is dominated by the target crop: winter wheat. A logistic function was used to characterize the GAI senescence phase and derive the metrics of this phase. Four regression-based models involving these metrics (i.e., the maximum GAI value, the senescence rate and the thermal time taken to reach 50% of the green surface in the senescent phase) were related to official wheat yield data. The performances of such models at this regional scale showed that final yield could be estimated with an RMSE of 0.57 ton ha−1, representing about 7% as relative RMSE. Such an approach may be considered as a first yield estimate that could be performed in order to provide better integrated yield assessments in operational systems.

Revisiting the concept of a symmetric index of agreement for continuous datasets

Quantifying how close two datasets are to each other is a common and necessary undertaking in scientific research. The Pearson product-moment correlation coefficient r is a widely used measure of the degree of linear dependence between two data series, but it gives no indication of how similar the values of these series are in magnitude. Although a number of indexes have been proposed to compare a dataset with a reference, only few are available to compare two datasets of equivalent (or unknown) reliability. After a brief review and numerical tests of the metrics designed to accomplish this task, this paper shows how an index proposed by Mielke can, with a minor modification, satisfy a series of desired properties, namely to be adimensional, bounded, symmetric, easy to compute and directly interpretable with respect to r. We thus show that this index can be considered as a natural extension to r that downregulates the value of r according to the bias between analysed datasets. The paper also proposes an effective way to disentangle the systematic and the unsystematic contribution to this agreement based on eigen decompositions. The use and value of the index is also illustrated on synthetic and real datasets.

Using Thermal Time and Pixel Purity for Enhancing Biophysical Variable Time Series: An Interproduct Comparison

This paper presents a multiannual comparison at regional scale of currently available 1-km global leaf area index (LAI) products with crop-specific green area index (GAI) retrieved from 250-m spatial resolution imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). The crop-specific GAI product benefits from the following extra processing steps: 1) spatial filtering of time series based on pixel purity; 2) transforming the time scale to thermal time; and 3) fitting a canopy structural dynamic model to smooth out the signal. In order to perform a rigorous comparison, these steps were also applied to the 1-km LAI products, namely, MODIS LAI (MCD15) and LAI produced in the CYCLOPES (Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites) project. A simple indicator was also designed to quantify the increase in temporal smoothness that can thus be obtained. The results confirm that, for winter wheat, the 250-m GAI product provides a more realistic description of the time course of the biophysical variable in terms of reaching higher values, grasping the variability, and providing smoother time series. However, the use of thermal time and pixel purity also improves the temporal consistency and coherence of the 1-km products. Overall, the results of this study suggest that these techniques could be valuable in harmonizing remote sensing data coming from different sources with varying spatial and temporal resolution for enhanced vegetation monitoring.

Estimating crop-specific evapotranspiration using remote-sensing imagery at various spatial resolutions for improving crop growth modelling

By governing water transfer between vegetation and atmosphere, evapotranspiration (ET) can have a strong influence on crop yields. An estimation of ET from remote sensing is proposed by the EUMETSAT ‘Satellite Application Facility’ (SAF) on Land Surface Analysis (LSA). This ET product is obtained operationally every 30 min using a simplified SVAT scheme that uses, as input, a combination of remotely sensed data and atmospheric model outputs. The standard operational mode uses other LSA-SAF products coming from SEVIRI imagery (the albedo, the downwelling surface shortwave flux, and the downwelling surface longwave flux), meteorological data, and the ECOCLIMAP database to identify and characterize the land cover. With the overall objective of adapting this ET product to crop growth monitoring necessities, this study focused first on improving the ET product by integrating crop-specific information from high and medium spatial resolution remote-sensing data. A Landsat (30 m)-based crop type classification is used to identify areas where the target crop, winter wheat, is located and where crop-specific Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m) time series of green area index (GAI) can be extracted. The SVAT model was run for 1 year (2007) over a study area covering Belgium and part of France using this supplementary information. Results were compared to those obtained using the standard operational mode. ET results were also compared with ground truth data measured in an eddy covariance station. Furthermore, transpiration and potential transpiration maps were retrieved and compared with those produced using the Crop Growth Monitoring System (CGMS), which is run operationally by the European Commissions Joint Research Centre to produce in-season forecast of major European crops. The potential of using ET obtained from remote sensing to improve crop growth modelling in such a framework is studied and discussed. Finally, the use of the ET product is also explored by integrating it in a simpler modelling approach based on light-use efficiency. The Carnegie–Ames–Stanford Approach (CASA) agroecosystem model was therefore applied to obtain net primary production, dry matter productivity, and crop yield using only LSA-SAF products. The values of yield were compared with those obtained using CGMS, and the dry matter productivity values with those produced at the Flemish Institute for Technological Research (VITO). Results showed the potential of using this simplified remote-sensing method for crop monitoring.

The mark of vegetation change on Earth’s surface energy balance

Changing vegetation cover alters the radiative and non-radiative properties of the surface. The result of competing biophysical processes on Earth’s surface energy balance varies spatially and seasonally, and can lead to warming or cooling depending on the specific vegetation change and background climate. Here we provide the first data-driven assessment of the potential effect on the full surface energy balance of multiple vegetation transitions at global scale. For this purpose we developed a novel methodology that is optimized to disentangle the effect of mixed vegetation cover on the surface climate. We show that perturbations in the surface energy balance generated by vegetation change from 2000 to 2015 have led to an average increase of 0.23 ± 0.03 °C in local surface temperature where those vegetation changes occurred. Vegetation transitions behind this warming effect mainly relate to agricultural expansion in the tropics, where surface brightening and consequent reduction of net radiation does not counter-balance the increase in temperature associated with reduction in transpiration. This assessment will help the evaluation of land-based climate change mitigation plans.Depending on where and when it occurs, vegetation cover change can affect local climate by altering the surface energy balance. Based on satellite data, this study provides the first data-driven assessment of such effects for multiple vegetation transitions at global scale.

A Method to Determine the Appropriate Spatial Resolution Required for Monitoring Crop Growth in a given Agricultural Landscape

This research investigates the adequacy of a remote sensing instruments spatial resolution for monitoring crop growth over agricultural landscapes with different spatial patterns. The approach is based on the postulate that time series of a subset of pixels can characterize crop growth over a small zone with similar agro-climatic growing conditions. The point spread function (PSF) is explicitly taken into account in order to identify, at different scales, the pixels whose effective instantaneous field of view (EIFOV) falls within the larger fields of the target crop. This pixel sampling approach enables the resolution to be much coarser than what would be recommended by the predominant scale of spatial variation of the image. Since monitoring crop growth is often done to evaluate the total regional crop production, the method is extended to explore the resolution necessary for crop area estimation.

A dataset mapping the potential biophysical effects of vegetation cover change

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes.

Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity

According to Monteith’s theory, crop biomass is linearly correlated with the amount of absorbed photosynthetically active radiation (APAR) and a constant radiation use efficiency (RUE) down-regulated by stress factors such as CO2 fertilisation, temperature and water stress. The objective was to investigate the relative importance of these stress factors in relation to regional biomass production and yield. The production efficiency model Copernicus Global Land Service-Dry Matter Productivity (CGLS-DMP), which follows Monteith’s theory, was modified and evaluated for common wheat and silage maize in France, Belgium and Morocco using SPOT VEGETATION for the period 1999–2012. For each study site the stress factor that has the highest correlation with crop yield was retained. The correlation between crop yield data and cumulative modified DMP, CGLS-DMP, fAPAR, and NDVI values were analysed for different crop growth stages. A leave-one-year-out cross validation was used to test the robustness of the model. On average, R2 values increased from 0.49 for CGLS-DMP to 0.68 for modified DMP, RMSE (t/ha) decreased from 0.84–0.61, RRMSE (%) reduced from 13.1–8.9, MBE (t/ha) decreased from 0.05–0.03 and the index of model performance (E1) increased from 0.08–0.28 for the selected sites and crops. The best results were obtained by including combinations of the most appropriate stress factors for each selected region and cumulating the modified DMP during part of the growing season that includes the reproductive stage. Though no single solution to the improvement of a global product could be demonstrated, our findings encourage an extension of the methodology to other regions of the world.

Local adjustments of image spatial resolution to optimize large-area mapping in the era of big data

Abstract Sentinel-2 has opened a new era for the remote sensing community where 10-m imagery is freely available with a 5-day revisit frequency and a systematic global coverage. Having both frequent and detailed observations across large geographic areas are ideal characteristics that can potentially revolutionize applications such as crop mapping and monitoring. However, such large volumes of high-resolution data pose challenges to users in terms of problem complexity, computational resources and processing time, beckoning the increasingly relevant question: at which resolution should this imagery be processed? Here, we develop a methodology to characterize resolution-dependent errors in cropland mapping and explore their behavior when we move across spatial scales and landscapes, taking special care to include the effects of the instruments Point Spread Function (PSF). Results show how local upscaling of 10-m imagery, e.g., from Sentinel-2, to 30 m mitigates most the adverse effects generated by the PSF when comparing it to native 30-m imagery, e.g., from Landsat-8. Extending this logic, we demonstrate for two nationwide cases how maps can be calculated showing the optimal spatial resolution that keeps resolution-dependent errors below a user-defined threshold. Based on these maps, we estimate that 31% of Belgium and 59% of South Africa could be processed at 20 m instead of 10 m, while keeping the increase of resolution-dependent errors below 3%. These local resolution adjustments lead to a reduction in data volume and processing time by 23% and 44%, respectively.

Correction to "Using thermal time and pixel purity for enhancing biophysical variable time series: An interproduct comparison" [Apr 13 2119-2127]

There is an error in the above-named article [ibid.,vol. 51, no. 4, pp. 2119-2127, Apr. 2013] regarding the definition and the implementation of equation (3), defining the proposed temporal smoothing index (TSI). The correct formula is provided. These corrections do not change any of the general conclusions of the paper, but some of the comments regarding the interpretation of this table are revised.