Francesco Nutini

Comparative analysis of normalised difference spectral indices derived from MODIS for detecting surface water in flooded rice cropping systems

Identifying managed flooding in paddy fields is commonly used in remote sensing to detect rice. Such flooding, followed by rapid vegetation growth, is a reliable indicator to discriminate rice. Spectral indices (SIs) are often used to perform this task. However, little work has been done on determining which spectral combination in the form of Normalised Difference Spectral Indices (NDSIs) is most appropriate for surface water detection or which thresholds are most robust to separate water from other surfaces in operational contexts. To address this, we conducted analyses on satellite and field spectral data from an agronomic experiment as well as on real farming situations with different soil and plant conditions. Firstly, we review and select NDSIs proposed in the literature, including a new combination of visible and shortwave infrared bands. Secondly, we analyse spectroradiometric field data and satellite data to evaluate mixed pixel effects. Thirdly, we analyse MODIS data and Landsat data at four sites in Europe and Asia to assess NDSI performance in real-world conditions. Finally, we test the performance of the NDSIs on MODIS temporal profiles in the four sites. We also compared the NDSIs against a combined index previously used for agronomic flood detection. Analyses suggest that NDSIs using MODIS bands 4 and 7, 1 and 7, 4 and 6 or 1 and 6 perform best. A common threshold for each NDSI across all sites was more appropriate than locally adaptive thresholds. In general, NDSIs that use band 7 have a negligible increase in Commission Error over those that use band 6 but are more sensitive to water presence in mixed land cover conditions typical of moderate spatial resolution analyses. The best performing NDSI is comparable to the combined index but with less variability in performance across sites, suggesting a more succinct and robust flood detection method.

Land-use and land-cover change detection in a semi-arid area of Niger using multi-temporal analysis of Landsat images

Recent studies using low-resolution satellite time series show that the Sahelian belt of West Africa is witnessing an increase in vegetation cover/biomass, called re-greening. However, detailed information on local processing and changes is rare or lacking. A multi-temporal set of Landsat images was used to produce land-cover maps for the years 2000 and 2007 in a semi-arid region of Niger, where an anomalous vegetation trend was previously detected. Several supervised classification approaches were tested: spectral classification of single Landsat data, temporal classification of normalized difference vegetation index time series from Landsat images, and two-step classification integrating both these approaches. The accuracy of the land-cover maps obtained ranges between 80% and 90% overall for the two-step classification approach. Comparison of the maps between the two years indicates a stable semi-arid region, where some change in hot spots exists despite a generally constant level of rainfall in the area during this period. In particular, the Dallol Bosso fossil valley highlights an increase in cultivated land, while a decrease in herbaceous vegetation was observed outside the valley where rangeland is the predominant natural landscape.

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R2 > 0.93) and good accuracies (RMSE < 0.83, rRMSEm < 23.6% and rRMSEr < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring.

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems.

Evaporative Fraction as an Indicator of Moisture Condition and Water Stress Status in Semi-Arid Rangeland Ecosystems

Rangeland monitoring services require the capability to investigate vegetation condition and to assess biomass production, especially in areas where local livelihood depends on rangeland status. Remote sensing solutions are strongly recommended, where the systematic acquisition of field data is not feasible and does not guarantee properly describing the spatio-temporal dynamics of wide areas. Recent research on semi-arid rangelands has focused its attention on the evaporative fraction (EF), a key factor to estimate evapotranspiration (ET) in the energy balance (EB) algorithm. EF is strongly linked to the vegetation water status, and works conducted on eddy covariance towers used this parameter to increase the performances of satellite-based biomass estimation. In this work, a method to estimate EF from MODIS products, originally developed for evapotranspiration estimation, is tested and evaluated. Results show that the EF estimation from low spatial resolution over wide semi-arid area is feasible. Estimated EF resulted in being well correlated to field ET measurements, and the spatial patterns of EF maps are in agreement with the well-known climatic and landscape Sahelian features. The preliminary test on rangeland biomass production shows that satellite-retrieved EF as a water availability factor significantly increased the capacity of a remote sensing operational product to detect the variability of the field biomass measurements.

Rapid Assessment of Crop Status: An Application of MODIS and SAR Data to Rice Areas in Leyte, Philippines Affected by Typhoon Haiyan

Asian countries strongly depend on rice production for food security. The major rice-growing season (June to October) is highly exposed to the risk of tropical storm related damage. Unbiased and transparent approaches to assess the risk of rice crop damage are essential to support mitigation and disaster response strategies in the region. This study describes and demonstrates a method for rapid, pre-event crop status assessment. The ex-post test case is Typhoon Haiyan and its impact on the rice crop in Leyte Province in the Philippines. A synthetic aperture radar (SAR) derived rice area map was used to delineate the area at risk while crop status at the moment of typhoon landfall was estimated from specific time series analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data. A spatially explicit indicator of risk of standing crop loss was calculated as the time between estimated heading date and typhoon occurrence. Results of the analysis of pre- and post-event SAR images showed that 6500 ha were flooded in northeastern Leyte. This area was also the region most at risk to storm related crop damage due to late establishment of rice. Estimates highlight that about 700 ha of rice (71% of which was in northeastern Leyte) had not reached maturity at the time of the typhoon event and a further 8400 ha (84% of which was in northeastern Leyte) were likely to be not yet harvested. We demonstrated that the proposed approach can provide pre-event, in-season information on the status of rice and other field crops and the risk of damage posed by tropical storms.

Assimilating seasonality information derived from satellite data time series in crop modelling for rice yield estimation

The agricultural sector is facing important global challenges due to the pressure of food demand, increased price-competition produced by market globalization and food price volatility (G20 Agriculture Action Plan), and the necessity of more environmentally and economically sustainable farming. Earth Observation (EO) systems can significantly contribute to these topics by providing reliable real time information on crop distribution, status and seasonal dynamics. ERMES FP7 project aims to create added-value information for the rice agro-sector by integrating EO-products in crop models. Time series of moderate resolution satellite data are analyzed exploiting the PhenoRice algorithm to retrieve seasonal occurrence of agro-practices and phenological stages. Eleven years (2003-2013) of rice seasonal metrics were derived and used in WARM crop model to set up a crop forecasting systems, with the aim to provide crop yield estimates for regional authorities. Preliminary test conducted in Italy on indica rice ecotype demonstrated that the system can provide rice yield estimates explaining up to 90% of interannual variability.

Analysis of vegetation pasture climate response on Sahel region through 10 years of remotely sensed data

Studies of impact of human activity on the vegetation dynamics in the Sahel belt of Africa are recently re-invigorated due to a new scientific findings that highlighted the primary role of climate in the drought crises of the 70s-80s. Time series of satellite observations allowed identifying re-greening of the Sahel belt that indicates no sensible human effect on vegetation dynamics at sub continental scale from 80s to late 90s. However, several regional/local crises related to natural resources occurred in the last decades underling that more detailed studies are needed. This study contribute to the understanding of climate/human impact on pasture vegetation status in the Sahel region in the last decade (1999- 2008). The use of a time-series of SPOT-VGT NDVI and FEWS-RFE rainfall estimates allowed to analyze vegetation and rainfall trends and identify local anomalous situation in the region. Trend analysis has been conducted to map a) areas where vegetation has been significantly decreased or increased due to rainfall pattern and b) anomalous zones where vegetation dynamics could not be fully explained by rainfall pattern by. The identified hot-spots areas have been compared with spatial information on the reported humanitarian-food crisis events in order to understand chronic situation where ecosystems carrying capacity is endangered. The results of this study show that even if a general positive re-greening situation is evident for the entire Sahel, some serious hot spots exist in areas where cropping system and pasture activity are conflicting.

Evaluation of remotely sensed DMP product using multi-year field measurements of biomass in West Africa

The Sahelian belt of West Africa is a region characterized by wide climate variations, which can in turn affect the survival of local populations especially in rangeland, as happened during the dramatic food crisis in the 70-80s caused by severe drought. This work has been carried out in the framework of the EU FP7 Geoland2 project as a contribution to the ECOWAS component (Economic Community Of West African States) of the AMESD (African Monitoring of the Environment for Sustainable Development) programme with the purpose of establishing the reliability of Dry Matter Productivity (DMP) developed by Flemish Institute for Technological Research (VITO), a spatial estimation of dry matter (DM) obtained from remotely sensed data. DMP can be of great help in monitoring savanna pasturelands in a region characterized by food insecurity and a significant variability of biomass production, linked to climate variations, which can in turn affect the survival of local populations. The evaluation of DMP was carried out thanks to the Centre de Suivi Ecologique (CSE) and Action Contre la Fame (ACF), the partners who provided the field biomass measurements. The paper shows the correlation of DMP with field measurements of herbaceous biomass, and discusses the differences among the different sites where ground data were collected. The analysis of other environmental variables (land cover, rainfall), which can be influential on rangeland biomass production, is presented in order to better explain the variance of field measurements among the different years.

An operational workflow to assess rice nutritional status based on satellite imagery and smartphone apps

Abstract Nitrogen fertilization plays a key role in rice productivity and environmental impact of rice-based cropping systems, as well as on farmers’ income, representing one of the main cost items of rice farming. Average nitrogen use efficiency in rice paddies is often very low (about 30%), leading to groundwater contamination, greenhouse gases emission, and economic losses for farmers. The resulting pressure on many actors in the rice production chain has generated a need for operational tools and techniques able to increase nitrogen use efficiency. We present an operational workflow for producing nitrogen nutritional index (NNI) maps at sub-field scale based on the combined use of high-resolution satellite images and ground-based estimates of Leaf Area Index (LAI) and plant nitrogen concentration (PNC, %) data collected using smart apps. The workflow was tested in northern Italy. The analysis reveals that vegetation indices are satisfactorily correlated with LAI (r2 > 0.77, p   0.55, p