Winter Wheat Mapping Based on Sentinel-2 Data in Heterogeneous Planting Conditions

Monitoring and mapping the spatial distribution of winter wheat accurately is important for crop management, damage assessment and yield prediction. In this study, northern and central Anhui province were selected as study areas, and Sentinel-2 imagery was employed to map winter wheat distribution and the results were verified with Planet imagery in the 2017–2018 growing season. The Sentinel-2 imagery at the heading stage was identified as the optimum period for winter wheat area extraction after analyzing the images from different growth stages using the Jeffries–Matusita distance method. Therefore, ten spectral bands, seven vegetation indices (VI), water index and building index generated from the image at the heading stage were used to classify winter wheat areas by a random forest (RF) algorithm. The result showed that the accuracy was from 93% to 97%, with a Kappa above 0.82 and a percentage error lower than 5% in northern Anhui, and an accuracy of about 80% with Kappa ranging from 0.70 to 0.78 and a percentage error of about 20% in central Anhui. Northern Anhui has a large planting scale of winter wheat and flat terrain while central Anhui grows relatively small winter wheat areas and a high degree of surface fragmentation, which makes the extraction effect in central Anhui inferior to that in northern Anhui. Further, an optimum subset data was obtained from VIs, water index, building index and spectral bands using an RF algorithm. The result of using the optimum subset data showed a high accuracy of classification with a great advantage in data volume and processing time. This study provides a perspective for winter wheat mapping under various climatic and complicated land surface conditions and is of great significance for crop monitoring and agricultural decision-making

Modelação da produtividade de trigo combinando dados climáticos e de observação da terra: o caso de estudo do Alentejo

O trigo é uma das principais culturas alimentares do mundo, e um dos 3 cereais mais cultivados juntamente com o milho e o arroz, ocupando 17% da terra cultivável do mundo. Para responder aos desafios das alterações climáticas, do aumento da população e da procura alimentar, é mais imperativo do que nunca uma estimativa atempada, precisa e fiável do rendimento das culturas para a gestão das culturas, a avaliação da segurança alimentar, o comércio alimentar e a elaboração de políticas. Historicamente a cultura do trigo em Portugal é realizada sobretudo na região do Alentejo. Durante vários séculos, a atividade agrícola com base na produção de cereais, com particular destaque para o trigo, foi e continua a ser uma das principais culturas do Alentejo, em especial no Baixo Alentejo. Esta dissertação de mestrado tem como objetivo avaliar um modelo preditivo para a produtividade do trigo (Kg/ha) na NUTS-II do Alentejo combinando informação estatística oficial, variáveis climáticas e índice de vegetação (NDVI) derivado de dados orbitais (EO) armazenados na cloud Google Earth Engine (GEE), em ambiente R, utilizando como base estatística de suporte algoritmos de Machine Learning (ML), como Random Forest (RF) e Support Vector Machines (SVM). O modelo será implementado em duas fases. Numa primeira fase serão avaliadas apenas as variáveis climáticas e numa segunda fase serão avaliadas as variáveis climáticas e o índice de vegetação, com o objetivo de avaliar o aumento da capacidade preditiva do modelo combinando estes dois tipos de dados. As variáveis climáticas utilizadas como variáveis explicativas do modelo preditivo foram a precipitação em setembro, a precipitação em dezembro, a temperatura média de dezembro, a temperatura média de março, a humidade relativa em março e a existência de precipitação em maio. As variáveis preditivas com origem nos dados EO são o valor de NDVI (Normalized Difference Vegetation Index) no mês de março e o valor de NDVI em junho para áreas cultivadas com trigo. Os resultados mostram que, quer o modelo de apenas variáveis climáticas, quer o modelo combinado de variáveis climáticas e NDVI, poderiam capturar as variações de produtividade de trigo no Alentejo. O modelo de apenas variáveis climáticas obteve um R2 que varia entre 0,76 (SVM) e 0,78 (RF) e RMS que varia entre 162,93 Kg/ha (RF) e 221,13 Kg/ha (SVM). O modelo combinando as duas fontes de dados (climática e NDVI) melhorou a capacidade preditiva em todas as medidas, obteve um R2 entre 0,81 (RF) e 0,84 (SVM) com RMS entre 144,4 e 148,41 Kg/ha para RF e SVM respetivamente.Wheat is one of the world’s main food crops and one of the three most cultivated cereals along with corn and rice, occupying 17% of the world’s arable land. In order to meet the challenges of climate change, population growth and food demand, is more imperative than ever timely, accurate and reliable crop yield estimation for crop management, food security assessment, food trade and policy making. Historically, the cultivation of wheat in Portugal is mainly carried out in the Alentejo region. For several centuries the agricultural activity based on cereals, with particular emphasis on wheat, was and continues to be one of the main crops in Alentejo, especially in Baixo Alentejo. This master's thesis aims to evaluate a predictive model for wheat productivity (Kg/ha) in NUTS-II of Alentejo, combining official statistical information, climatic variables and vegetation index (NDVI) derived from orbital data (EO) stored in cloud Google Earth Engine (GEE), in R environment, using Machine Learning (ML) algorithms as a statistical support base, such as Random Forest (RF) and Support Vector Machines (SVM). The model will be implemented in two phases. In the first phase, only climatic variables will be evaluated. In the second phase, the climatic variables and the vegetation index will be evaluated to analyse the model's predictive capacity increase by combining these two types of data. The climatic variables used as explanatory variables of the predictive model are precipitation in September, precipitation in December, the average temperature in December, the average temperature in March, relative humidity in March, and May with precipitation. The predictive variables originating from the EO data are the NDVI value in March and the NDVI value in June for areas cultivated with wheat. The results showed that both the model of only climatic variables, and the combined model of climatic variables and NDVI, could capture the variations in wheat productivity in the Alentejo. The model of only climatic variables obtained a R2 that varies between 0.76 (SVM) and 0.78 (RF) and RMS that varies between 162.93 kg/ha (RF) and 221.13 kg/ha (SVM). The model combining the two data sources (climate and NDVI) improved the predictive capacity in all measurements, obtained a R2 between 0.81 (RF) and 0.84 (SVM) with RMS between 144.4 and 148.41 Kg/ha for RF and SVM, respectively

Assessing Normalized Difference Vegetation Index (NDVI) data to estimate winter wheat yields and analyze winter wheat by homogeneous subregions at field scale in Kansas.

Doctor of PhilosophyDepartment of GeographyMarcellus M CaldasWheat (Triticum aestivum L.) is the 4th largest staple crop produced worldwide. While global demand has increased over the last 15 years, the rate of increase of global cereal production has slowed or stagnated. Accurate information about crop production is key for local-scale research, farmers, and decision-making evaluation due to the typically high spatial variability in agricultural production, especially in environmentally heterogeneous high-producing regions. The main goal of this dissertation was to investigate the potential of satellite imagery in predicting winter wheat yields and analyze winter wheat yields by homogeneous subregions at field scale in Kansas, the largest producer of winter wheat in the U.S. The first chapter examined the performance of different satellite sensors (from coarse to moderate resolution - MODIS, Landsat, and Sentinel) in predicting winter wheat yields. The following chapters analyze the winter wheat yield prediction using environmentally distinct subregions regarding weather and management practices and multisource data (NDVI, weather, and climate). Linear Regression and a robust machine learning model, (i.e., Random Forest) were applied to predict winter wheat yields. The results, using NDVI predictor variables, were not enough to explain field-scale winter wheat yield variability across much of Kansas, where Landsat USGS achieved the lowest prediction error among all sensors (RMSE = 0.95 Mg ha-1). The results proved to be more accurate when using Landsat NDVI variables to predict winter wheat yields in more homogeneous subregions (NC, SC, and West), with the best prediction in NC (RMSE = 0.76 Mg ha-1). NC, SC, and West Kansas achieved the best results when including weather and management variables along with NDVI (RMSE of 0.59 Mg ha-1 , 0.66 Mg ha-1, and 0.69 Mg ha-1in NC, SC, and West), and outperformed the prediction when using all fields-yields across Kansas ( RMSE=0.78 Mg ha-1). The prediction model showed that it is possible to predict yield in early crop developmental stages; however, after adding weather and management variables, NDVI predictor variables in the late stages of the growing season were the most important for winter wheat yield prediction. NDVI was more significant in predicting winter wheat yields in NC and West than in SC Kansas. NC showed management of fertilizers ( N, P, Cl) as good yield predictors and could be used along with NDVI to estimate yields. SC and West predictor variables relied more on variables related to environmental conditions or management practices related to environmental conditions, such as fungicide application, soil water storage, and sowing date. Overall, this research demonstrates that the applicability of empirical winter wheat yield modeling using NDVI predictor variables in Kansas is environmentally dependent. Lastly, winter wheat yield prediction using satellite imagery at the field scale could be benefited using this subregional scheme in Kansas