Suitability of satellite remote sensing data for yield estimation in northeast Germany

Information provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm management and optimize precision agriculture applications. A vast amount of data is made available both as map material and from space. However, it is up to the user to select the appropriate data for a particular problem. Without the appropriate knowledge, this may even entail an economic risk. This study therefore investigates the direct relationship between satellite data from six different optical sensors as well as different soil and relief parameters and yield data from cereal and canola recorded by the thresher in the field. A time series of 13 years is considered, with 947 yield data sets consisting of dense point data sets and 755 satellite images. To answer the question of how well the relationship between remote sensing data and yield is, the correlation coefficient r per field is calculated and interpreted in terms of crop type, phenology, and sensor characteristics. The correlation value r is particularly high when a field and its crop are spatially heterogeneous and when the correct phenological time of the crop is reached at the time of satellite imaging. Satellite images with higher resolution, such as RapidEye and Sentinel-2 performed better in comparison with lower resolution sensors of the Landsat series. The additional Red Edge spectral band also has advantage, especially for cereal yield estimation. The study concludes that there are high correlation values between yield data and satellite data, but several conditions must be met which are presented and discussed here

Étude comparative d’indices de végétation radar à plusieurs fréquences et de l’indice de végétation optique (NDVI) pour le suivi de la croissance des cultures

De nos jours, la télédétection contribue énormément dans le domaine de l’agriculture. La possibilité d’acquisition des mesures en tout temps et la non sensibilité aux perturbations atmosphériques sont des avantages reconnus à la télédétection radar. Cette étude a pour objectif d’effectuer une analyse comparative des indices radar, à savoir l’indice de végétation radar (RVI) et l’indice de végétation radar à double polarisation (IVRDvv) dans trois fréquences (L, C et X) et de l’indice de végétation par différence normalisée (NDVI) utilisé en télédétection multispectrale optique dans un contexte de suivi de la croissance des cultures de blé, de canola, de maïs et de soja. Pour y parvenir, ces indices de végétation radar (RVI et IVRDvv) calculés à plusieurs fréquences et l’indice optique (NDVI) sont utilisés pour effectuer un suivi temporel de la croissance de ces quatre cultures. D’une part, l’efficacité des indices de végétation radar à traduire la quantité de la biomasse végétale disponible est analysée en déterminant l’indice et la fréquence les mieux adaptés au suivi de la croissance de chaque type de culture. D’autre part, la corrélation des indices de végétation radar (RVI et IVRDvv) et le NDVI par rapport à la quantité de la biomasse végétale est utilisée pour apprécier l’usage de ces indices de végétation radar comme alternative à l’utilisation du NDVI dans un contexte de suivi de la croissance des cultures de blé, de canola, de maïs et de soja. Les indices radar RVI (indice de végétation radar) et IVRDvv (indice de végétation radar à double polarisation) ont été calculés sur la base d’images acquises sur les sites des campagnes de terrain SMAP Validation Experiment 2012 (SMAPVEX12) et SMAP Validation Experiment 2016 in Manitoba (SMAPVEX16-MB) situés au Sud du Manitoba. Les données de biomasse végétale ainsi que l’indice de surface foliaire (LAI) ont été recueillis directement sur le terrain durant ces deux campagnes. Les données radar en bande L proviennent de la campagne SMAPVEX12, elles sont acquises par un Uninhabited Aerial Vehicule Synthetic Aperture Radar UAVSAR; celles utilisées en bande C et X ont été acquises durant la campagne SMAPVEX16-MB par les satellites Radarsat-2 et TerraSAR-X, respectivement. Les données optiques proviennent des images de Sentinelle-2. Le suivi de la croissance des cultures de blé, de canola, de maïs et de soja sur une base temporelle a permis de remarquer l’inefficacité de la bande L à évaluer la croissance des plantes. Le coefficient de rétrodiffusion dans cette bande est contrôlé par les paramètres de surface et particulièrement l’humidité du sol plutôt que la biomasse végétale. Les indices de végétation radar en bandes C et X ont présenté de bons résultats qui traduisent l’évolution de la quantité de la biomasse végétale disponible; la bande X étant toutefois beaucoup mieux corrélée à la biomasse végétale. Pour le blé, la quantité de biomasse végétale est mieux corrélée à l’IVRDvv en bande X (R = 0,9) que le NDVI (R = 0,7). De même, pour la culture de canola, la quantité de la biomasse disponible est légèrement mieux corrélée à l’IVRDvv en bande X (R =0,96) qu’au NDVI (R=0,9). D’autre part, le RVI et l’IVRDvv en bande C pour les cultures de maïs et de soja a montré des fortes corrélations avec le NDVI (R = 0,9). Ces résultats montrent que dans un contexte de suivi de la croissance des végétaux, les indices de végétation radar en bande C et X sont une alternative à l’indice de végétation par différence normalisée utilisé en télédétection optique

Solução de geoinformação para mapeamento da distribuição espacial da produtividade de culturas a partir de RNA e imagens multiespectrais

Trabalho de Conclusão de Curso (Graduação)A estimativa da produtividade de culturas é extremamente desafiadora devido à sua relação com vários fatores complexos como as condições ambientais, estrutura física da cultura, composição química do solo e práticas de manejo de campo. Dessa forma, se torna necessária a exploração de técnicas de pré-processamento dos dados coletados, modelagem e avaliação das ferramentas de previsão dessa variável. Recentemente estão sendo desenvolvidos trabalhos que utilizam de técnicas de aprendizado de máquina e dados advindo do sensoriamento remoto para aplicações na agricultura, e mais recentemente para estimativa de variáveis agrícolas. Nesse sentido, o objetivo deste trabalho é desenvolver uma ferramenta em software SIG de código aberto (QGIS) para mapear a distribuição espacial da produtividade das culturas de milho, trigo e girassol utilizando o algoritmo de Redes Neurais Artificiais (RNA) e imagens multiespectrais. Inicialmente foram adquiridos os dados de produtividade das três culturas em estudo, e imagens multiespectrais gratuitas advindas do satélite Sentinel 2. Em seguida, para compor os dados de entrada para o algoritmo de RNA foi realizado o pré-processamento dos dados que compreendeu a extração da reflectância das bandas e os cálculos dos índices de vegetação (NDVI, GNDVI e VARI), além da organização dos arquivos de entrada para treinamento, validação e teste dos modelos de RNA. Por fim, foi desenvolvido o código de RNA, em que os modelos que apresentaram menor RMSE foram implementados na ferramenta desenvolvida em ambiente SIG. Os resultados foram satisfatórios para as culturas de trigo e girassol, atingindo o objetivo de terem RMSE de validação interna menores que 40% e validação externa menores que 30%, porém, para a cultura de milho, os modelos testados não foram capazes de realizar generalizações para os dados de teste. Conclui-se que os modelos de RNA implementados na ferramenta atenderam ao objetivo desejado, porém necessitam de constante atualização visando aumentar o poder de generalização, além da necessidade de realizar estudos mais aprofundados em relação a cultura de milho a fim de obter um modelo apto para estimativa da produtividade

Crop growth and yield monitoring in smallholder agricultural systems:a multi-sensor data fusion approach

Smallholder agricultural systems are highly vulnerable to production risks posed by the intensification of extreme weather events such as drought and flooding, soil degradation, pests, lack of access to agricultural inputs, and political instability. Monitoring the spatial and temporal variability of crop growth and yield is crucial for farm management, national-level food security assessments, and famine early warning. However, agricultural monitoring is difficult in fragmented agricultural landscapes because of scarcity and uncertainty of data to capture small crop fields. Traditional pre- and post-harvest crop monitoring and yield estimation based on fieldwork is costly, slow, and can be unrepresentative of heterogeneous agricultural landscapes as found in smallholder systems in sub-Saharan Africa. Devising accurate and timely crop phenology detection and yield estimation methods can improve our understanding of the status of crop production and food security in these regions.Satellite-based Earth observation (EO) data plays a key role in monitoring the spatial and temporal variability of crop growth and yield over large areas. The small field sizes and variability in management practices in fragmented landscapes requires high spatial and high temporal resolution EO data. This thesis develops and demonstrates methods to investigate the spatiotemporal variability of crop phenology detection and yield estimation using Landsat and MODIS data fusion in smallholder agricultural systems in the Lake Tana sub-basin of Ethiopia. The overall aim is to further broaden the application of multi-sensor EO data for crop growth monitoring in smallholder agricultural systems.The thesis addressed two important aspects of crop monitoring applications of EO data: phenology detection and yield estimation. First, the ESTARFM data fusion workflow was modified based on local knowledge of crop calendars and land cover to improve crop phenology monitoring in fragmented agricultural landscapes. The approach minimized data fusion uncertainties in predicting temporal reflectance change of crops during the growing season and the reflectance value of fused data was comparable to the original Landsat image reserved for validation. The main sources of uncertainty in data fusion are the small field size and abrupt crop growth changes between the base andviiprediction dates due to flooding, weeding, fertiliser application, and harvesting. The improved data fusion approach allowed us to determine crop phenology and estimate LAI more accurately than both the standard ESTARFM data fusion method and when using MODIS data without fusion. We also calibrated and validated a dynamic threshold phenology detection method using maize and rice crop sowing and harvest date information. Crop-specific phenology determined from data fusion minimized the mismatch between EO-derived phenometrics and the actual crop calendar. The study concluded that accurate phenology detection and LAI estimation from Landsat–MODIS data fusion demonstrates the feasibility of crop growth monitoring using multi-sensor data fusion in fragmented and persistently cloudy agricultural landscapes.Subsequently, the validated data fusion and phenology detection methods were implemented to understand crop phenology trends from 2000 to 2020. These trends are often less understood in smallholder agricultural systems due to the lack of high spatial resolution data to distinguish crops from the surrounding natural vegetation. Trends based on Landsat–MODIS fusion were compared with those detected using MODIS alone to assess the contribution of data fusion to discern crop phenometric change. Landsat and MODIS fusion discerned crop and environment-specific trends in the magnitude and direction of crop phenology change. The results underlined the importance of high spatial and temporal resolution EO data to capture environment-specific crop phenology change, which has implications in designing adaptation and crop management practices in these regions.The second important aspect of the crop monitoring problem addressed in this thesis is improving crop yield estimation in smallholder agricultural systems. The large input requirements of crop models and lack of spatial information about the heterogeneous crop-growing environment and agronomic management practices are major challenges to the accurate estimation of crop yield. We assimilated leaf area index (LAI) and phenology information from Landsat–MODIS fusion in a crop model (simple algorithm for yield estimation: SAFY) to obtain reasonably reliable crop yield estimates. The SAFY model is sensitive to the spatial and temporal resolution of the calibration input LAI, phenology information, and the effective light use efficiency (ELUE) parameter, which needs accurate field level inputs during modelviiioptimization. Assimilating fused EO-based phenology information minimized model uncertainty and captured the large management and environmental variation in smallholder agricultural systems.In the final research chapter of the thesis, we analysed the contribution of assimilating LAI at different phenological stages. The frequency and timing of LAI observations influences the retrieval accuracy of the assimilating LAI in crop growth simulation models. The use of (optical) EO data to estimate LAI is constrained by limited repeat frequency and cloud cover, which can reduce yield estimation accuracy. We evaluated the relative contribution of EO observations at different crop growth stages for accurate calibration of crop model parameters. We found that LAI between jointing and grain filling has the highest contribution to SAFY yield estimation and that the distribution of LAI during the key development stages was more useful than the frequency of LAI to improve yield estimation. This information on the optimal timing of EO data assimilation is important to develop better in-season crop yield forecasting in smallholder systems

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