UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture

Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite's output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d'Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers

Estimación de Cosecha de Maíz Forrajero (Zea mays L.) Mediante Índices Espectrales Derivados de LANDSAT-8 y SENTINEL-2

La estimación de cosecha basada en índices espectrales conforma un elemento de decisión importante para quienes participan en la actividad agrícola; sin embargo, muchas interrogantes sobre su utilidad aún persisten. Los objetivos de esta investigación fueron: 1) relacionar propiedades radiativas del maíz forrajero (MF) y producción de biomasa mediante imágenes LANDSAT-8 y SENTINEL-2; y 2) seleccionar el índice de vegetación (IV) con mejor desempeño que permita modelar el rendimiento del MF para condiciones similares. El estudio se realizó en el ciclo PV-2019 con mediciones morfológicas en distintas etapas de crecimiento del MF y mediante muestreos aleatorios destructivos a los 72 dds para determinar magnitud de biomasa en laboratorio; los datos de biomasa se relacionaron con valores de reflectancia e IV de LANDAT-8 y SENTINEL-2 para estimar rendimiento mediante regresión lineal múltiple; ocho IV (NDVI, TVI TTVI, RDVI, RVI, RATIO, SAVI, MSAVI2) se evaluaron mediante evaluaciones cruzadas con base en estadísticos clave. Los resultados del análisis de regresión múltiple indicaron que el mejor modelo (R2 = 0.66) se obtuvo con datos de imágenes SENTINEL-2 a partir de las bandas 3 (α3 = 0.54-0.57 µm) y 8 (α8= 0.78-0.90 µm) con estimadores βi muy significativos (P < 0.05); RDVI presentó el mejor desempeño debido a una buena relación espacial entre los valores digitales ráster y la producción de biomasa verde producida con una asociación del 75.41%; en tanto que los indicadores estadísticos fueron R2= 0.75 y CME=17; con ambos recursos (Modelos de Regresión Múltiple e IV) se pronosticó el rendimiento a los 72 dds en un rango de 10.7 – 57.01 Mg ha-1. La conclusión es que SENTINEL-2 superó a LANDSAT-8 como herramienta libre para la evaluación de cultivos y estimación de biomasa debido a una mejor resolución espacial y temporal

Spatiotemporal analysis of gapfilled high spatial resolution time series for crop monitoring.

[ES] La obtención de mapas fiables de clasificación de cultivos es importante para muchas aplicaciones agrícolas, como el monitoreo de los campos y la seguridad alimentaria. Hoy en día existen distintas bases de datos de cobertura terrestre con diferentes escalas espaciales y temporales cubriendo diferentes regiones terrestres (por ejemplo, Corine Land cover (CORINE) en Europa o Cropland Data Layer (CDL) en Estados Unidos (EE.UU.)). Sin embargo, estas bases de datos son mapas históricos y por lo tanto no reflejan los estados fenológicos actuales de los cultivos. Normalmente estos mapas requieren un tiempo específico (anual) para generarse basándose en las diferentes fenologías de cada cultivo. Los objetivos de este trabajo son dos: 1- analizar la distribución espacial de los cultivos a diferentes regiones espaciales para identificar las áreas más representativas. 2- analizar el rango temporal utilizado para acelerar la generación de mapas de clasificación. El análisis se realiza sobre el contiguo de Estados Unidos (CONUS, de sus siglas en inglés) en 2019. Para abordar estos objetivos, se utilizan diferentes fuentes de datos. La capa CDL, una base de datos robusta y completa de mapas de cultivo en el CONUS, que proporciona datos anuales de cobertura terrestre rasterizados y georeferenciados. Así como, datos multiespectrales a 30 metros de resolución espacial, preprocesados para rellenar los posibles huecos debido a la presencia de nubes y aerosoles en los datos. Este conjunto de datos ha sido generado por la fusión de sensores Landsat y Moderate Resolution Imaging Spectroradiometer (MODIS). Para procesar tal elevada cantidad de datos se utilizará Google Earth Engine (GEE), que es una aplicación que procesa la información en la nube y está especializada en el procesamiento geoespacial. GEE se puede utilizar para obtener mapas de cultivos a nivel mundial, pero requiere algoritmos eficientes. En este estudio se analizarán diferentes algoritmos de aprendizaje de máquina (machine learning) para analizar la posible aceleración de la obtención de los mapas de clasificación de cultivo. En GEE hay diferentes tipos de algoritmos de clasificación disponibles, desde simples árboles de decisión (decision trees) hasta algoritmos más complejos como máquinas de vectores soporte (SVM) o redes neuronales (neural networks). Este estudio presentará los primeros resultados para la generación de mapas de clasificación de cultivos utilizando la menor cantidad posible de información, a nivel temporal, con una resolución espacial de 30 metros.[EN] Reliable crop classification maps are important for many agricultural applications, such as field monitoring and food security. Nowadays there are already several crop cover databases at different scales and temporal resolutions for different parts of the world (e. g. Corine Land cover in Europe (CORINE) or Cropland Data Layer (CDL) in the United States (US)). However, these databases are historical crop cover maps and hence do not reflect the actual crops on the ground. Usually, these maps require a specific time (annually) to be generated based on the diversity of the different crop phenologies. The aims of this work are two: 1- analyzing the multi-scale spatial crop distribution to identify the most representative areas. 2- analyzing the temporal range used to generate crop cover maps to build maps promptly. The analysis is done over the contiguous US (CONUS) in 2019. To address these objectives, different types of data are used. The CDL, a robust and complete cropland mapping in the CONUS, which provides annual land cover data raster geo-referenced. And, multispectral high-resolution gap-filled data at 30 meter spatial resolution used to avoid the presence of clouds and aerosols in the data. This dataset has been generated by the fusion of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). To process this large amount of data will be used Google Earth Engine (GEE) which is a cloud-based application specialized in geospatial processing. GEE can be used to map crops globally, but it requires efficient algorithms. In this study, different machine learning algorithms will be analyzed to generate the promptest classification crop maps. Several options are available in GEE from simple decision trees to more complex algorithms like support vector machines or neural networks. This study will present the first results and the potential to generate crop classification maps using as less possible temporal range information at 30 meters spatial resolution.Rajadel Lambistos, C. (2020). Análisis espaciotemporal de series temporales sin huecos de alta resolución espacial. Universitat Politècnica de València. http://hdl.handle.net/10251/155879TFG

Machine learning and high spatial resolution multi-temporal Sentinel-2 imagery for crop type classification

Thesis (MPhil)--Stellenbosch University, 2019.ENGLISH SUMMARY : Spatially-explicit crop type information is useful for estimating agricultural production areas. Such information is used for various monitoring and decision-making applications, including crop insurance, food supply-demand logistics, commodity market forecasting and environmental modelling. Traditional methods, such as ground surveys and agricultural censuses, involve high production costs and are often labour intensive, which limit their use for timely and accurate crop type data production. Remote sensing, however, offers a dependable, cost-effective and timely way of mapping crop types. Although remote sensing approaches – particularly using multitemporal techniques – have been successfully employed for producing crop type information, this information is mostly available post-harvest. Thus, researchers and decision-makers have to wait several months after harvest to have such information, which is usually too late for many applications. The availability and accessibility of imagery collected with optical sensors make such data preferable for mapping crop types. However, these sensors are subject to cloud-interference, which has been recognised as a source of error in the retrieval of surface parameters. It is therefore important to assess the strengths and weaknesses of using multi-temporal optical imagery for differentiating crop types. This study utilises Sentinel-2A and 2B imagery to perform several experiments in selected parts of the Western Cape, South Africa, to undertake this assessment. The first three experiments assessed the significance of image selection on the accuracies of crop type classification. A recommended number of Sentinel-2 images was selected, using two different methods. The first of the three experiments was conducted with uni-temporal images. Based on the performance rankings of the uni-temporal images, five images with the highest ranks were used to set up Experiment 2. The third experiment was undertaken with a handpicked set of five images, based on crop developmental stages. The two image selection methods were compared to each other and subsequently to the entire time-series, to determine the significance of selecting images for crop type mapping. These classifications were undertaken with several supervised machine learning classifiers and one parametric classifier. Results showed no significant difference in classification accuracies between the two image selection methods and the entire time-series. Overall, the support vector machine (SVM) and random forest (RF) algorithms outperformed all the other classifiers. The fourth experiment was undertaken by chronologically adding images to the classifiers. The progression of classification accuracies against time and the increase in the number of images were analysed to determine the earliest period (pre-harvest) when crops can be classified with sufficient accuracies. The highest pre-harvest accuracy achieved was then compared to that obtained at the end of the season, including images acquired post-harvest, to assess the effectiveness of machine learning classifiers for classifying crop types when only pre-harvest images are used. The results of this experiment showed that machine learning classifiers can classify crops when only preharvest images are used, with accuracies similar to those obtained when the entire time-series is used. Satisfactory classification accuracies were attainable as early as Aug/Sept (eight weeks before harvest). The fifth to tenth experiments were undertaken to assess the impact of cloud cover and image compositing on crop type classification accuracies. The fifth and sixth experiments were performed with non-composited images. Experiment Five (5) was undertaken with cloud-free images only, while the sixth experiment involved using all available images, including cloudcontaminated observations. The seventh to tenth experiments were undertaken with monthly image composites computed using four different image compositing approaches. All these experiments were undertaken using several machine learning classifiers. The results showed that machine learning classifiers performed best when all images – including cloud-contaminated images – are used as input to the classifiers. Image compositing had a detrimental effect on classification accuracies. Generally, multi-temporal Sentinel-2 data hold great potential for operational crop type map production early in the season. However, more work is needed to develop simple workflows for eliminating cloud cover, particularly for crop type mapping in areas characterised by frequent overcast conditions.AFRIKAANSE OPSOMMING : Eksperiment 2 op te stel. Die derde eksperiment is gedoen met ’n uitgesoekte stel van vyf beelde, gebaseer op stadiums van gewasontwikkeling. Die twee beeldseleksiemetodes is met mekaar vergelyk en gevolglik met die hele tydreeks, om te bepaal wat die betekenis daarvan is om beelde te kies vir gewastipe-kartering. Hierdie klassifikasies is onderneem met verskeie masjienlerende klassifiseerders en een parametriese klassifiseerder, onder toesig. Resultate het geen beduidende verskil in klassifikasie-akkuraathede gewys tussen die twee beeldseleksiemetodes en die algehele tydreeks nie. In die geheel het die steunvektormasjien- (SVM) en lukrake-woud- (“random forest”, RF) -algoritmes beter presteer as al die ander klassifiseerders. Die vierde eksperiment is onderneem deur beelde chronologies by die klassifiseerders te voeg. Die progressie van klassifikasie-akkuraathede teenoor tyd en die toename in die aantal beelde is geanaliseer om die vroegste periode (voor-oes) te bepaal wanneer gewasse met voldoende akkuraathede geklassifiseer kan word. Die hoogste voor-oes-akkuraatheid is toe vergelyk met dit wat teen die end van die seisoen behaal is, insluitend beelde wat na-oes ingesamel is, om die doeltreffendheid van masjienlerende klassifiseerders te bepaal by die klassifisering van gewastipes wanneer slegs voor-oes-beelde gebruik is. Die resultate van hierdie eksperiment het gewys dat masjienlerende klassifiseerders gewasse kan klassifiseer wanneer slegs voor-oes-beelde gebruik is, met akkuraathede wat soortgelyk is aan dit wat behaal is wanneer die hele tydreeks gebruik is. Bevredigende klassifikasie-akkuraathede is so vroeg as Aug/Sep behaal (agt weke voor oes). Die vyfde tot tiende eksperimente is onderneem om die impak van wolkbedekking en beeldsamestelling op klassifikasie-akkuraathede van gewastipes te bepaal. Die vyfde en sesde eksperimente is met nie-saamgestelde beelde uitgevoer. Eksperiment Vyf (5) is slegs met wolkvrye beelde gedoen, terwyl die sesde eksperiment die gebruik van alle beskikbare beelde, insluitend wolkgekontamineerde observasies, betrek het. Die sewende tot tiende eksperimente is onderneem met maandelikse beeldsamestellings wat bereken is deur middel van die gebruik van vier verskillende benaderings tot beeldsamestelling. Al hierdie eksperimente is met behulp van verskeie masjienlerende klassifiseerders uitgevoer. Die resultate het gewys dat masjienlerende klassifiseerders die beste presteer het wanneer alle beelde – insluitend wolkgekontamineerde beelde – as invoer aan die klassifiseerders gebruik word. Beeldsamestelling het ’n nadelige uitwerking op klassifikasie-akkuraathede gehad. Oor die algemeen het multitemporale Sentinel-2-data vroeg in die seisoen goeie potensiaal vir operasionele gewastipe-kaartproduksie. Meer werk is nietemin nodig om eenvoudige werkvloei te ontwikkel om wolkbedekking te elimineer, veral vir gewastipe-kartering in areas wat gereeld gekenmerk word deur oortrokke toestande.Master

Estimation of Spatial Change in Cropland Area and Evaluation of Irrigation Performance in Imperial Valley Using Remotely Sensed Data

The Imperial Valley (IV) in the US is an extensively irrigated agricultural region, which includes multiple crops changing on an annual and semiannual basis. The valley is facing grave concerns about water management due to its semi-arid environment, water intensive crops, and limited water supply. A simple, inexpensive, and repeatable method to detect changes in cropping patterns may assist irrigation managers to understand crop diversification and associated consumptive use. In addition, a spatial assessment of existing water irrigation system performance and productivity is crucial to benchmark and improve current water management strategies. This thesis estimates the spatial pattern of change in crop distributions from 2018 to 2019 across the IV, using remotely sensed data with high resolution and a machine learning algorithm. Furthermore, it also quantifies the irrigation performance indicators based on the equity, adequacy, and water productivity of water intensive crops utilizing remote sensing, Vegetation indices, and county level crop production statistics. First, we addressed the spatial analysis of cropland change in an agricultural field of the IV over 2018 and 2019. Optical images from the Sentinel-2 platform were used to develop an annual cropland map using a random forest algorithm in R version 4.0.2. The reflectance from the Sentinel images and Normalized Difference Vegetation Index (NDVI) served as a predictor variable. A cropland data layer was utilized to identify the field’s crop type for ground truthing. We used the dataset provided by the United States Department of Agriculture to access the accuracy of classification. The changes in cropping patterns were quantified by preparing a transition matrix through image the differencing technique in Geographical Information System (GIS). The spatial analysis of change was characterized by generating a map showing the change in cropping proportion for major crop types over the two-year period. We obtained the overall classification accuracy of 85% for each year. Classification results showed that dominant crops, including alfalfa, mixed grasses, and sugar beet, could be categorized more accurately than scant crops, such as wheat and corn. In terms of total acreage, alfalfa, mixed crops, and mixed grasses increased in 2019, whereas there was reduction in corn, wheat, and sugar beet acreages. A change analysis showed that the spatial variation of alfalfa fields was prominent, whereas mixed grasses were the most stable. The changes mainly occurred in the northeast and southeast of the valley. We found that the wheat intensity reduced significantly in 2019 and was concentrated in the region where expansion of alfalfa, mixed crops, and mixed grasses occurred. The predictor variables of the red edge band and SWIR band were found to be most important in identification of the crops studied. The contribution of NDVI was least among all, and the reason was attributed to the saturation of NDVI at the late season stage, producing an indistinctive signature between crops. Secondly, we estimated spatially distributed irrigation equity, adequacy, and crop water productivity (CWP) of two water intensive crops, i.e., alfalfa and sugar beet, in the IV, using remotely sensed data and GIS. The analysis was performed for the 2018/2019 crop growing season. The actual evapotranspiration (ETa) of a crop was mapped utilizing the automated Mapping Evapotranspiration at High Resolution using Internalized Calibration (METRIC) algorithm in Google Earth Engine Evapotranspiration Flux (EEFlux) platform. We utilized the linear interpolation method in R version 4.0.2 to produce daily ETa maps, which were then totaled to compute ETa for the whole season. The within and among field coefficients of variation of water use i.e …. CVw and CVa respectively were computed utilizing the United States Bureau of Reclamation field boundary layer as a measure of irrigation equity. Similarly, Relative Evapotranspiration (RET) was computed to address the adequacy as a ratio of ETa to potential evapotranspiration (ETp). We computed the crop water productivity (CWP) as a ratio of crop yield to crop water use. The yield disaggregation method was employed to map the crop yield, which uses county-level production statistics data and NDVI images as a bridge. The results were validated with various data reported in the literature, as well as compared with ET from crop coefficient-reference ET (kc-ETo) approach. The relative error of ETas, when compared to literature reported values, were in the range of (7-27) % for alfalfa and (0-3) % for sugar beet. The predicted ETa values and ET computed using kc-ETo approach for different growth stages were different. The average CVws were found to be low; however, spatial variation within fields showed that 36.14% of sugar beet and 34.17% of alfalfa fields had variability greater than 10%. CVas were estimated to be about 19% for both. The relative ET was high, indicating adequate irrigation. About 31.5% of alfalfa fields and 12% of sugar beet fields were consuming water more than its potential visibly, clustered in the central corner of the valley. CWP showed a wide variation with CV of 32.92% for alfalfa and 25.4% for sugar beet, signifying a substantial scope of CWP enhancement

Regional mapping of crops under agricultural nets using Sentinel-2

Geography and Environmental Studie