A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments

Contribution of Remote Sensing on Crop Models: A Review

Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products

Combining remote sensing and crop modeling techniques to derive a nitrogen fertilizer application strategy

The crucial question in this thesis was how can remote sensing data and crop models be used to derive a N fertilizer strategy that is capable to lower the environmental side effects of N fertilizer application. This raised the following detailed objectives: The first objective (i) how N content determination via spectral reflectance is influenced by different leaves and positions on the leaf was investigated in Publication I. Different wheat plants were cultivated under different N levels and under drought stress in two hydroponic greenhouse trials. Spectral reflectance measurements were taken from three leaves and at three positions on the leaf for each plant. In total, 16 vegetation indices broadly used in the literature were calculated based on the spectral reflectance for each combination of leaf and position. The plant N content was determined by lab analyses. Neither the position on the leaf nor leaf number had an impact on the accuracy of plant N determination via spectral reflectance measurements. Therefore measurements taken at the canopy level seem to be a valid approach. However, if other stress symptoms like drought or disease infection occur, a differentiation between leaves and positions on the leaf might play a more crucial role. Publication II dealt with the second objective on (ii), how to incorporate leaf disease into the DSSAT wheat model to enable the simulation of the impact of leaf disease on yield. An integration of sensor information in crop growth models requires the update of model state variables. A model extension was developed by adding a pest damage module to the existing wheat model. The approach was tested on a two-year dataset from Argentina with different wheat cultivars and on a one-year dataset from Germany with different inoculum levels of septoria tritici blotch (STB). After the integration of disease infection, the accuracy of the simulated yield and leaf area index (LAI) was improved. The Root mean squared error (RMSE) values for yield (1144 kg ha−1) and LAI (1.19 m2 m−2) were reduced by half (499 kg ha−1) for yield and LAI (0.69 m2 m−2). A sensitivity analysis also showed a strong responsiveness of the model by the integration of different STB disease infection scenarios. Increasing the modeling accuracy even further a MM approach seems to be suitable. Assembling more models increases the complexity of the simulation and the involved calibration procedure especially if the user is not familiar with all models. To avoid these conflicts, Publication III evaluated the third objective (iii) if an automatic calibration procedure in a MM approach for winter wheat can eliminate the subjectivity factor in model calibration. The model calibration was performed on a 4-yr N wheat fertilizer trial in southwest Germany. The evaluation mean showed satisfying results for the calibration (d-Index 0.93) and evaluation dataset (d-Index 0.81). This lead to the fourth (iv) objective to use a MM approach to improve the overall modeling accuracy. The evaluation of a fertilizer trial showed an improved modeling accuracy in most cases, especially in the drought season 2018. Based on the combination of a MM approach and the incorporation of sensor data, a Nitrogen Application Prescription System (NAPS) was developed. The initial NAPS setup requires long term recorded data (yield, weather, and soil) to ensure proper MM calibration. After calibration, the current growing season conditions are required (weather, management information) until the N application date. Afterward, the NAPS incorporates remote sensing information and generated weather for running future N application scenarios. The selection of the proper amount of N is determined by economic and ecological criteria. Furthermore, in order to account for differences in in-field variabilities and to deliver a N prescription site-specifically, the NAPS concept has to be applied on a geospatial scale by adjusting soil parameters spatially. The NAPS concept has the potential to adjust the N application more economically and ecologically by using current sensor data, historical yield records, and future weather prediction to derive a more precise N application strategy. Finally, this concept exhibits the potential for reconciliation of the issue of an economic, agricultural production without harming the environment.In dieser Arbeit wurde eruiert, ob mit Hilfe von Sensordaten und Pflanzenwachstumsmodellen eine N-Düngemittelstrategie abgeleitet werden kann, die in der Lage ist die ökologischen Belastung zu verringern. Dies umfasste die Evaluation folgender Fragestellungen: (I) Wird die spektrale Reflexion und somit die Bestimmung der N-Konzentration durch die Messung an verschiedenen Blattetagen und -Positionen beeinflusst (Publikation I)? Für die Klärung dieser ersten Frage wurden in zwei hydroponischen Gewächshausversuchen Weizenpflanzen bei unterschiedlicher N-Exposition und Trockenstress kultiviert. Für jede Pflanze wurden spektrale Reflexionsmessungen an drei Blattetagen und an drei Positionen auf dem Blatt durchgeführt. Insgesamt wurden die 16 üblichsten auf spektraler Reflexion basierenden Vegetationsindizes für jede Kombination von Blattetage und -Position berechnet. Die N-Konzentration der Pflanze wurde durch Laboranalysen bestimmt. Weder die Position auf dem Blatt noch die Blattetage hatten einen Einfluss auf die Genauigkeit der Bestimmung der N-Konzentration der Pflanze durch spektrale Reflexionsmessungen. Daher sind Messungen auf Bestandsebene ausreichend. Falls jedoch weitere Stressfaktoren wie Trockenheit oder Krankheitsbefall auftreten, kann eine Differenzierung zwischen verschiedenen Blattetagen notwendig oder von Vorteil sein. In der nächsten Fragestellung (Publikation II) wurde untersucht, wie Blattkrankheiten in ein DSSAT-Weizenmodell integriert werden können, um so die Auswirkungen von Blattkrankheiten auf den Ertrag zu simulieren. Eine Modellerweiterung wurde entwickelt, durch die Integration eines Blattkrankheitsmoduls in das bestehende DSSAT Weizenmodell. Das Modul simuliert die Auswirkungen des täglichen Schadens durch die Krankheit auf die Photosynthese und den Blattflächenindex. Der Ansatz wurde an einem zweijährigen Datensatz aus Argentinien mit verschiedenen Weizensorten und an einem einjährigen Datensatz aus Deutschland mit verschiedenen Inokulumniveaus von Septoria tritici-Blotch (STB) getestet. Die Sensitivitätsanalyse zeigte die Möglichkeit des Modells, den Ertrag in einer exponentiellen Beziehung mit zunehmendem Infektionsgrad (0-70%) zu reduzieren. Das erweiterte Modell stellt somit eine Möglichkeit dar, STB-Infektionen standortspezifisch in Verbindung mit verfügbaren Sensordaten zu simulieren. Um die Modellierungsgenauigkeit noch weiter zu erhöhen, wurde der Einsatz eines MM-Ansatz geprüft. Die Kombination von verschiedenen Modellen erhöht die Komplexität der Simulation und des damit verbundenen Kalibrierungsverfahrens, insbesondere wenn der Benutzer nicht mit allen Modellen vertraut ist. Die dritte Fragestellung (iii) untersuchte daher, ob objektive Kalibrierungsergebnisse gewährleitet werden könnten, wenn die cultivar coefficients im Modell auf Basis tatsächlich gemessener Daten mittels eines neu entwickelten automatischen Calibrator-Programms optimiert wurden. Die Modellkalibrierung wurde an einem 4-jährigen-Weizendüngungsversuch in Südwestdeutschland durchgeführt. Die statistische Auswertung des Kalibrierverfahrens zeigte zufriedenstellende Ergebnisse und führte zur vierten Fragestellung. Die vierte Fragestellung befasste sich mit dem Thema, ob ein MM-Ansatz die Gesamtmodelliergenauigkeit verbessern kann. Die Auswertung des Düngemittelversuchs zeigte in den meisten Fällen eine verbesserte Modellierungsgenauigkeit, insbesondere in einem durch Wasserstress geprägten Versuchsjahr wie 2018. Unter Verwendung eines MM-Ansatzes, durch Anpassung der Modellvariablen und durch die Integration von Sensordaten wurde ein Nitrogen Application Prescription System (NAPS) entwickelt. Eine Voraussetzung für das NAPS-Konzepts ist das Vorhandensein von Langzeit-Daten (Ertrag, Klima- und Bodenbedingungen), um eine korrekte MM-Kalibrierung zu gewährleisten. Nach der Kalibrierung werden die Bedingungen der aktuellen Wachstumssaison (Wetter, Managementinformationen) bis zum Düngetermin benötigt. Anschließend berechnet das NAPS basierend auf Sensorinformationen und simulierten Wetterbedingungen verschiedene Düngeszenarien. Ökonomische und ökologische Kriterien bestimmen die optimierte Düngemenge. Darüber hinaus muss das NAPS-Konzept auf räumlicher Ebene arbeiten, indem es die Bodenparameter berücksichtigt. So kann unter Beachtung der Feldvariabilität eine standortspezifische N-Ausbringung gewährleistet werden. In Summe zeigte sich, dass NAPS die Düngung an ökonomische und ökologische Faktoren anpasst, indem es aktuelle Sensordaten, historische Ertragsaufzeichnungen und zukünftige Wettervorhersagen zur Ermittlung einer präziseren N-Ausbringung nutzt. Das Konzept hat so das Potenzial, die nachteiligen Auswirkungen einer Überdüngung zu begrenzen, so dass eine umweltfreundlichere Agrarproduktion gewährleistet wird

Combining remote sensing and crop modeling techniques to derive a nitrogen fertilizer application strategy

The crucial question in this thesis was how can remote sensing data and crop models be used to derive a N fertilizer strategy that is capable to lower the environmental side effects of N fertilizer application. This raised the following detailed objectives: The first objective (i) how N content determination via spectral reflectance is influenced by different leaves and positions on the leaf was investigated in Publication I. Different wheat plants were cultivated under different N levels and under drought stress in two hydroponic greenhouse trials. Spectral reflectance measurements were taken from three leaves and at three positions on the leaf for each plant. In total, 16 vegetation indices broadly used in the literature were calculated based on the spectral reflectance for each combination of leaf and position. The plant N content was determined by lab analyses. Neither the position on the leaf nor leaf number had an impact on the accuracy of plant N determination via spectral reflectance measurements. Therefore measurements taken at the canopy level seem to be a valid approach. However, if other stress symptoms like drought or disease infection occur, a differentiation between leaves and positions on the leaf might play a more crucial role. Publication II dealt with the second objective on (ii), how to incorporate leaf disease into the DSSAT wheat model to enable the simulation of the impact of leaf disease on yield. An integration of sensor information in crop growth models requires the update of model state variables. A model extension was developed by adding a pest damage module to the existing wheat model. The approach was tested on a two-year dataset from Argentina with different wheat cultivars and on a one-year dataset from Germany with different inoculum levels of septoria tritici blotch (STB). After the integration of disease infection, the accuracy of the simulated yield and leaf area index (LAI) was improved. The Root mean squared error (RMSE) values for yield (1144 kg ha−1) and LAI (1.19 m2 m−2) were reduced by half (499 kg ha−1) for yield and LAI (0.69 m2 m−2). A sensitivity analysis also showed a strong responsiveness of the model by the integration of different STB disease infection scenarios. Increasing the modeling accuracy even further a MM approach seems to be suitable. Assembling more models increases the complexity of the simulation and the involved calibration procedure especially if the user is not familiar with all models. To avoid these conflicts, Publication III evaluated the third objective (iii) if an automatic calibration procedure in a MM approach for winter wheat can eliminate the subjectivity factor in model calibration. The model calibration was performed on a 4-yr N wheat fertilizer trial in southwest Germany. The evaluation mean showed satisfying results for the calibration (d-Index 0.93) and evaluation dataset (d-Index 0.81). This lead to the fourth (iv) objective to use a MM approach to improve the overall modeling accuracy. The evaluation of a fertilizer trial showed an improved modeling accuracy in most cases, especially in the drought season 2018. Based on the combination of a MM approach and the incorporation of sensor data, a Nitrogen Application Prescription System (NAPS) was developed. The initial NAPS setup requires long term recorded data (yield, weather, and soil) to ensure proper MM calibration. After calibration, the current growing season conditions are required (weather, management information) until the N application date. Afterward, the NAPS incorporates remote sensing information and generated weather for running future N application scenarios. The selection of the proper amount of N is determined by economic and ecological criteria. Furthermore, in order to account for differences in in-field variabilities and to deliver a N prescription site-specifically, the NAPS concept has to be applied on a geospatial scale by adjusting soil parameters spatially. The NAPS concept has the potential to adjust the N application more economically and ecologically by using current sensor data, historical yield records, and future weather prediction to derive a more precise N application strategy. Finally, this concept exhibits the potential for reconciliation of the issue of an economic, agricultural production without harming the environment.In dieser Arbeit wurde eruiert, ob mit Hilfe von Sensordaten und Pflanzenwachstumsmodellen eine N-Düngemittelstrategie abgeleitet werden kann, die in der Lage ist die ökologischen Belastung zu verringern. Dies umfasste die Evaluation folgender Fragestellungen: (I) Wird die spektrale Reflexion und somit die Bestimmung der N-Konzentration durch die Messung an verschiedenen Blattetagen und -Positionen beeinflusst (Publikation I)? Für die Klärung dieser ersten Frage wurden in zwei hydroponischen Gewächshausversuchen Weizenpflanzen bei unterschiedlicher N-Exposition und Trockenstress kultiviert. Für jede Pflanze wurden spektrale Reflexionsmessungen an drei Blattetagen und an drei Positionen auf dem Blatt durchgeführt. Insgesamt wurden die 16 üblichsten auf spektraler Reflexion basierenden Vegetationsindizes für jede Kombination von Blattetage und -Position berechnet. Die N-Konzentration der Pflanze wurde durch Laboranalysen bestimmt. Weder die Position auf dem Blatt noch die Blattetage hatten einen Einfluss auf die Genauigkeit der Bestimmung der N-Konzentration der Pflanze durch spektrale Reflexionsmessungen. Daher sind Messungen auf Bestandsebene ausreichend. Falls jedoch weitere Stressfaktoren wie Trockenheit oder Krankheitsbefall auftreten, kann eine Differenzierung zwischen verschiedenen Blattetagen notwendig oder von Vorteil sein. In der nächsten Fragestellung (Publikation II) wurde untersucht, wie Blattkrankheiten in ein DSSAT-Weizenmodell integriert werden können, um so die Auswirkungen von Blattkrankheiten auf den Ertrag zu simulieren. Eine Modellerweiterung wurde entwickelt, durch die Integration eines Blattkrankheitsmoduls in das bestehende DSSAT Weizenmodell. Das Modul simuliert die Auswirkungen des täglichen Schadens durch die Krankheit auf die Photosynthese und den Blattflächenindex. Der Ansatz wurde an einem zweijährigen Datensatz aus Argentinien mit verschiedenen Weizensorten und an einem einjährigen Datensatz aus Deutschland mit verschiedenen Inokulumniveaus von Septoria tritici-Blotch (STB) getestet. Die Sensitivitätsanalyse zeigte die Möglichkeit des Modells, den Ertrag in einer exponentiellen Beziehung mit zunehmendem Infektionsgrad (0-70%) zu reduzieren. Das erweiterte Modell stellt somit eine Möglichkeit dar, STB-Infektionen standortspezifisch in Verbindung mit verfügbaren Sensordaten zu simulieren. Um die Modellierungsgenauigkeit noch weiter zu erhöhen, wurde der Einsatz eines MM-Ansatz geprüft. Die Kombination von verschiedenen Modellen erhöht die Komplexität der Simulation und des damit verbundenen Kalibrierungsverfahrens, insbesondere wenn der Benutzer nicht mit allen Modellen vertraut ist. Die dritte Fragestellung (iii) untersuchte daher, ob objektive Kalibrierungsergebnisse gewährleitet werden könnten, wenn die cultivar coefficients im Modell auf Basis tatsächlich gemessener Daten mittels eines neu entwickelten automatischen Calibrator-Programms optimiert wurden. Die Modellkalibrierung wurde an einem 4-jährigen-Weizendüngungsversuch in Südwestdeutschland durchgeführt. Die statistische Auswertung des Kalibrierverfahrens zeigte zufriedenstellende Ergebnisse und führte zur vierten Fragestellung. Die vierte Fragestellung befasste sich mit dem Thema, ob ein MM-Ansatz die Gesamtmodelliergenauigkeit verbessern kann. Die Auswertung des Düngemittelversuchs zeigte in den meisten Fällen eine verbesserte Modellierungsgenauigkeit, insbesondere in einem durch Wasserstress geprägten Versuchsjahr wie 2018. Unter Verwendung eines MM-Ansatzes, durch Anpassung der Modellvariablen und durch die Integration von Sensordaten wurde ein Nitrogen Application Prescription System (NAPS) entwickelt. Eine Voraussetzung für das NAPS-Konzepts ist das Vorhandensein von Langzeit-Daten (Ertrag, Klima- und Bodenbedingungen), um eine korrekte MM-Kalibrierung zu gewährleisten. Nach der Kalibrierung werden die Bedingungen der aktuellen Wachstumssaison (Wetter, Managementinformationen) bis zum Düngetermin benötigt. Anschließend berechnet das NAPS basierend auf Sensorinformationen und simulierten Wetterbedingungen verschiedene Düngeszenarien. Ökonomische und ökologische Kriterien bestimmen die optimierte Düngemenge. Darüber hinaus muss das NAPS-Konzept auf räumlicher Ebene arbeiten, indem es die Bodenparameter berücksichtigt. So kann unter Beachtung der Feldvariabilität eine standortspezifische N-Ausbringung gewährleistet werden. In Summe zeigte sich, dass NAPS die Düngung an ökonomische und ökologische Faktoren anpasst, indem es aktuelle Sensordaten, historische Ertragsaufzeichnungen und zukünftige Wettervorhersagen zur Ermittlung einer präziseren N-Ausbringung nutzt. Das Konzept hat so das Potenzial, die nachteiligen Auswirkungen einer Überdüngung zu begrenzen, so dass eine umweltfreundlichere Agrarproduktion gewährleistet wird

Retrieval of biophysical parameters from multi-sensoral remote sensing data, assimilated into the crop growth model CERES-Wheat

This study investigated the possibilities and constraints for an integrated use of a crop growth model (CERES-Wheat) and earth observation techniques. The assimilation of information derived from earth observation sensors into crop growth models enables regional applications and may also help to improve the profound knowledge of the different involved processes and interactions. Both techniques can contribute to improved use of resources, reduced crop production risks, minimised environmental degradation, and increased farm income. Up to now, crop growth modelling and remote sensing techniquices mostly have been used separately for the assessment of agricultural applications. Crop growth models have made valuable contributions to, e.g., yield forecasting or to management decision support systems. Likewise, remote sensing techniques were successfully utilized in classification of agricultural areas or in the quantification of vegetation characteristics at various spatial and temporal scales. Multisensoral remote sensing approaches for the quantification biophysical variables are rarely realized. Normally the fusion of the data sources is based on the use of one sensor for classification purposes and the other one for the extraction of the desired parameters, based on the map classified previously. Pixel-based fusions between multispectral and SAR data is seldom realised for the assessment of quantitative parameters. The integration of crop growth models and remote sensing techniques by assimilating remotely sensed parameters into the models, is also still an issue of research. Especially, the integration of, e.g., multi-sensor biophysical parameter time-series for the improvement of the model performance, might feature a high potential. The starting point of the presented study was the question, if it is possible to derive the values of important crop variables from various remote sensing data? For the retrieval of these quantitative parameters by the use of various multispectral remote sensing sensors, intercalibration issues between the different retrieved vegetation indices had to be taken into account, in order to assure the comparability. Features influencing the vegetation indices are, e.g., the sensor geometry (like viewing- and solar-angle), atmospherical conditions, topography and spatial or radiometric resolution. However, the factors taken into account within this study are the spectral characteristics of the different sensors, like band position, bandwidth and centre wavelengths, which are described by the relative spectral response functions. Due to different RSR functions of the sensor bands, measured spectral differences occur, because the sensors record different components of the reflectance’s spectra from the monitored targets. These are then also introduced into the derived vegetation indices. The chosen cross-calibration method, intercalibrated the assessed Normalized Difference Vegetation Index and the Weighted Difference Vegetation Index between the various sensor pairs by regression, based on simulated multispectral sensors. Differences between the various assessed remote sensing sensors decreased form around 7% to below 1%. The intercalibration also had a positive impact on the later biophysical retrieval performance, producing sounder retrieval results. For the retrieval of the biophysical parameters empirical and semi-empirical models were assessed. The results indicate that the semi-empirical CLAIR model outperforms the empirical approaches. Not only for the Leaf Area Index retrieval, but also in the cases of all other assessed parameters. Concerning the other remote sensing data type used, the SAR data, it was analysed what potential different polarizations and incidence angles have for the extraction of the quantitative parameters. It became obvious that especially high incidence angles, as provided by the satellite Envisat ASAR, produce sounder retrieval results than lower incidence angles, due to a smaller amount of received soil signal. In the context of the assessed polarizations, sound results for the VV polarization could only be achieved for the retrieval of fresh biomass and the plant water content. For the ASAR sensor modelling fresh biomass and LAI using the HV polarization or the dry biomass using the ratio (HH/HV) was appropriate. As roughness aspects also have an influence on the retrieval performance from biophysical parameters using SAR data, the impact of soil surface and vegetation roughness was additionally considered. Best results were achieved, when also considering roughness features, however due to the need of regional modelling it is more appropriate not to consider them. For the calibration and re-tuning of crop growth models information about important phenological events such as heading/flowering is rather important. After this stage reproductive growth begins, whereby the number of kernels per plant is often calculated from plant weight at flowering and kernel weight is calculated from time and temperature available for dry matter distribution. By the use of the SAR VV time-series this important stage could be successfully extracted. Further methods for pixel-based fused biophysical parameter estimations, using SAR and multispectral data were analysed. By this approach the different features, being monitored of the two systems, are combined for sounder parameter retrieval. The assessed method of combining the multi-sensoral information by linear regression did not bring sound results and was outperformed by single sensor use, only taking into account the multispectral information. Only for the parameter fresh biomass, modelling based on the NDIV and the ASAR ratio slightly outperformed the single sensor modelling approaches. The complex combined modelling by the use of the CLAIR and the Water Cloud Model featured no valid results. For the combination, by using the CLAIR model and multiple regression slight improvements, in contrast to the single multispectral sensor use, were achieved. Especially, during late phenological stages, the assessed VV information improved the modelling results, in comparison to only using the CLAIR model. All the findings could finally be successfully applied for regional estimations. Only the roughness features could not be applied, due to the fact, that it is hard to regionally assess this needed model input parameter. Regional parameter on the basis of remote sensing data, is the major advantage of this technique, due to the large spatial overview given. The second main question was, if it is possible to integrate the crop variables gained from multisensoral data into a crop growth model, increasing the final yield estimation accuracy. Thus far, beneficial linkages between both techniques have been often limited to land use classification via remote sensing for choosing the adequate model and quantification of crop growth and development curves using biophysical parameters derived from remote sensing images for model calibration. Only a few studies actually considered the potentials of remote sensing for model re-initialization of growth and development characteristics of a specific crop, as the here studied winter wheat. Overall, the integration of remotely sensed variables into the crop growth model CERES-Wheat led to an improved final yield estimation accuracy in comparison to an automatic input parameter setting. The assessed final yield bias for the automatic input parameter setting summed up to 6.6%. When re-initializing the most sensitive input parameters (sowing date and fertilizer application date) by the use of remotely sensed biophysical variables the biases ranged from 0.56% overestimation to 5.4% understimation, in dependence of the data series used for assimilation. Whereby, it was assessed that the combined dense data series, considering SAR and multispectral information, slightly outperformed the performance of the full multispectral data series. However, when analysing the assimilation of the multispectral data series in further detail, it became clear that the actually information from the phenological stage ripening declines the modelling performance and thus the final yield estimation accuracy. When neglecting the information from this phenological stage the reduced multispectral data series performed as sound as the dense data series containing SAR and multispectral information. Thus, when the appropriate phenological stages are monitored by multispectral data, additional SAR information does not lead to a model improvement. However, when important dates are not monitored by multispectral images, e.g., due to cloud coverage, the additionally considered SAR information was not able to appropriatly fill these important multispectral time gaps. They even had a more negeative influence on the modelling performance. Overall, the best results could be obtained by assimilating a multispectral data series, covering the crop development during the important phenological stages stem elongation and flowering (without ripening stage), into the CERES-Wheat model. Finally, the integration of remote sensing data in the point-based crop growth model allowed it‘s spatial application for prediction of wheat production at a more regional scale. This approach also outperformed another evaluated method of direct multi-sensoral regional yield estimation. This study has demonstrated that biophysical parameters can be retrieved from remote sensing data and led, when assimilated into a crop growth model, to an improved final yield estimation. However, overall the SAR information did not really have a significant positive effect on the multi-sensoral biophysical parameter retrieval and on the later assimilation process. Thus, overall SAR information should only be considered, when multispectral data acquisitions are tremendously hampered by cloud coverage. The assessed assimilation of remote sensing information into a crop growth model had a positive effect on the final yield estimation performance. The analysed method, combining remote sensing and crop growth model techniques, was succsessfully demonstrated and will gain even more importance in the future for, e.g., decision support systems fine-tuning fertilizer regimes and thus contributing to more environmentally sound and sustained agricultural production

Assessment of Maize Yield Response to Agricultural Management Strategies Using the DSSAT-CERES-Maize Model in Trans Nzoia County in Kenya

Maize production in low-yielding regions is influenced by climate variability, poor soil fertility, suboptimal agronomic practices, and biotic influences, among other limitations. Therefore, the assessment of yields to various management practices is, among others, critical for advancing site-specific measures for production enhancement. In this study, we conducted a multiseason calibration and evaluation of the DSSAT-CERES-Maize model to assess the maize yield response of two common cultivars grown in Trans Nzoia County in Kenya under various agricultural strategies, such as sowing dates, nitrogen fertilization, and water management. We then applied the Mann-Kendall (MK), and Sen's Slope Estimator (SSE) tests to establish the yield trends and magnitudes of the different strategies. The evaluated model simulated long-term yields (1984-2021) and characterized production under various weather regimes. The model performed well in simulating the growth and development of the two cultivars, as indicated by the model evaluation results. The RMSE for yield was 333 and 239 kg ha(-1) for H614 and KH600-23A, respectively, representing a relative error (RRMSE) of 8.1 and 5.1%. The management strategies assessment demonstrated significant feedback on sowing dates, nitrogen fertilization, and cultivars on maize yield. The sowing date conducted in mid-February under fertilization of 100 kg of nitrogen per hectare proved to be the best strategy for enhancing grain yields in the region. Under the optimum sowing dates and fertilization rate, the average yield for cultivar KH600-23A was 7.1% higher than that for H614. The MK and SSE tests revealed a significant (p < 0.05) modest downwards trend in the yield of the H614 cultivar compared to the KH600-23A. The eastern part of Trans Nzoia County demonstrated a consistent downwards trend for the vital yield enhancement strategies. Medium to high nitrogen levels revealed positive yield trends for more extensive coverage of the study area. Based on the results, we recommend the adoption of the KH600-23A cultivar which showed stability in yields under optimum nitrogen levels. Furthermore, we recommend measures that improve soil quality and structure in the western and northern parts, given the negative model response on maize yield in these areas. Knowledge of yield enhancement strategies and their spatial responses is of utmost importance for precision agricultural initiatives and optimization of maize production in Trans Nzoia County

Evaluating remotely piloted aircraft estimates of crop height and LAI against satellite and crop model outputs

Crop simulation models (CSM) have been a method for decision makers to study the effects of crop management activities for predicting, planning, and improving crop growth for the past several decades. While the applicability and robustness of CSMs had been rapidly evolving, the methods of gathering input and validation data for CSMs has remained predominantly the same. However, the application of remote sensing technologies including remotely piloted aircraft systems (RPAS) and satellites for agricultural purposes has demonstrated the potential for automated rapid and high detail CSM validation data. This study evaluated the accuracy of validation data acquired using RPAS and satellite technologies when compared to CSM outputs and observed crop measurements. Imagery of an agricultural field was acquired throughout a growing season with the use of a multi-sensor RPAS and existing satellite missions. Field work was performed alongside the RPAS imagery acquisitions to collect input data for crop modelling and accuracy assessments. Using the acquired imagery, the crop height and leaf area index (LAI) values of crops in the field were estimated for multiple dates. The LAI was estimated using 1) a regression-based method and 2) a function of the fractional vegetation cover and the leaf angle distribution method. A CSM was run alongside the remote sensing to simulate crop height and LAI values. When the estimated values were compared to observed measurements, showing the RPAS-derived crop height values were significantly more accurate (RMSE=193.6 cm, RMSE=161.3 cm) than the satellite-derived crop heights values (RMSE=223.4 m, RMSE=117.1 m respectively) yet less accurate than the CSM crop heights values. The RPAS-derived LAI value accuracies (RMSE=0.42, RMSE=0.66) and satellite-derived LAI value accuracies (RMSE=0.56, RMSE=0.56) were similar but the RPAS was found to, on average, estimate LAI more accurately than the CSM. Overall, the RPAS methods showed moderate accuracy across both crop height and LAI estimations and was found to perform better than the CSM in some situations. Future work may include additional imagery acquisitions throughout a growing season to further test the accuracies of RPAS-derived estimates as well as integrating estimates directly into CSMs for validation purposes

Mapping and modeling groundnut growth and productivity in rainfed areas of Tamil Nadu

A research study was conducted at Tamil Nadu Agricultural University, Coimbatore during kharif and rabi 2015 to estimate groundnut area, model growth and productivity and assess the vulnerability of groundnut to drought using remote sensing techniques. Multi temporal Sentinel 1A satellite data at VV and VH polarization with 20 m spatial resolution was acquired from May, 2015 to January, 2016 at 12 days interval and processed using MAPscape-RICE software. Continuous monitoring was done for ground truth on crop parameters in twenty monitoring sites and validation exercise was done for accuracy assessment. Input files on soil, weather and management practices were generated and crop coefficients pertaining to varieties were developed to assess growth and productivity of groundnut using DSSAT CROPGRO-Peanut model. Outputs from remote sensing and DSSAT model were assimilated to generate LAI thereby groundnut yield spatially and validated against observed yields. Being a rainfed crop, vulnerability of groundnut to drought was assessed integrating different meteorological and spectral indices viz., Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI) and Water Requirement Satisfaction Index (WRSI).Spectral dB curve of groundnut was generated using temporal multi date Sentinel 1A data. A detailed analysis of temporal signatures of groundnut showed a minimum at sowing and a peak at pod development stage and decreasing thereafter towards maturity. Groundnut crop expressed a significant temporal behaviour and large dynamic range (-11.74 to -5.31 in VV polarization and -20.04 to -13.05 in VH polarization) during its growth period. Groundnut area map was generated using maximum likelihood classifier integrating multi temporal features with a classification accuracy of 87.2 per cent and a kappa score of 0.74. The total classified groundnut area in the study districts was 88023 ha covering 17817 and 22582 ha in Salem and Namakkal districts during kharif 2015 while Villupuram and Tiruvannamalai districts accounted for 22722 and 24903 ha respectively during rabi 2015. Blockwise statistics on groundnut area during both seasons were also generated. To model growth and productivity of groundnut in DSSAT, weather and soil input files were generated using weatherman and ‘S’ build respectively besides deriving genetic coefficients for CO 6, TMV 7 and VRI 2 varieties of groundnut. Growth and development variables of groundnut were simulated using CROPGROPeanut model i.e., days to emergence (7-9 days) and anthesis (25-32 days), canopy height (63 to 70 cm), maximum LAI (1.12 to 3.07) and biomass (4176 to 9576 kg ha-1 across twenty monitoring locations spatially. The resultant pod yield was simulated to be 1796 to 3060 kg ha-1 with a harvest index of 0.28 to 0.43. On comparison of LAI between observed (2.01 to 4.05) and simulated values (1.12 to 3.07) the CROPGRO-Peanut model was found to under estimate the values with R2, RMSE and NRMSE of 0.82, 1.10 and 34 per cent. However, the model predicted the biomass of groundnut with an agreement of 89 per cent through the simulated values of 4176 to9576 kg ha-1 as against the observed biomass to 4620 to 9959 kg ha-1. The simulated pod yields of groundnut in the study area were 1796 to 3060 kg ha-1 as compared to the observed yields of 2115 to 2750 kg ha-1. The overall agreement between simulated and observed yields was 84 per cent with the average errors of 0.81, 342 kg ha-1 and 16 percent for R2, RMSE and NRMSE respectively. LAI values of groundnut, generated spatially through suitable regression models using dB from satellite images and LAI from DSSAT, ranged from 1.31 to 3.23 with R2, RMSE and NRMSE of 0.86, 0.78 and 24 per cent respectively on comparison with observed values. Remote sensing based spatial estimation resulted in groundnut pod yields of 1570 to 3102 kg ha-1 across the study districts of Salem, Namakkal, Tiruvannamalai and Villupuram. In the 20 monitoring locations, the pod yields were estimated to be 1912 to 2975 kg ha-1 as against the observed pod yields of 1450 to 2750 kg ha-1 with a fairly good agreement of 80 per cent. The vulnerability of groundnut was assessed using different drought indices viz., SPI, NDVI and WRSI. Considering SPI, out of the total groundnut area of 88023 ha, an area of 86607 ha was found to be under near normal condition based on deviation of rainfall received during cropping season from historical precipitation. Similarly NDVI, an indicator of vegetation condition during the cropping season, showed that 14272 ha of groundnut area were under stressed condition during 2015. An area of 40981 ha in Villupuram and Tiruvannamalai districts was found to be under chances of crop failure based on Water Requirement Satisfaction index (WRSI). Major groundnut areas of Salem district (14188 ha) was under medium risk zone. Considering overall vulnerability, whole district of Villupuram was adjudged as highly vulnerable to drought with regard to groundnut cultivation whereas four blocks of Salem, eight blocks of Namakkal and all the blocks of Tiruvannamalai were found to be moderately vulnerable to drought

Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction

The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems. This study examines the applicability of EOs obtained from Sentinel-2 and Landsat-8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel-2 and Landsat-8 NDVI (Normalized Difference Vegetation Index) to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different calibration sites in the U.S. Midwest. The novelty of the current study lies in its approach in providing a mathematical framework to directly integrate EOs into process-based models for improved parameter estimation and system representation. Thus, a time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI (Leaf Area Index) estimates in APSIM-Maize model. Then surrogate models were developed using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. The lowest RMSE within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions for within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S. Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements