The potential for using remote sensing to quantify stress in and predict yield of sugarcane (Saccharum spp. hybrid)

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010

Hyperspectral Imaging to Evaluate the Effect of IrrigationWater Salinity in Lettuce

Salinity is one of the most important stress factors in crop production, particularly in arid regions. This research focuses on the effect of salinity on the growth of lettuce plants; three solutions with different levels of salinity were considered and compared (S1 = 50, S2 = 100 and S3 = 150 mM NaCl) with a control solution (Ct = 0 mM NaCl). The osmotic potential and water content of the leaves were measured, and hyperspectral images of the surfaces of 40 leaves (10 leaves per treatment) were taken after two weeks of growth. The mean spectra of the leaves (n = 32,000) were pre-processed by means of a Savitzky–Golay algorithm and standard normal variate normalization. Principal component analysis was then performed on a calibration set of 28 mean spectra, yielding an initial model for salinity effect detection. A second model was subsequently proposed based on an index computing an approximation to the second derivative at the red edge region. Both models were applied to all the hyperspectral images to obtain the corresponding artificial images, distinguishing between the 28 that were used to extract the calibration mean spectra and the rest that constituted an external validation. Those virtual images were studied using analysis of variance in order to compare their ability for detecting salinity effects on the leaves. Both models showed significant differences between each salinity level, and the hyperspectral images allowed observations of the distribution of the salinity effects on the leaf surfaces, which were more intense in the areas distant from the veins. However, the index-based model is simpler and easier to apply because it is based solely on the reflectance at three different wavelengths, thus allowing for the implementation of less expensive multispectral devices.The funding of this work was supported by the MICINN through projects Multihort (AGL2008-05666-C02-01) and QualityLeaf (AGL2013-48529-R). LPF-TAGRALIA is part of the CEI Moncloa Campus. We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)

The Potential of Hyperspectral Patterns of Winter Wheat to Detect Changes in Soil Microbial Community Composition

Reliable information on soil status and crop health is crucial for detecting and mitigating disasters like pollution or minimizing impact from soil-borne diseases. While infestation with an aggressive soil pathogen can be detected via reflected light spectra, it is unknown to what extent hyperspectral reflectance could be used to detect overall changes in soil biodiversity. We tested the hypotheses that spectra can be used to (1) separate plants growing with microbial communities from different farms; (2) to separate plants growing in different microbial communities due to different land use; and (3) separate plants according to microbial species loss. We measured hyperspectral reflectance patterns of winter wheat plants growing in sterilized soils inoculated with microbial suspensions under controlled conditions. Microbial communities varied due to geographical distance, land use and microbial species loss caused by serial dilution. After 3 months of growth in the presence of microbes from the two different farms plant hyperspectral reflectance patterns differed significantly from each other, while within farms the effects of land use via microbes on plant reflectance spectra were weak. Species loss via dilution on the other hand affected a number of spectral indices for some of the soils. Spectral reflectance can be indicative of differences in microbial communities, with the Renormalized Difference Vegetation Index the most common responding index. Also, a positive correlation was found between the Normalized Difference Vegetation Index and the bacterial species richness, which suggests that plants perform better with higher microbial diversity. There is considerable variation between the soil origins and currently it is not possible yet to make sufficient reliable predictions about the soil microbial community based on the spectral reflectance. We conclude that measuring plant hyperspectral reflectance has potential for detecting changes in microbial communities yet due to its sensitivity high replication is necessary and a strict sampling design to exclude other ‘noise’ factors.</p

Aplicación de imagen hiperespectral para observar el efecto de la salinidad en hojas de lechuga.

La salinización en suelos de cultivos es un fenómeno muy relevante en el Sureste español que se produce por el empleo de aguas de riego salinas o de mala calidad. La lechuga (Lactuca sativa L.) es uno de los cultivos hortícolas de mayor implantación en esta área, destinándose principalmente para consumo en fresco y en productos cuarta gama. La salinidad puede afectar a la productividad y calidad del cultivo, sin embargo, en determinadas concentraciones investigaciones previas muestran que la salinidad puede favorecer la conservación de las hojas tras el corte, disminuyendo los procesos de degradación enzimática y el desarrollo de microorganismos. El objetivo del presente trabajo es evaluar la viabilidad de la imagen hiperespectral (400 a 1000 nm) para identificar la influencia del estrés salino en lechuga ?baby? recién recolectada. Para ello, se han adquirido imágenes de 40 hojas de diferentes lechugas sometidas a tres soluciones salinas diferentes y a una solución control. Dichas imágenes fueron sometidas a preprocesado de espectros (suavizado con el algoritmo Savitsky-Golay + normalización SNV), combinado con Análisis de Componentes Principales. Las imágenes virtuales de scores generadas con el modelo muestran diferencias progresivas en los valores asignados a los píxeles de las imágenes a medida que aumenta la concentración salina de la solución aplicada al cultivo. Se observa cómo la solución salina afecta a la hoja cambiando la coloración de las zonas medias, posiblemente debido a la concentración de solutos. La interpretación de los loadings de estos modelos permite conocer cómo afecta la salinidad al comportamiento espectral de las hojas. La imagen hiperespectral puede tener un gran potencial para identificar los límites de salinidad tolerados y evitar concentraciones tóxicas que afecten negativamente la vida útil de las hortalizas de hoja. Este conocimiento permitirá desarrollar índices multiespectrales capaces de identificar hojas afectadas por salinidad durante su cultivo y procesado postcosecha

Hyperspectral imaging to evaluate the effect of irrigation water salinity in lettuce

International audienceSalinity is one of the most important stress factors in crop production, particularly in arid regions. This research focuses on the effect of salinity on the growth of lettuce plants; three solutions with different levels of salinity were considered and compared (S1 = 50, S2 = 100 and S3 = 150 mM NaCl) with a control solution (Ct = 0 mM NaCl). The osmotic potential and water content of the leaves were measured, and hyperspectral images of the surfaces of 40 leaves (10 leaves per treatment) were taken after two weeks of growth. The mean spectra of the leaves (n = 32,000) were pre-processed by means of a Savitzky-Golay algorithm and standard normal variate normalization. Principal component analysis was then performed on a calibration set of 28 mean spectra, yielding an initial model for salinity effect detection. A second model was subsequently proposed based on an index computing an approximation to the second derivative at the red edge region. Both models were applied to all the hyperspectral images to obtain the corresponding artificial images, distinguishing between the 28 that were used to extract the calibration mean spectra and the rest that constituted an external validation. Those virtual images were studied using analysis of variance in order to compare their ability for detecting salinity effects on the leaves. Both models showed significant differences between each salinity level, and the hyperspectral images allowed observations of the distribution of the salinity effects on the leaf surfaces, which were more intense in the areas distant from the veins. However, the index-based model is simpler and easier to apply because it is based solely on the reflectance at three different wavelengths, thus allowing for the implementation of less expensive multispectral devices

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

Past and future of plant stress detection: an overview from remote sensing to Positron Emission Tomography

Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters) and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture

Characterizing leaf nutrients of wetland plants and agricultural crops with nonparametric approach using Sentinel-2 imagery data

In arid environments of the world, particularly in sub-Saharan Africa and Asia, floodplain wetlands are a valuable agricultural resource. However, the water reticulation role by wetlands and crop production can negatively impact wetland plants. Knowledge on the foliar biochemical elements of wetland plants enhances understanding of the impacts of agricultural practices in wetlands. This study thus used Sentinel-2 multispectral data to predict seasonal variations in the concentrations of nine foliar biochemical elements in plant leaves of key floodplain wetland vegetation types and crops in the uMfolozi floodplain system (UFS). Nutrient concentrations in different floodplain plant species were estimated using Sentinel-2 multispectral data derived vegetation indices in concert with the random forest regression. The results showed a mean R2 of 0.87 and 0.86 for the dry winter and wet summer seasons, respectively. However, copper, sulphur, and magnesium were poorly correlated (R2 ≤ 0.5) with vegetation indices during the summer season. The average % relative root mean square errors (RMSE’s) for seasonal nutrient estimation accuracies for crops and wetland vegetation were 15.2 % and 26.8%, respectively. There was a significant difference in nutrient concentrations between the two plant types, (R2 = 0.94 (crops), R2 = 0.84 (vegetation). The red-edge position 1 (REP1) and the normalised difference vegetation index (NDVI) were the best nutrient predictors. These results demonstrate the usefulness of Sentinel-2 imagery and random forests regression in predicting seasonal, nutrient concentrations as well as the accumulation of chemicals in wetland vegetation and crops.Department of Higher Education, Science and Technology and Agricultural Research Council.http://www.mdpi.com/journal/remotesensingpm2022Geography, Geoinformatics and Meteorolog

Mapping Soil Salinity and Its Impact on Agricultural Production in Al Hassa Oasis in Saudi Arabia

Soil salinity is considered as one of the major environmental issues globally that restricts agricultural growth and productivity, especially in arid and semi-arid regions. One such region is Al Hassa Oasis in the eastern province of Saudi Arabia, which is one of the most productive date palm (Phoenix dactylifera L.) farming regions in Saudi Arabia and is seriously threatened by soil salinity. Development of remote sensing techniques and modelling approaches that can assess and map soil salinity and the associated agricultural impacts accurately and its likely future distribution should be useful in formulating more effective, long-term management plans. The main objective of this study was to detect, assess and map soil salinity and and its impact on agricultural production in the Al Hassa Oasis. The presented research first started by reviewing the related literature that have utilized the use of remote sensing data and techniques to map and monitor soil salinity. This review started by discussing soil salinity indicators that are commonly used to detect soil salinity. Soil salinity can be detected either directly from the spectral reflectance patterns of salt features visible at the soil surface, or indirectly using the vegetation reflectance since it impacts vegetation. Also, it investigated the most commonly used remote sensors and techniques for monitoring and mapping soil salinity in previous studies. Both spectral vegetation and salinity indices that have been developed and proposed for soil salinity detection and mapping have been reviewed. Finally, issues limiting the use of remote sensing for soil salinity mapping, particularly in arid and semi-arid regions have been highlighted. In the second study, broadband vegetation and soil salinity indices derived from IKONOS images along with ground data in the form of soil samples from three sites across the Al Hassa Oasis were used to assess soil salinity in the Al-Hassa Oasis. The effectiveness of these indices to assess soil salinity over a dominant date palm region was examined statistically. The results showed that very strongly saline soils with different salinity level ranges are spread across the three sites in the study area. Among the investigated indices, the Soil Adjusted Vegetation Index (SAVI), Normalized Differential Salinity Index (NDSI) and Salinity Index (SI-T) yielded the best results for assessing the soil salinity in densely vegetated area, while NDSI and SI-T revealed the highest significant correlation with salinity for less densely vegetated lands and bare soils. In the third study, combined spectral-based statistical regression models were developed using IKONOS images to model and map the spatial variation of the soil salinity in the Al Hassa Oasis. Statistical correlation between Electrical Conductivity (EC), spectral indices and IKONOS original bands showed that the Salinity Index (SI) and red band (band 3) had the highest correlation with EC. Integrating SI and band 3 into one model produced the best fit with R2 = 0.65. The high performance of this combined model is attributed to: (i) the spatial resolution of the images; (ii) the great potential of SI in enhancing and delineating the spatial variation of soil salinity; and (iii) the superiority of band 3 in retrieving soil salinity features and patterns. Soil salinity maps generated using the selected model showed that strongly saline soils (&gt;16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors.16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors. In the fourth study, Landsat time series data of years 1985, 2000 and 2013 were used to detect the temporal change in soil salinity and vegetation cover in the Al Hassa Oasis and investigate whether there is any linkage of vegetation cover change to the change in soil salinity over a 28-year period. Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) differencing images were used to identify vegetation and salinity change/no-change for the two periods. The results revealed that soil salinity during 2000-2013 exhibited much higher increase compared to 1985-2000, while the vegetation cover declined for the same period. Highly significant (p In the fifth study, the effects of physical and proximity factors, including elevation, slope, soil salinity, distance to water, distance to built-up areas, distance to roads, distance to drainage and distance to irrigation factors on agricultural expansion in the Al Hassa Oasis were investigated. A logistic regression model was used for two time periods of agricultural change in 1985 and 2015. The probable agricultural expansion maps based on agricultural changes in 1985 was used to test the performance of the model to predict the probable agricultural expansion after 2015. This was achieved by comparing the probable maps of 1985 and the actual agricultural land of 2015 model. The Relative Operating Characteristic (ROC) method was also used and together these two methods were used to validate the developed model. The results showed that the prediction model of 2015 provides a reliable and consistent prediction based on the performance of 1985. The logistic regression results revealed that among the investigated factors, distance to water, distance to built-up areas and soil salinity were the major factors having a significant influence on agricultural expansion. In the last study, the potential distribution of date palm was assessed under current and future climate scenarios of 2050 and 2100. Here, CLIMEX (an ecological niche model) and two different Global Climate Models (GCMs), CSIRO-Mk3.0 (CS) and MIROC-H (MR), were employed with the A2 emission scenario to model the potential date palm distribution under current and future climates in Saudi Arabia. A sensitivity analysis was conducted to identify the CLIMEX model parameters that had the most influence on date palm distribution. The model was also run with the incorporation of six non-climatic parameters, which are soil taxonomy, soil texture, soil salinity, land use, landform and slopes, to further refine the distributions. The results from both GCMs showed a significant reduction in climatic suitability for date palm cultivation in Saudi Arabia by 2100 due to increment of heat stress. The lower optimal soil moisture, cold stress temperature threshold and wet stress threshold parameters had the greatest impact on sensitivity, while other parameters were moderately sensitive or insensitive to change. A more restricted distribution was projected with the inclusion of non-climatic parameters. Overall, the research demonstrated the potential of remote sensing and modeling techniques for assessing and mapping soil salinity and providing the essential information of its impacts on date palm plantation. The findings provide useful information for land managers, environmental decision makers and governments, which may help them in implementing more suitable adaptation measures, such as the use of new technologies, management practices and new varieties, to overcome the issue of soil salinity and its impact on this important economic crop so that long-term sustainable production of date palm in this region can be achieved. Additionally, the information derived from this research could be considered as a useful starting point for public policy to promote the resilience of agricultural systems, especially for smallholder farmers who might face more challenges, if not total loss, not only due to soil salinity but also due to climate change

Applications of satellite ‘hyper-sensing’ in Chinese agriculture:Challenges and opportunities

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China