Comparación de técnicas de estimación del grado de salinidad en suelos de escasa vegetación mediante el procesamiento de imágenes multiespectrales de satélite

En 2005 el MINAG, concluyó que 0.24 % del total de suelo agricultor en Perú, es afectado por salinización y este se encuentra ubicado en suelos costeños. Del mismo modo se advirtió que la producción agrícola en el departamento de Lambayeque aportó 0.8% al PBI del país y 16,2% del PBI de la región. Siendo la degradación del suelo a causa de la salinización, el escaso recurso hídrico y una deficiente planificación por parte de los productores, las principales causas de un bajo nivel de crecimiento agrícola en dicha región. Por tal motivo y teniendo en cuenta los datos antes descritos, se propuso el trabajo de investigación “Comparación de técnicas de estimación del grado de salinidad en suelos de escaza vegetación, mediante el procesamiento de imágenes multiespectrales de satélite” y de esta forma usar el procesamiento de imágenes multiespectrales para estimar la salinidad de los terrenos de escasa vegetación, y así aprovecharlos mediante la agricultura dirigida. Para lo cual se consideró utilizar las siguientes técnicas de estimación; SLR, MLR, RFR y DTR, para extraer características como los indicadores de salinidad y vegetación utilizar imágenes multiespectrales, para validar se realizaron pruebas de conductividad eléctrica para medir el grado de salinidad´ La comparación de estas técnicas mostró resultado que dan a DTR como el de mejor con 95% de precisión y un error promedio de 0.11 dS/m, en segundo lugar, RFR con 88%, de precisión y un error promedio de 0.18 dS/m, mientras que SLR y MLR solo obtuvieron un 52% de precisión y un error promedio de 0.33 dS/m.TesisInfraestructura, Tecnología y Medio Ambient

The utility of very-high resolution unmanned aerial vehicles (UAV) imagery in monitoring the spatial and temporal variations in leaf moisture content of smallholder maize farming systems.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Maize moisture stress, resulting from rainfall variability, is a primary challenge in the production of rain-fed maize farming, especially in water-scarce regions such as southern Africa. Quantifying maize moisture variations throughout the growing season can support agricultural decision-making and prompt the rapid and robust detection of smallholder maize moisture stress. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit near real-time information for determining maize moisture content at farm scale. Therefore, this study evaluated the utility of UAV derived multispectral imagery in estimating maize leaf moisture content indicators on smallholder farming systems throughout the maize growing season. The first objective of the study was to conduct a comparative analysis in order to evaluate the performance of five regression techniques (support vector regression, random forest regression, decision trees regression, artificial neural network regression and the partial least squares regression) in predicting maize water content indicators (i.e. equivalent water thickness (EWT), fuel moisture content (FMC) and specific leaf area (SLA)), and determine the most suitable indicator of smallholder maize water content variability based on multispectral UAV data. The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising maize moisture indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC and SLA were derived from the random forest regression algorithm with a relative root mean square error (rRMSE) of 3.13%, 1% and 3.48 %, respectively. Additionally, EWT and FMC yielded the highest predictive performance of maize leaf moisture and demonstrated the best correlation with remotely sensed data. The study’s second objective was to evaluate the utility of UAVderived multispectral imagery in estimating the temporal variability of smallholder maize moisture content across the maize growing season using the optimal maize moisture indicators. The findings illustrated that the NIR and red-edge wavelengths were influential in characterising maize moisture variability with the best models for estimating maize EWT and FMC resulting in a rRMSE of 2.27 % and 1%, respectively. Furthermore, the early reproductive stage was the most optimal for accurately estimating maize EWT and FMC using UAVproximal remote sensing. The findings of this study demonstrate the prospects of UAV- derived multispectral data for deriving insightful information on maize moisture availability and overall health conditions. This study serves as fundamental step towards the creation of an early maize moisture stress detection and warning systems, and contributes towards climate change adaptation and resilience of smallholder maize farming

Monitoring within-field variability of corn yield using sentinel-2 and machine learning techniques

Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4-R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4-R6)

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (ANN), multivariable linear regression (MLR), decision-tree regression (DT), boosted binary regression tree (BBRT), partial least squares regression (PLSR), random forest regression (RF), support vector machine regression (SVM), and principal component regression (PCR), which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs) determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise). Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE) = 0.18); thus, PLSR is best suited for works requiring high-accuracy estimation models. The results indicate that all these techniques provide impressive accuracy. The comparison and analysis provided herein thus reveals the advantages and disadvantages of the ANN, MLR, DT, BBRT, PLSR, RF, SVM, and PCR techniques and can help researchers to build efficient AGB-estimation models

Remote Sensing and Site Specific Crop Management in Precision Agriculture

Application of variable crop inputs in the right quantity and place is very important for optimizing plant growth and final yield through efficient use of finite resources and minimum environmental impacts. In this framework, actions were carried out to support the adoption of PA: In Chapter 1 several remotely sensed vegetation indices (VIs) were used to estimate the spatial crop yields of winter cereals (durum and bread wheat) and spring dicots (sunflower and coriander) through simple correlation over five years. Pixel level study was also investigated between original VIs data and kriged crop yield data. Results showed that spatial variability of crops can be effectively assessed through Landsat imagery with 30 m resolution even on a relatively small area (11.07 ha). Simple ratio and normalized difference vegetation index were shown slightly better indices during vegetative to reproductive stages as compared to enhanced vegetation index, soil adjusted vegetation index, green normalized difference vegetation index and green chlorophyll index. Pixel level study also demonstrated a good agreement between five classes of VIs and grain yield. In Chapter 2, three yield stability classes (YSCs) were developed using spatio-temporal yield maps over five years: high yielding and stable (HYS), low yielding and stable (LYS), and unstable class. Thereafter, we evaluated the YSCs through simple correlations and statistical differences of soil data with spatiotemporal yield within YSCs. Results showed that spatial maps were more consistent with the YSCs map than the temporal stability map. Yield classes were found considerably consistent with soil properties. Lower values of soil apparent electrical conductivity (ECa), in the average, were consistent with HYS class featuring maximum crop yield (122 %), compared to LYS and unstable class. In addition, the balance between precipitation and evapo-transpiration support the fluctuations of yield across years in the unstable area