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

The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan

We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems

Mapping Crop Cycles in China Using MODIS-EVI Time Series

As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA’s (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications

A multi-temporal phenology based classification approach for Crop Monitoring in Kenya

The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps

Regional estimation of daily to annual regional evapotranspiration with MODIS data in the Yellow River Delta wetland

Evapotranspiration (ET) from the wetland of the Yellow River Delta (YRD) is one of the important components in the water cycle, which represents the water consumption by the plants and evaporation from the water and the non-vegetated surfaces. Reliable estimates of the total evapotranspiration from the wetland is useful information both for understanding the hydrological process and for water management to protect this natural environment. Due to the heterogeneity of the vegetation types and canopy density and of soil water content over the wetland (specifically over the natural reserve areas), it is difficult to estimate the regional evapotranspiration extrapolating measurements or calculations usually done locally for a specific land cover type. Remote sensing can provide observations of land surface conditions with high spatial and temporal resolution and coverage. In this study, a model based on the Energy Balance method was used to calculate daily evapotranspiration (ET) using instantaneous observations of land surface reflectance and temperature from MODIS when the data were available on clouds-free days. A time series analysis algorithm was then applied to generate a time series of daily ET over a year period by filling the gaps in the observation series due to clouds. A detailed vegetation classification map was used to help identifying areas of various wetland vegetation types in the YRD wetland. Such information was also used to improve the parameterizations in the energy balance model to improve the accuracy of ET estimates. This study showed that spatial variation of ET was significant over the same vegetation class at a given time and over different vegetation types in different seasons in the YRD wetlan