Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA’s Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky–Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta.E.A. was supported by the predoctoral scholarship, grant number ACIF/2019/187, funded by the Generalitat Valenciana and co-funded by the European Social Fund. J.V. and S.B. were supported by the European Research Council (ERC) under the ERC-2017-STG SENTIFLEX project, grant number 755617. J.V. was additionally supported by a Ramón y Cajal Contract (Spanish Ministry of Science, Innovation and Universities). S.B. was additionally supported by the Generalitat Valenciana SEJIGENT program (SEJIGENT/2021/001) and European Union—NextGenerationEU (ZAMBRANO 21-04)

Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes

The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery which can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge. In this work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a machine learning technique that learns automatically the statistical relationships among multisensor time series, to detect vegetated areas over which the synergy between SAR-optical imageries is profitable. For this purpose, we use the Sentinel-1 Radar Vegetation Index (RVI) and Sentinel-2 Leaf Area Index (LAI) time series over a study area in north west of the Iberian peninsula. Through a physical interpretation of MOGP trained models, we show its ability to provide estimations of LAI even over cloudy periods using the information shared with RVI, which guarantees the solution keeps always tied to real measurements. Results demonstrate the advantage of MOGP especially for long data gaps, where optical-based methods notoriously fail. The leave-one-image-out assessment technique applied to the whole vegetation cover shows MOGP predictions improve standard GP estimations over short-time gaps (R 2 of 74% vs 68%, RMSE of 0.4 vs 0.44 [m 2 m −2 ]) and especially over long-time gaps (R 2 of 33% vs 12%, RMSE of 0.5 vs 1.09 [m 2 m −2 ])

Contributions to cropland monitoring systems using optical satellite data

As the global demand for food is expected to increase due to population growth, efforts should be made to develop effective agricultural strategies and monitoring systems that can support farmers to optimize their field management to avoid limiting cropland production, and thus food availability. Remote sensing sensors onboard Earth observation satellites offer powerful tools for monitoring our planet at high spatial and temporal resolutions. Among the wide variety of optical remote sensing instruments currently available, medium-resolution sensors make it possible to monitor agricultural areas, capturing changes in field characteristics at a regular frequency. In the current context of the increasing availability of optical satellite data, developing approaches to monitor and assess crop growth and development across distinct agricultural regions and crop types is of paramount interest. In this line, the overall goal of this thesis is to develop efficient cropland monitoring tools by exploiting optical satellite images. The contributions of this thesis addressed: (1) the assessment of vegetation canopy status through the entire crop growth cycle, from the vegetative status until the senescence stage, by training a machine learning retrieval model with ground-based measurements and Sentinel-2 data time series; (2) the detection of the inter- and intra-annual crop phenology seasonal patterns in high rotation cropping systems using Sentinel-2 and Landsat 8 satellite time series; and finally, (3) developing within-season crop yield forecasting models from Sentinel-2 time series. The proposed data-driven regression models and the implementation of advanced time series processing techniques allowed the tracking of cropland across different locations and temporal scales, resulting in efficiently validated monitoring tools with the potential for broad applicability in other regions