Implementing an Agro-Environmental Information System (AEIS) Based on GIS, Remote Sensing, and Modelling -- A case study for rice in the Sanjiang Plain, NE-China

Information on agro-ecosystems is crucial for understanding the agricultural production and its impacts on the environment, especially over large agricultural areas. The Sanjiang Plain (SJP), covering an area of 108 829 km², is a critical food base located in NE-China. Rice, soya bean and maize are the major crops in the SJP which are sold as commercial grain throughout China. The aim of this study is to set up an Agro-Environmental Information System (AEIS) for the SJP by employing the technologies of geographic information systems (GIS), remote sensing (RS), and agro-ecosystem modelling. As the starting step, data carrying interdisciplinary information from multiple sources are organized and processed. For an AEIS, geospatial data have to be acquired, organized, operated, and even regenerated with good positioning conditions. Georeferencing of the multi-source data is mandatory. In this thesis, high spatial accuracy TerraSAR-X imagery was used as a reference for georeferencing raster satellite data and vector GIS topographic data. For the second step, the georeferenced multi-source data with high spatial accuracy were integrated and categorized using a knowledge-based classifier. Rice was analysed as an example crop. A rice area map was delineated based on a time series of three high resolution FORMOSAT-2 (FS-2) images and field observed GIS topographic data. Information on rice characteristics (i.e., biomass, leaf area index, plant nitrogen concentration and plant nitrogen uptake) was derived from the multi-temporal FS-2 images. Spatial variability of rice growing status on a within-field level was well detected. As the core part of the AEIS, an agro-ecosystem modelling was then applied and subsequently crops and the environmental factors (e.g., climate, soil, field management) are linked together through a series of biochemical functions inherent in the modelling. Consequently, the interactions between agriculture and the environment are better interpreted. In the AEIS for the SJP, the site-specific mode of the DeNitrification-DeComposition (DNDC) model was adapted on regional scales by a technical improvement for the source code. By running for each pixel of the model input raster files, the regional model assimilates raster data as model inputs automatically. In this study, detailed soil data, as well as the accurate field management data in terms of crop cultivation area (i.e. rice) were used as model inputs to drive the regional model. Based on the scenario optimized from field observation, rice yields over the Qixing Farm were estimated and the spatial variability was well detected. For comparison, rice yields were derived from multi-temporal FS-2 images and the spatial patterns were analysed. As representative environmental effects, greenhouse gas of nitrous oxide (N2O) and carbon dioxide (CO2) emitted from the paddy rice fields were estimated by the regional model. This research demonstrated that the AEIS is effective in providing information about (i) agriculture on the region, (ii) the impacts of agricultural practices on the environment, and (iii) simulation scenarios for sustainable strategies, especially for the regional areas (e.g. the SJP) that is lacking of geospatial data

Combining Multitemporal Microwave and Optical Remote Sensing Data. Mapping of Land Use / Land Cover, Crop Type, and Crop Traits

Humanity has changed the earth’s surface to a dramatic extent. This is especially true for the area used for agricultural production. Against the background of a growing world population and the associated increased demand for food, it is precisely this area that will become even more important in the future. In order not to have to allocate even more land to agricultural use, optimization and intensification is the only way out of the dilemma. In this context, precise Geoinformation of the agriculturally used area is of central importance. It is utilized for improving land use, producing yield forecasts for more stable food security, and optimizing agricultural management. Rapid developments in the field of satellite-based remote sensing sensors make it possible to monitor agricultural areas with increased spatial, spectral and temporal resolution. However, to retrieve the needed information from this data, new methods are needed. Furthermore, the quality of the data has to be verified. Only then can the presented geodata help to grow crops more sustainably and more efficiently. This thesis develops new approaches for monitoring agricultural areas using the technology of microwave remote sensing in combination with optical remote sensing and existing geodata. It is framed by the overall objective to obtain knowledge on how this combination of data can provide the necessary geoinformation for land use studies, precision farming, and agricultural monitoring systems. Hundreds of remote sensing images from more than eight different satellites were analyzed in six research studies from two different Areas of Interest (AOIs). The studies guide through various spatial scales. First, the general Land Use / Land Cover (LULC) on a regional level in a multi-sensor scenario is derived, evaluating different sensor combinations of varying resolutions. Next, an innovative method is proposed, through which the high geometric accuracy of radar-imaging satellite sensors is exploited to update the spatial accuracy of any external geodata of lower spatial accuracy. Such external data is then used in the next two studies, which focus on cost-effective crop type mapping using Synthetic Aperture Radar (SAR) images. The resulting enhanced LULC maps present the annually changing crop types of the region alongside external, official geoinformation that is not retrievable from remote sensing sensors. The last two research studies deal with a single maize field, on which high resolution optical WorldView-2 images and experimental bistatic SAR observations from TanDEM-X are assessed and combined with ground measurements. As a result, this thesis shows that, depending on the AOI and the application, different resolution demands need to be fulfilled before LULC, crop type, and crop traits mapping can be performed with adequate accuracy. The spatial resolution needs to be adapted to the particularities of the AOI. Evaluation of the sensors showed that SAR sensors proved beneficial for the study objective. Processing the SAR images is complicated, and the images are unintuitive at first sight. However, the advantage of SAR sensors is that they work even in cloudy conditions. This results in an increased temporal resolution, which is particularly important for monitoring the highly dynamic agricultural area. Furthermore, the high geometric accuracy of the SAR images proved ideal for implementing the Multi-Data Approach (MDA). Thus information-rich external geodata could be used to lower the remote sensing resolution needs, improve the accuracy of the LULC-maps, and to provide enhanced LULC-maps. The first study of the maize field demonstrates the potential of the WorldView-2 data in predicting in-field biomass variations, and its increased accuracy when fused with plant height measurements. The second study shows the potential of the TanDEM-X Constellation (TDM) to retrieve plant height from space. LULC, crop type and information on the spatial distribution of biomass can thus be derived efficiently and with high accuracy from the combination of SAR, optical satellites and external geodata. The shown analyses for acquiring such geoinformation represent a high potential for helping to solve the future challenges of agricultural production

Evaluating and Developing Methods for Non-Destructive Monitoring of Biomass and Nitrogen in Wheat and Rice Using Hyperspectral Remote Sensing

Aboveground plant biomass and plant nitrogen are two important parameters for plant growth monitoring, which have a decisive influence on the final yield. Mismanagement of fertilizer or pesticide inputs leads to poor plant growth, environmental pollution, and accordingly, yield loss. Biomass development is driven by nutrient supply, temperature, and phenology. Crop biomass reaches its highest weight at the harvest time. In contrast, plant nitrogen is dependent from fertilizer inputs to the soil and from biomass. Destructive measurement of both parameters is time-consuming and labor-intensive. Remote sensing offers remotely non-direct observation methods from outer space, air space, or close-range in the field by sensors. This dissertation focuses on non-destructive monitoring of plant biomass (the primary parameter) and plant nitrogen (the secondary parameter) using hyperspectral data from non-imaging field spectrometers and the imaging EO-1 Hyperion satellite. The study was conducted on two field crops: winter wheat of two growing seasons of the Huimin test site in the North China Plain; and rice of three growing seasons of the Jiansanjiang test site in the Sanjiang Plain of China. Study fields were set up in different spatial scales, from small experimental scale to large farmers' scale. Extensive field measurements were carried out, including both destructive measuring and non-destructive hyperspectral remote sensing of biomass and plant nitrogen. Besides, two years' Hyperion images were acquired at the Huimin test site. Four different approaches were used to develop the estimation models, which include: vegetation indices (VIs), band combinations, Optimum Multiple Narrow Band Reflectance (OMNBR) and stepwise Multiple Linear Regression (MLR), and derivatives of reflectance. Based on these four approaches, models were constructed, compared, and improved step by step. Additionally, a multiscale approach and a new VI, named GnyLi, were developed. Since experimental and farmers' fields were differently managed, several calibration and validation methods were tested and the field datasets were pooled. All tested approaches and band selections were greatly influenced by single growth stages. The broad band VIs saturated for both crops at the booting stage at the latest and were greatly outperformed by the narrow band VIs with optimized band combinations. Model applications from experimental to farmers' scale using the narrow bands measured by field spectrometers mostly failed due to the effects of different management practices and crop cultivars at both spatial scales. In contrast, the multiscale approach was successfully applied in winter wheat monitoring to transfer data and knowledge from field spectrometer measurements from the experimental scale to the farmers' field scale and the scale that is covered by the Hyperion imagery. The GnyLi and the Normalized Ratio Index (NRI) based on the optimized band combinations performed the best in the up-scaling process in the winter wheat study. In the rice study, MLR or OMNBR models based on 4–6 narrow bands better explained biomass variability compared to VIs based on broad bands and optimized band combinations. The models were more robust when data from different scales were pooled and then randomly divided into calibration and validation datasets. Additional model improvements were obtained using derivatives of reflectance. This dissertation evaluates different hyperspectral remote sensing approaches for non-destructive biomass and plant nitrogen monitoring, with the main focus on biomass estimation. The results and comparisons of different approaches revealed their potentials and limits. Development of new VIs, such as GnyLi, is advantageous due to the saturation problem of broad band VIs. However, the developed VIs need to be tested and improved for different crops and sites. Detection of optimized band combinations facilitates the development of new VIs, which are site-specific and crop-specific. MLR-based models may better explain the biomass variability; nevertheless, with more bands, they are prone to the issues of over-fitting and collinearity. Hence, no more than six bands were recommended to select from the hyperspectral data. Derivatives of reflectance were beneficial at the early growing season of rice when the canopy was strongly influenced by background signals from soil and water. However, their benefits were reduced when more bands were used

Satellite and Fluorescence Remote Sensing for Rice Nitrogen Status Diagnosis in Northeast China

Nitrogen (N), as the most important element of crop growth and development, plays a decisive role in ensuring yield. However, the problems of over-application of N fertilizers have been repeatedly reported in China, which resulted in low N use efficiency and high risk of environmental pollution. The requirements of developing technologies for real-time and site-specific diagnosis of crop N status are the foundation to realize the precision N management, and also benefit to the improvement of the N use efficiency. Remote sensing technology provides a promising non-intrusive solution to monitor rice N status and to realize the precision N management over large areas. This research focuses on proposing N nutrition diagnosis methods and developing N fertilizer management strategies for paddy rice of cold regions in Northeast China. The main contents and results are presented as follows: (1)This study developed a new critical N (Nc) dilution curve for paddy rice of cold regions in Northeast China. The curve could be described by the equation Nc=27.7W^(-0.34) if W≥1 t/ha for dry matter (DM) or Nc=27.7g/kg DM if W<1 t/ha, where W is the aboveground biomass. Results indicated that the new Nc dilution curve was suitable for diagnosing short-season Japonica rice N status in Northeast China. The validation result indicated that it worked well to diagnose plant N status of the 11-leaf variety rice. (2)This study investigated the potential of the satellite remote sensing data for diagnosing rice N status and guiding the topdressing N application at the stem elongation stage in Northeast China. 50 vegetation indices (VIs) were computed based on the FORMOSAT-2 satellite data, and they were correlated with the field-based agronomic variables, i.e., aboveground biomass (AGB), leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), chlorophyll meter readings, and N nutrition index (NNI, defined as the ratio of actual PNC and critical PNC according to the new Nc dilution curves). The results presented that 45% of variation in the NNI was obtained by using a direct estimation method based on the best VI according to the FORMOSAT-2 satellite data, while 52% of the variation in the NNI was yielded by an indirect estimation method, which firstly used the VIs to estimate AGB and PNU, respectively, then estimated NNI according to these two variables. Moreover, based on the critical N uptake curve, a N recommendation algorithm was proposed. The algorithm was based on the difference between the estimated PNU and the critical PNU to adjust the topdressing N application rate. The results demonstrated that FORMOSAT-2 images have the potential to estimate rice N status and guide panicle N fertilizer applications in Northeast China. (3)This study also evaluated the potential improvements of the newest satellite sensors with the red edge band for diagnosing rice N status in Northeast China. The canopy-scale hyperspectral data were upscaled to simulate the wavebands of RapidEye, WorldView-2, and FORMOSAT-2, respectively. The VI analysis, stepwise multiple linear regression (SMLR), and partial least squares regression (PLSR) were performed to evaluate the N status indicators. The results indicated that the VIs based on the RE band from RapidEye and WorldView-2 data could explain more variability for N indicators than the VIs from FORMOSAT-2 data having no RE band. Moreover, the SMLR and PLSR results revealed that both the near-infrared and red edge band were important for N status estimation. (4)The proximal fluorescence sensor Multiplex_3 was used to evaluate the potential of fluorescence spectrum for estimating the N status of the cold regional paddy rice at different growth stages. The Multiplex indices and their normalized N sufficient indices (NSI) were used to estimate the five N status indicators, i.e., AGB, leaf N concentration (LNC), PNC, PNU, and NNI. The results indicated that there were strong relationships between the fluorescence indices (i.e., BRR_FRF, FLAV, NBI_G, and NBI_R) and (i.e., LNC, PNC, NNI), with the coefficient of determination between 0.40 and 0.78. In particular, NNI was well estimated by these fluorescence indices. Moreover, the NSI data improved the accuracy of the N diagnosis. These results of this study were useful for N nutrition diagnosis and variable fertilization of the cold regional paddy rice, which were significant for the ecological environment protection and the national food security

Crop Growth Monitoring by Hyperspectral and Microwave Remote Sensing

Methoden und Techniken der Fernerkundung fungieren als wichtige Hilfsmittel im regionalen Umweltmanagement. Um diese zu optimieren, untersucht die folgende Arbeit sowohl die Verwendung als auch Synergien verschiedener Sensoren aus unterschiedlichen Wellenlängenbereichen. Der Fokus liegt auf der Modellentwicklung zur Ableitung von Pflanzenparametern aus fernerkundlichen Bestandsmessungen sowie auf deren Bewertung. Zu den verwendeten komplementären Fernerkundungssystemen zählen die Sensoren EO-1 Hyperion und ALI, Envisat ASAR sowie TerraSAR-X. Für die optischen Hyper- und Multispektralsysteme werden die Reflexion verschiedener Spektralbereiche sowie die Performanz der daraus abgeleiteten Vegetationsindizes untersucht und bewertet. Im Hinblick auf die verwendeten Radarsysteme konzentriert sich die Untersuchung auf Parameter wie Wellenlänge, Einfallswinkel, Radarrückstreuung und Polarisation. Die Eigenschaften verschiedener Parameterkombinationen werden hierbei dargestellt und der komplementäre Beitrag der Radarfernerkundung zur Wachstumsüberwachung bewertet. Hierzu wurden zwei Testgebiete, eines für Winterweizen in der Nordchinesischen Tiefebene und eines für Reis im Nordosten Chinas ausgewählt. In beiden Gebieten wurden während der Wachstumsperioden umfangreiche Feldmessungen von Bestandsparametern während der Satellitenüberflüge oder zeitnah dazu durchgeführt. Mit Hilfe von linearen Regressionsmodellen zwischen Satellitendaten und Biomasse wird die Sensitivität hyperspektraler Reflexion und Radarrückstreuung im Hinblick auf das Wachstum des Winterweizens untersucht. Für die optischen Daten werden drei verschiedene Modelvarianten untersucht: traditionelle Vegetationsindices berechnet aus Multispektraldaten, traditionelle Vegetationsindices berechnet aus Hyperspektraldaten sowie die Berechnung von Normalised Ratio Indices (NRI) basierend auf allen möglichen 2-Band Kombinationen im Spektralbereich zwischen 400 und 2500 nm. Weiterhin wird die gemessene Biomasse mit der gleichpolarisierten (VV) C-Band Rückstreuung des Envisat ASAR Sensors linear in Beziehung gesetzt. Um den komplementären Informationsgehalt von Hyperspektral und Radardaten zu nutzen, werden optische und Radardaten für die Parameterableitung kombiniert eingesetzt. Das Hauptziel für das Reisanbaugebiet im Nordosten Chinas ist das Verständnis über die kohärente Dualpolarimetrische X-Band Rückstreuung zu verschiedenen phänologischen Wachstumsstadien. Hierfür werden die gleichpolarisierte TerraSAR-X Rückstreuung (HH und VV) sowie abgeleitete polarimetrische Parameter untersucht und mit verschiedenen Ebenen im Bestand in Beziehung gesetzt. Weiterhin wird der Einfluss der Variation von Einfallswinkel und Auflösung auf die Bestandsparameterableitung quantifiziert. Neben der Signatur von HH und VV ermöglichen vor allem die polarimetrischen Parameter Phasendifferenz, Ratio, Koherenz und Entropy-Alpha die Bestimmung bestimmter Wachstumsstadien. Die Ergebnisse der Arbeit zeigen, dass die komplementären Fernerkundungssysteme Optik und Radar die Ableitung von Pflanzenparametern und die Bestimmung von Heterogenitäten in den Beständen ermöglichen. Die Synergien diesbezüglich müssen auch in Zukunft weiter untersucht werden, da neue und immer variablere Fernerkundungssysteme zur Verfügung stehen werden und das Umweltmanagement weiter verbessern können

Within-Field Yield Prediction for Sugarcane and Rice Focused on Precision Agriculture Applications

Food and energy security are two main topics when it comes to the on-growing world population. Rice and sugarcane play an important role in this scenario since sugarcane can be used for energy production and rice is one of major staple cereals. In this scenario, Precision Agriculture (PA) management strategies aims to improve productivity, efficiency, profitability, and sustainability, and can help agriculture to fulfill the needs of the growing population in a sustainable way. However, yield maps are essential for PA, but its adoption is still very low. Thus, the main objective of this study was to evaluate the potential of satellite imagery and machine learning to predict yield maps that could support the adoption of precision agriculture practices for rice and sugarcane. Consequently, a framework for the data processing, imagery acquisition and machine learning model generation, was proposed and tested. The results presented a high potential for the usage of those techniques, generating yield maps very similar to the ones obtained from yield monitors (RMSE for rice of 0.9Mg.ha-1 and for sugarcane 3.14Mg.ha-1). Also, in-season yield map prediction was evaluated for rice and sugarcane. Therefore, the prediction was performed for different growth stages by stacking all the images until a specific date. Sugarcane maps were obtained with a satisfactory accuracy early in the season (May-June) (no statistical significance when compared to the predicted maps of the end of the season) whilst for rice the yield maps with the lowest errors were only obtained late in the season. Therefore, sugarcane maps obtained early in the season could be used for in-season management of the crop. On the other hand, the in-season applicability for rice yield maps were limited since accurate maps were obtained at late ripening. However, this information could still be used for harvest planning and nitrogen application on the second harvest of Louisiana’s rice. In general, the framework proposed presented a high potential to be used for yield maps prediction. Furthermore, yield maps, an important tool for PA, were obtained with low errors RMSE of 0.83 and 3.14 Mg.ha-1 for rice and sugarcane, respectively

Rice crop classification and yield estimation using multi-temporal sentinel-2 data: a case study of Terai districts of Nepal

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesCrop monitoring, especially in developing countries, can improve food production, address food security issues, and support sustainable development goals. Crop type mapping and yield estimation are the two major aspects of crop monitoring that remain challenging due to the problem of timely and adequate data availability. Existing approaches rely on ground-surveys and traditional means which are time-consuming and costly. In this context, we introduce the use of freely available Sentinel-2 (S2) imagery with high spatial, spectral and temporal resolution to classify crop and estimate its yield through a deep learning approach. In particular, this study uses patch-based 2D and 3D Convolutional Neural Network (CNN) algorithms to map rice crop and predict its yield in the Terai districts of Nepal. Firstly, the study reviews the existing state-of-art technologies in this field and selects suitable CNN architectures. Secondly, the selected architectures are implemented and trained using S2 imagery, groundtruth and auxiliary data in addition for yield estimation.We also introduce a variation in the chosen 3D CNN architecture to enhance its performance in estimating rice yield. The performance of the models is validated and then evaluated using performance metrics namely overall accuracy and F1-score for classification and Root Mean Squared Error (RMSE) for yield estimation. In consistency with the existing works, the results demonstrate recommendable performance of the models with remarkable accuracy, indicating the suitability of S2 data for crop mapping and yield estimation in developing countries. Reproducibility self-assessment (https://osf.io/j97zp/): 2, 2, 2, 1, 2 (input data, preprocessing, methods, computational environment, results)

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production