Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province, China

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere

Iz stranih časopisa

U tekstu je dan popis radova koji su objavljeni u stranim časopisima

Monitoring the Sustainable Intensification of Arable Agriculture:the Potential Role of Earth Observation

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data

Sustainable intensification of arable agriculture:The role of Earth Observation in quantifying the agricultural landscape

By 2050, global food production must increase by 70% to meet the demands of a growing population with shifting food consumption patterns. Sustainable intensification has been suggested as a possible mechanism to meet this demand without significant detrimental impact to the environment. Appropriate monitoring techniques are required to ensure that attempts to sustainably intensify arable agriculture are successful. Current assessments rely on datasets with limited spatial and temporal resolution and coverage such as field data and farm surveys. Earth Observation (EO) data overcome limitations of resolution and coverage, and have the potential to make a significant contribution to sustainable intensification assessments. Despite the variety of established EO-based methods to assess multiple indicators of agricultural intensity (e.g. yield) and environmental quality (e.g. vegetation and ecosystem health), to date no one has attempted to combine these methods to provide an assessment of sustainable intensification. The aim of this thesis, therefore, is to demonstrate the feasibility of using EO to assess the sustainability of agricultural intensification. This is achieved by constructing two novel EO-based indicators of agricultural intensity and environmental quality, namely wheat yield and farmland bird richness. By combining these indicators, a novel performance feature space is created that can be used to assess the relative performance of arable areas. This thesis demonstrates that integrating EO data with in situ data allows assessments of agricultural performance to be made across broad spatial scales unobtainable with field data alone. This feature space can provide an assessment of the relative performance of individual arable areas, providing valuable information to identify best management practices in different areas and inform future management and policy decisions. The demonstration of this agricultural performance assessment method represents an important first step in the creation of an operational EO-based monitoring system to assess sustainable intensification, ensuring we are able to meet future food demands in an environmentally sustainable way

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

Investigating the feasibility of using remote sensing in index-based crop insurance for South Africa’s smallholder farming systems

Crop farming in Sub-Saharan Africa (SSA) is largely practiced by resource-poor farmers under rain-fed and unpredictable weather conditions. Since agriculture is the mainstay of SSA’s economy, the lack of improved and adapted agricultural technologies in this region sets back economic development and the fight against poverty. Overcoming this constraint and achieving the sustainable development goal to end poverty, requires innovative tools that can be used for weather risk management. One tool that has been gaining momentum recently is index-based crop insurance (IBCI). Since the launch of the first IBCI program in Africa around 2005, the number of IBCI programs has increased. Unfortunately, these programs are constrained by poor product design, basis risk, and low uptake of contracts. When these issues were first pointed-out in the earliest IBCI programs, many reports suggested satellite remote sensing (RS) as a viable solution. Hence, the first objective of this study was to assess how RS has been used in IBCI, the challenges RS faces, and potential contributions of RS that have not yet been meaningfully exploited. The literature shows that IBCI programs are increasingly adopting RS. RS has improved demarcation of unit areas of insurance and enabled IBCI to reach inaccessible areas that do not have sufficient meteorological infrastructure. However, the literature also shows that IBCI is still tainted by basis risk, which emanates from poor contract designs, the influence of non-weather factors on crop yields, imperfect correlations between satellite-based indices and crop yields, and the lack of historical data for calibration. Although IBCI reports cover vegetation and crop health monitoring, few to none cover crop type and crop area mapping. Furthermore, areas including high-resolution mapping, data fusion, microwave RS, machine learning, and computer vision have not been sufficiently tested in IBCI. The second objective of this study was to assess how RS and machine learning techniques can be used to enhance the mapping of smallholder crop farming landscapes. The findings show that machine learning ensembles and the combination of optical and microwave data can map a smallholder farming landscape with a maximum accuracy of 97.71 percent. The third objective was to identify factors that influence crop yields and crop losses in order to improve IBCI design. Results demonstrated that the pervasive notion that low yields in smallholder farms are related to rainfall is an oversimplification. Factors including fertilizer use, seed variety, soil properties, soil moisture, growing degree-days, management, and socioeconomic conditions are some of the most important factors influencing crop yields and crop losses in smallholder farming systems. This shows why IBCI needs to be part of a comprehensive risk management system that understands and approaches smallholder crop farming as complex by linking insurance with advisories and input supplies. Improved inputs and good farming practices could reduce the influence of non-weather factors on crop losses, and thereby reduce basis risk in weather-based index insurance (WII) contracts. The fourth objective of this study was to assess how well the combination of synthetic aperture radar (SAR) and optical indices estimate soil moisture. As stated earlier, soil moisture was found to be one of the most important factors affecting crop yields. Although this method better estimated soil moisture over the first half of the growing season, estimation accuracies were comparable to those found in studies that had used similar datasets (RMSE = 0.043 m3 m-3, MAE = 0.034 m3 m- 3). Further interrogation of interaction effects between the variables used in this study and consideration of other factors that affect SAR backscatter could improve the method. More importantly, incorporating high-resolution satellite-based monitoring of soil moisture into IBCI could potentially reduce basis risk. The fifth objective of this study was to develop an IBCI for smallholder crop farming systems. The proposed IBCI scheme covers maize and derives index thresholds from crop water requirements and satellite-based rainfall estimates. It covers rainfall deficits over the vegetative, mid-season, and late-season stages of maize growth. The key contribution of this system is the derivation of index thresholds from CWR and site-specific rainfall conditions. The widely used approach, which calibrates IBCI by correlating yields and rainfall, exposes contracts to basis risk because, by simply correlating yield and rainfall data, it overlooks the influence of non-weather factors on crop yields and losses. The proposed system must be linked or bundled with non-weather variables that affect crop yields. Effectively, this means that the insurance must be linked or bundled with advisories and input supplies to address the influence of non-weather factors on crop losses. This system also incorporates a crop area-mapping component, which was found to be lacking in many IBCI systems. In conclusion, an IBCI that is based on crop water requirements, which incorporates crop area mapping and links insurance with non-weather crop yield-determining factors, is potentially capable of improving crop insurance for smallholder farming systems.Thesis (PhD) -- Faculty of Science and Agriculture, 202

Investigating the feasibility of using remote sensing in index-based crop insurance for South Africa’s smallholder farming systems

Crop farming in Sub-Saharan Africa (SSA) is largely practiced by resource-poor farmers under rain-fed and unpredictable weather conditions. Since agriculture is the mainstay of SSA’s economy, the lack of improved and adapted agricultural technologies in this region sets back economic development and the fight against poverty. Overcoming this constraint and achieving the sustainable development goal to end poverty, requires innovative tools that can be used for weather risk management. One tool that has been gaining momentum recently is index-based crop insurance (IBCI). Since the launch of the first IBCI program in Africa around 2005, the number of IBCI programs has increased. Unfortunately, these programs are constrained by poor product design, basis risk, and low uptake of contracts. When these issues were first pointed-out in the earliest IBCI programs, many reports suggested satellite remote sensing (RS) as a viable solution. Hence, the first objective of this study was to assess how RS has been used in IBCI, the challenges RS faces, and potential contributions of RS that have not yet been meaningfully exploited. The literature shows that IBCI programs are increasingly adopting RS. RS has improved demarcation of unit areas of insurance and enabled IBCI to reach inaccessible areas that do not have sufficient meteorological infrastructure. However, the literature also shows that IBCI is still tainted by basis risk, which emanates from poor contract designs, the influence of non-weather factors on crop yields, imperfect correlations between satellite-based indices and crop yields, and the lack of historical data for calibration. Although IBCI reports cover vegetation and crop health monitoring, few to none cover crop type and crop area mapping. Furthermore, areas including high-resolution mapping, data fusion, microwave RS, machine learning, and computer vision have not been sufficiently tested in IBCI. The second objective of this study was to assess how RS and machine learning techniques can be used to enhance the mapping of smallholder crop farming landscapes. The findings show that machine learning ensembles and the combination of optical and microwave data can map a smallholder farming landscape with a maximum accuracy of 97.71 percent. The third objective was to identify factors that influence crop yields and crop losses in order to improve IBCI design. Results demonstrated that the pervasive notion that low yields in smallholder farms are related to rainfall is an oversimplification. Factors including fertilizer use, seed variety, soil properties, soil moisture, growing degree-days, management, and socioeconomic conditions are some of the most important factors influencing crop yields and crop losses in smallholder farming systems. This shows why IBCI needs to be part of a comprehensive risk management system that understands and approaches smallholder crop farming as complex by linking insurance with advisories and input supplies. Improved inputs and good farming practices could reduce the influence of non-weather factors on crop losses, and thereby reduce basis risk in weather-based index insurance (WII) contracts. The fourth objective of this study was to assess how well the combination of synthetic aperture radar (SAR) and optical indices estimate soil moisture. As stated earlier, soil moisture was found to be one of the most important factors affecting crop yields. Although this method better estimated soil moisture over the first half of the growing season, estimation accuracies were comparable to those found in studies that had used similar datasets (RMSE = 0.043 m3 m-3, MAE = 0.034 m3 m- 3). Further interrogation of interaction effects between the variables used in this study and consideration of other factors that affect SAR backscatter could improve the method. More importantly, incorporating high-resolution satellite-based monitoring of soil moisture into IBCI could potentially reduce basis risk. The fifth objective of this study was to develop an IBCI for smallholder crop farming systems. The proposed IBCI scheme covers maize and derives index thresholds from crop water requirements and satellite-based rainfall estimates. It covers rainfall deficits over the vegetative, mid-season, and late-season stages of maize growth. The key contribution of this system is the derivation of index thresholds from CWR and site-specific rainfall conditions. The widely used approach, which calibrates IBCI by correlating yields and rainfall, exposes contracts to basis risk because, by simply correlating yield and rainfall data, it overlooks the influence of non-weather factors on crop yields and losses. The proposed system must be linked or bundled with non-weather variables that affect crop yields. Effectively, this means that the insurance must be linked or bundled with advisories and input supplies to address the influence of non-weather factors on crop losses. This system also incorporates a crop area-mapping component, which was found to be lacking in many IBCI systems. In conclusion, an IBCI that is based on crop water requirements, which incorporates crop area mapping and links insurance with non-weather crop yield-determining factors, is potentially capable of improving crop insurance for smallholder farming systems.Thesis (PhD) -- Faculty of Science and Agriculture, 202