Remote Sensing Monitoring of Rice Fields: Towards Assessing Water Saving Irrigation Management Practices

Abstract

Rice cultivation is one of the largest users of the world’s freshwater resources. The contribution of remote sensing observations for identifying the conditions under which rice is cultivated, particularly throughout the growing season, can be instrumental for water, and crop management. Data from different remote sensing platforms are being used in agriculture, namely to detecting anomalies in crops. This is attempted by calculating vegetation indices (VI) that are based on different vegetation reflectance bands, especially those that rely on the Red, Green, and near-infrared bands, such as the Normalised Difference Vegetation Index (NDVI) or the Green Normalised Difference Vegetation Index (GNDVI). However, particular features of different crops and growing conditions justify that some indices are more adequate than others on a case-to-case basis, according to the different vegetation’s spectral signatures. In recent years, a vegetation index related to the Red Edge reflectance band, the Normalised Difference Red Edge (NDRE) has shown potential to be used as a tool to support agricultural management practices; this edge band, by taking a transition position, is very sensitive to changes in vegetation properties. This work, focusing on the rice crop and the application of different irrigation practices, explores the capability of several VIs calculated from different reflectance bands to detect variability, at the plot scale, in rice cultivation in the Lower Mondego region (Portugal). The remote sensing data were obtained from satellite Sentinel-2A imagery and using a multispectral camera mounted on an Unmanned Aerial System (UAS). By comparing several vegetation indices, we found that NDRE is particularly useful for identifying non-homogeneities in irrigation and crop growth in rice fields. Since few satellite sensors are sensible in the Red Edge band and none has the spatial resolution offered by UAS, this study explores the potential of UAS to be used as a useful support information tool in rice farming and precision agriculture, regarding irrigation, and agronomic management.

Applying RGB-Based Vegetation Indices Obtained from UAS Imagery for Monitoring the Rice Crop at the Field Scale: A Case Study in Portugal

“…In particular, the magnitude of the BNDVI and NDVI was similar, with BNDVI attaining the largest values. While differences between the selected normalized VIs are expected, especially for rice [37], their magnitudes are not directly comparable with the mean values displayed by the non-normalized indices. For example, MCARI1, which had the lowest mean values among the MS indices in this case, was found suitable for estimating Chl content variation by Zhang et al (2021) [74].…”

“…Studies dedicated to rice production areas located in the Mediterranean basin are also lacking. These areas have specific climatic conditions, e.g., [35][36][37], that differ from those found in other important rice production areas. Their vulnerability to climate change and environmental hazards demands that agricultural production in these areas receives special attention, particularly rice farming.…”

Optimal-Band Analysis for Chlorophyll Quantification in Rice Leaves Using a Custom Hyperspectral Imaging System

“…GNDVI facilitates the assessment of the photosynthetic activity and exhibits greater sensitivity towards variations in chlorophyll concentration compared to NDVI [ 52 ]. GNDVI provides advantages in scenarios where hyperspectral data are deficient in an extreme red channel [ 53 ]. VIs using the green wavelength can detect changes in chlorophyll contents at the leaf and canopy scale, and are appropriate for determining the developmental phases and stress levels of a plant [ 54 ].…”

SEA-Rice-Ci10: High-resolution Mapping of Rice Cropping Intensity and Harvested Area Across Southeast Asia using the Integration of Sentinel-1 and Sentinel-2 Data

“…To capture rice phenology, NDVI from Sentinel-2 was used in identifying rice growth stages and differentiating rice plants from other land covers, while MNDWI from Sentinel-2 identified flooded areas during rice transplanting (Sakamoto et al, 2018;de Lima et al, 2021). MNDWI was used to improve the detection of flooded areas while reducing the prominence of built-up features, which often exhibit similar reflectance characteristics to water or wet surfaces (Mansaray et al, 2019).…”