Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales

Estimación del rendimiento de soja empleando información satelital y modelos de simulación de cultivos

Tesis (Doctor en Ciencias Agropecuarias) -- UNC- Facultad de Ciencias Agropecuarias, 2019La estimación de la productividad de la soja es un dato estratégico a nivel regional. Los modelos de cultivos proveen una descripción continua del crecimiento y desarrollo del cultivo. La teledetección permite el monitoreo del crecimiento y desarrollo de la vegetación en un área determinada. El objetivo general de este estudio fue predecir parámetros biofísicos del cultivo de soja, integrando datos de teledetección del sensor MODIS en el modelo de cultivo STICS. En diez lotes con soja de la región central de la provincia de Córdoba se realizaron observaciones de: índice de área foliar (LAI), fracción de la radiación fotosintéticamente activa interceptada (fIPAR), cobertura del cultivo (%C), materia seca de la parte aérea (MS), fase fenológica, contenido de agua del suelo (HS) y rendimiento. El modelo de transferencia radiativa PROSAIL se utilizó para generar una base de datos que se invirtió empleando redes neuronales artificiales (ANN), con el fin de estimar LAI a partir de valores de reflectancias de MODIS. Se forzó al modelo STICS a tomar valores de LAI (STICS-f) y con ambos se simuló MS y rendimiento, como así también HS. Las reflectancias simuladas con PROSAIL mostraron la evolución típica con el incremento del LAI: un aumento de la absorción en azul y rojo e incremento en la reflectancia NIR. La ANN con mejor desempeño en la inversión de PROSAIL consideró como datos de entrada solamente las reflectancias en azul, rojo y NIR para estimar el LAI. En la calibración de los coeficientes del cultivo del modelo STICS, se debió considerar las diferentes fechas y densidades de siembra como así también la longitud del ciclo de las variedades utilizadas. Como era de esperar, al pasar del modelo STICS a STICS-f, los errores de estimación del LAI disminuyen (%RMSE de 29,2% a 12,2%), como así también los errores al estimar MS; sin embargo, resultaron elevados en comparación con la bibliografía analizada. Cuando el análisis considera solamente la materia seca vegetativa, los errores disminuyen considerablemente. El contenido de humedad del suelo hasta 2m de profundidad y el rendimiento, fueron estimados adecuadamente tanto con STICS como con STICS-f, cuyos valores de %RMSE estuvieron en torno a 10%.The estimation of soybean productivity is a strategic information at the regional level. While crop models provide a continuous description of crop growth, remote sensing also allows monitoring the growth and development of vegetation also for a given area. The general objective of this study was to predict biophysical parameters of soybean crop, integrating remote sensing data from the MODIS sensor with the STICS crop model. At central region of Córdoba province, observations were made, in ten plots with soybean: leaf area index (LAI), fraction of intercepted photosynthetically active radiation (fIPAR), crop coverage (% C), dry matter the aerial (MS), phenological stage, soil moisture content (HS) and yield. The radiative transfer model PROSAIL was used to generate a database that was inverted using artificial neural networks (ANN) to estimate LAI from MODIS reflectance values. The STICS model was forced to take LAI values (STICS-f), to estimate MS and yield, as well as HS. The simulated reflectances with PROSAIL showed the typical evolution with the increase of LAI: an increase for the absorption in blue and red bands and an increase in the NIR reflectance. Only data of the reflectance in blue, red and NIR were used as input in the ANN with better performance in the inversion of PROSAIL to estimate LAI. In the calibration of STICS model, crop coefficients for the different sowing dates and densities as well as the cycle length of the varieties used should be considered. As expected, when changing from STICS to STICS-f model, the LAI estimation errors decrease (% RMSE from 29.2% to 12.2%), as well as errors in MS estimating; however, they were high in comparison with the literature analyzed. When considering vegetative dry matter, the errors decrease markedly. The moisture content of the soil up to 2m depth and the yield were properly estimated with both STICS and STICS- f, models, whose %RMSE values were around 10%