Abstract
Optical and microwave images have been combined for land cover monitoring in different agriculture scenarios, providing useful information on qualitative and quantitative land cover changes. This study aims to assess the complementarity and interoperability of optical (SPOT-5 Take-5) and synthetic aperture radar (SAR) (Sentinel-1A) data for crop parameter (basal crop coefficient (Kcb) values and the length of the crop’s development stages) retrieval and crop type classification, with a focus on crop water requirements, for an irrigation perimeter in Angola. SPOT-5 Take-5 images are used as a proxy of Sentinel-2 data to evaluate the potential of their enhanced temporal resolution for agricultural applications. In situ data are also used to complement the Earth Observation (EO) data. The Normalized Difference Vegetation Index (NDVI) and dual (VV + VH) polarization backscattering time series are used to compute the Kcb curve for four crop types (maize, soybean, bean and pasture) and to estimate the length of each phenological growth stage. The Kcb values are then used to compute the crop’s evapotranspiration and to subsequently estimate the crop irrigation requirements based on a soil water balance model. A significant R2 correlation between NDVI and backscatter time series was observed for all crops, demonstrating that optical data can be replaced by microwave data in the presence of cloud cover. However, it was not possible to properly identify each stage of the crop cycle due to the lack of EO data for the complete growing season.
Highlights
Crop monitoring by satellite remote sensing requires high spatial and temporal resolution image time series and ground campaigns to monitor the entire crop cycle with frequent ground acquisitions over extensive areas
Normalized Difference Vegetation Index (NDVI) values in the beginning of the curve are explained by the crop growth stage and trend was observed in the VV + VH backscatter time series, indicating that both types of Earth Observation (EO) data by the typical short height of soybean
Only half of a crop growth cycle is retrieved from the EO data, the results are the three time series, in agreement with the fact that pasture is always in the leaf development stage promising because they demonstrate that this correlation is possible for the late season period
Summary
Crop monitoring by satellite remote sensing requires high spatial and temporal resolution image time series and ground campaigns to monitor the entire crop cycle with frequent ground acquisitions over extensive areas. With the ever-increasing number of satellites and the availability of free data, the integration of multisensor images in coherent time series offers new opportunities for land cover and crop type classification [1]. Equator for the Sentinel-1 constellation and five days at the Equator under cloud-free conditions for the Sentinel-2 constellation) and reconfigurable acquisitions (different viewing conditions can be applied for more frequent observation of a certain area) can be used to better identify the different growth cycle stages that are often imperceptible when using more sporadic data. The repeatability of observations on a cyclic basis and the availability of high spatial resolution multispectral data are suitable for cost-effectively mapping crops and irrigated areas with satisfactory accuracy [10]
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