Assessing the performance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale

Crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. Improvements in the timeliness and accuracy of yield forecasting by incorporating near real-time remote sensing data and the use of sophisticated statistical methods can improve our capacity to respond effectively to these challenges. The objectives of this study were (i) to investigate the use of derived vegetation indices for the yield forecasting of spring wheat (Triticum aestivum L.) from the Moderate resolution Imaging Spectroradiometer (MODIS) at the ecodistrict scale across Western Canada with the Integrated Canadian Crop Yield Forecaster (ICCYF); and (ii) to compare the ICCYF-model based forecasts and their accuracy across two spatial scales-the ecodistrict and Census Agricultural Region (CAR), namely in CAR with previously reported ICCYF weak performance. Ecodistricts are areas with distinct climate, soil, landscape and ecological aspects, whereas CARs are census-based/statistically-delineated areas. Agroclimate variables combined respectively with MODIS-NDVI and MODIS-EVI indices were used as inputs for the in-season yield forecasting of spring wheat during the 2000–2010 period. Regression models were built based on a procedure of a leave-one-year-out. The results showed that both agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI performed equally well predicting spring wheat yield at the ECD scale. The mean absolute error percentages (MAPE) of the models selected from both the two data sets ranged from 2% to 33% over the study period. The model efficiency index (MEI) varied between -1.1 and 0.99 and -1.8 and 0.99, respectively for the agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI data sets. Moreover, significant improvement in forecasting skill (with decreasing MAPE of 40% and 5 times increasing MEI, on average) was obtained at the finer, ecodistrict spatial scale, compared to the coarser CAR scale. Forecast models need to consider the distribution of extreme values of predictor variables to improve the selection of remote sensing indices. Our findings indicate that statistical-based forecasting error could be significantly reduced by making use of MODIS-EVI and NDVI indices at different times in the crop growing season and within different sub-regions

Long-term hindcasts of wheat yield in fields using remotely sensed phenology, climate data and machine learning

Satellite remote sensing offers a cost-effective means of generating long-term hindcasts of yield that can be used to understand how yield varies in time and space. This study investigated the use of remotely sensed phenology, climate data and machine learning for estimating yield at a resolution suitable for optimising crop management in fields. We used spatially weighted growth curve estimation to identify the timing of phenological events from sequences of Landsat NDVI and derive phenological and seasonal climate metrics. Using data from a 17,000 ha study area, we investigated the relationships between the metrics and yield over 17 years from 2003 to 2019. We compared six statistical and machine learning models for estimating yield: multiple linear regression, mixed effects models, generalised additive models, random forests, support vector regression using radial basis functions and deep learning neural networks. We used a 50-50 train-test split on paddock-years where 50% of paddock-year combinations were randomly selected and used to train each model and the remaining 50% of paddock-years were used to assess the model accuracy. Using only phenological metrics, accuracy was highest using a linear mixed model with a random effect that allowed the relationship between integrated NDVI and yield to vary by year (R2 = 0.67, MAE = 0.25 t ha−1, RMSE = 0.33 t ha−1, NRMSE = 0.25). We quantified the improvements in accuracy when seasonal climate metrics were also used as predictors. We identified two optimal models using the combined phenological and seasonal climate metrics: support vector regression and deep learning models (R2 = 0.68, MAE = 0.25 t ha−1, RMSE = 0.32 t ha−1, NRMSE = 0.25). While the linear mixed model using only phenological metrics performed similarly to the nonlinear models that are also seasonal climate metrics, the nonlinear models can be more easily generalised to estimate yield in years for which training data are unavailable. We conclude that long-term hindcasts of wheat yield in fields, at 30 m spatial resolution, can be produced using remotely sensed phenology from Landsat NDVI, climate data and machine learning

Advancing agricultural monitoring for improved yield estimations using SPOT-VGT and PROBA-V type remote sensing data

Accurate and timely crop condition monitoring is crucial for food management and the economic development of any nation. However, accurately estimating crop yield from the field to global scales is a challenge. According to the global strategy of the World Bank, in order to improve national agricultural statistics, crop area, crop production, and crop yield are key variables that all countries should be able to provide. Crop yield assessment requires that both an estimation of the quantity of a product and the area provided for that product should be available. The definition seems simple; however, these measurements are time consuming and subject to error in many circumstances. Remote sensing is one of several methods used for crop yield estimation. The yield results from a combination of environmental factors, such as soil, weather, and farm management, which are responsible for the unique spectral signature of a crop captured by satellite images. Additionally, yield is an expression of the state, structure, and composition of the plant. Various indices, crop masks, and land observation sensors have been developed to remotely observe and control crops in different regions. This thesis focuses on how much low spatial resolution satellites, such as Project for On Board Autonomy Vegetation (PROBA V), can contribute to global crop monitoring by aiding the search for improved methods and datasets for better crop yield estimation. This thesis contains three chapters. The first chapter explores how an existing product, Dry Matter Productivity (DMP), that has been developed for Satellites Pour l’Observation de la Terre or Earth observing Satellites VeGeTation (SPOT VGT), and transferred to PROBA V, can be improved to more closely relate to yield anomalies across selected regions. This chapter also covers the testing of the contribution of stress factors to improve wheat and maize yield estimations. According to Monteith’s theory, crop biomass linearly correlates with the amount of Absorbed Photosynthetically Active Radiation (APAR) and constant Radiation Use Efficiency (RUE) downregulated by stress factors such as CO2, fertilization, temperature, and water stress. The objective of this chapter is to investigate the relative importance of these stress factors in relation to the regional biomass production and yield. The production efficiency model Copernicus Global Land Service Dry Matter Productivity (CGLS DMP), which follows Monteith’s theory, is modified and evaluated for common wheat and silage maize in France, Belgium, and Morocco using SPOT VGT for the 1999–2012 period. The correlations between the crop yield data and the cumulative modified DMP, CGLS DMP, Fraction of APAR (fAPAR), and Normalized Difference Vegetation Index (NDVI) values are analyzed for different crop growth stages. The best results are obtained when combinations of the most appropriate stress factors are included for each selected region, and the modified DMP during the reproductive stage is accumulated. Though no single solution can demonstrate an improvement of the global product, the findings support an extension of the methodology to other regions of the world. The second chapter demonstrates how PROBA V can be used effectively for crop identification mapping by utilizing spectral matching techniques and phenological characteristics of different crop types. The study sites are agricultural areas spread across the globe, located in Flanders (Belgium), Sria (Russia), Kyiv (Ukraine), and Sao Paulo (Brazil). The data are collected for the 2014–2015 season. For each pure pixel within a field, the NDVI profile of the crop type for its growing season is matched with the reference NDVI profile. Three temporal windows are tested within the growing season: green up to senescence, green up to dormancy, and minimum NDVI at the beginning of the growing season to minimum NDVI at the end of the growing season. In order of importance, the crop phenological development period, parcel size, shorter time window, number of ground truth parcels, and crop calendar similarity are the main reasons behind the differences between the results. The methodology described in this chapter demonstrates the potentials and limitations of using 100 m PROBA V with revisiting frequency every 5 days in crop identification across different regions of the world. The final chapter explores the trade off between the different spatial resolutions provided by PROBA V products versus the temporal frequency and, additionally, explores the use of thermal time to improve statistical yield estimations. The ground data are winter wheat yields at the field level for 39 fields across Northern France during one growing season 2014–2015. An asymmetric double sigmoid function is fitted, and the NDVI values are integrated over thermal time and over calendar time for the central pixel of the field, exploring different thresholds to mark the start and end of the cropping season. The integrated NDVI values with different NDVI thresholds are used as a proxy for yield. In addition, a pixel purity analysis is performed for different purity thresholds at the 100 m, 300 m, and 1 km resolutions. The findings demonstrate that while estimating winter wheat yields at the field level with pure pixels from PROBA V products, the best correlation is obtained with a 100 m resolution product. However, several fields must be omitted due to the lack of observations throughout the growing season with the 100 m resolution dataset, as this product has a lower temporal resolution compared to 300 m and 1 km. This thesis is a modest contribution to the remote sensing and data analysis field with its own merits, in particular with respect to PROBA V. The experiments provide interesting insight into the PROBA V dataset at 1 km, 300 m, and 100 m resolutions. Specifically, the results show that 100 m spatial resolution imagery could be used effectively and advantageously in agricultural crop monitoring and crop identification at local – field level – and regional – the administrative regions defined by the national governments – levels. Furthermore, this thesis discusses the limitations of using a low resolution satellite, such as the PROBA V 100 m dataset, in crop monitoring and identification. Also, several recommendations are made for space agencies that can be used when designing the new generation of satellites

A Review of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data

Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes, and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness, and growing season length) often termed “land surface phenology,” as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data-processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multiscale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams, and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization

Land Surface Phenologies and Seasonalities Using Cool Earthlight in Temperate and Tropical Croplands

In today’s world of increasing food insecurity due to more frequent and extreme events (droughts, floods), a comprehensive understanding of global cropland dynamics is critically needed. Land surface parameters derived from the passive microwave Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and AMSR2 data enable monitoring of cropland dynamics and they can complement visible to near infrared (VNIR) and thermal infrared (TIR) data. Passive microwave data are less sensitive to atmospheric effects, cloud contamination, and solar illumination constraints resulting in finer temporal resolution suitable to track the temporal progression of cropland cover development compared to the VNIR data that has coarser temporal resolution due to compositing to lessen the atmospheric effects. Both VNIR and TIR data have moderate to fine spatial resolution compared to passive microwaves, due to the faint microwave flux from the planetary surface. I used AMSR, MODIS, TRMM, and simplified surface energy balance (SSEB) data to study cropland dynamics from 2003-2015 in North Dakota, USA, the Canadian Prairie Provinces, Northern Eurasia, and East Africa: a contrast between crop exporting regions and a food insecure region. Croplands in the temperate region are better studied compared to that of the tropics. The objective of this research was to characterize cropland dynamics in the tropics based on the knowledge gained about the microwave products in the temperate croplands. This study also aimed at assessing the utility of passive microwave data for cropland dynamics study, especially for tropical cropland regions that are often cloud-obscured during the growing season and have sparse in situ data networks. Using MODIS land cover data, I identified 162 AMSR grid cells (25km*25km=625km2) dominated by croplands within the study regions. To fit the passive microwave time series data to environmental forcings, I used the convex quadratic (CxQ) model fit that has been successfully applied with the VNIR and TIR data to herbaceous vegetation in temperate and boreal ecoregions. Land surface dynamics in the thermally-limited temperate croplands were characterized as a function of temperature; whereas, a function of moisture to model land surface dynamics in the tropical croplands. In the temperate croplands, growing degree-day (GDD), NDVI, and vegetation optical depth (VOD) were modeled as a convex quadratic function of accumulated GDD (AGDD) derived from AMSR air temperature data, yielding high coefficients of determination (0.88≤ r2≤0.98) Deviations of GDD from the long term average CxQ model by site corresponded to peak VI producing negative residuals (arising from higher latent heat flux) and low VI at beginning and end of growing season producing positive residuals (arising from higher sensible heat flux). In Northern Eurasia, sites at lower latitude (44° - 48° N) that grow winter grains showed either a longer unimodal growing season or a bimodal growing season; whereas, sites at higher latitude (48° - 56° N) where spring grains are cultivated showed shorter, unimodal growing seasons. Peak VOD showed strong linear correspondence with peak greenness (NDVI) with r2\u3e0.8, but with a one-week lag. The AMSR data were able to capture the effects of the 2010 and 2007 heat waves that devastated grain production in southwestern Russia and Northern Kazakhstan, and Ukraine, respectively, better than the MODIS data. In East African croplands, the AMSR, TRMM, and SSEB datasets modeled as a convex quadratic function of cumulative water-vapor-days displayed distinct cropland dynamics in space and time, including unimodal and bimodal growing seasons. Interannual moisture variability is at its highest at the beginning of the growing season affecting planting times of crops. Moisture time to peak from AMSR and TRMM land surface parameters displayed strong correspondence (r2 \u3e 0.80) and logical lags among variables. Characterizing cropland dynamics based on the synergistic use of complementary remote sensing data should help to advance and improve agricultural monitoring in tropical croplands that are often associated with food insecurity

Novel Satellite-Based Methodologies for Multi-Sensor and Multi-Scale Environmental Monitoring to Preserve Natural Capital

Global warming, as the biggest manifestation of climate change, has changed the distribution of water in the hydrological cycle by increasing the evapotranspiration rate resulting in anthropogenic and natural hazards adversely affecting modern and past human properties and heritage in different parts of the world. The comprehension of environmental issues is critical for ensuring our existence on Earth and environmental sustainability. Environmental modeling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as processes related to the atmosphere, hydrology, land surface, and vegetation. The environmental models can be applied on a wide range of spatiotemporal scales (i.e. from local to global and from daily to decadal levels); and they can employ various types of models (e.g. process-driven, empirical or data-driven, deterministic, stochastic, etc.). Satellite remote sensing and Earth Observation techniques can be utilized as a powerful tool for flood mapping and monitoring. By increasing the number of satellites orbiting around the Earth, the spatial and temporal coverage of environmental phenomenon on the planet has in-creased. However, handling such a massive amount of data was a challenge for researchers in terms of data curation and pre-processing as well as required computational power. The advent of cloud computing platforms has eliminated such steps and created a great opportunity for rapid response to environmental crises. The purpose of this study was to gather state-of-the-art remote sensing and/or earth observation techniques and to further the knowledge concerned with any aspect of the use of remote sensing and/or big data in the field of geospatial analysis. In order to achieve the goals of this study, some of the water-related climate-change phenomena were studied via different mathematical, statistical, geomorphological and physical models using different satellite and in-situ data on different centralized and decentralized computational platforms. The structure of this study was divided into three chapters with their own materials, methodologies and results including: (1) flood monitoring; (2) soil water balance modeling; and (3) vegetation monitoring. The results of this part of the study can be summarize in: 1) presenting innovative procedures for fast and semi-automatic flood mapping and monitoring based on geomorphic methods, change detection techniques and remote sensing data; 2) modeling soil moisture and water balance components in the root zone layer using in-situ, drone and satellite data; incorporating downscaling techniques; 3) combining statistical methods with the remote sensing data for detecting inner anomalies in the vegetation covers such as pest emergence; 4) stablishing and disseminating the use of cloud computation platforms such as Google Earth Engine in order to eliminate the unnecessary steps for data curation and pre-processing as well as required computational power to handle the massive amount of RS data. As a conclusion, this study resulted in provision of useful information and methodologies for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

Evaluating Vegetation Response to Water Stress Using Close-Range and Satellite Remote Sensing

Drought is a weather related natural disaster that occurs in virtually all climatic zones of the world. In the last century, almost all parts of the contiguous United States have experienced several prolonged drought events with considerable impacts on the agricultural economy and environment. With changing climates, the droughts are expected to be more severe, longer, and widespread in many parts of the world including sections of the United States. Understanding the response of vegetation to water stress using remote sensing technologies will enhance our ability to detect and monitor drought. This research evaluates the response of vegetation to drought-related water stress at the leaf, canopy, and landscape scales using remotely sensed reflectance and/or thermal data. At the leaf level, a crop water stress index model was developed using high spatial resolution thermal imagery to estimate Relative Water Content (RWC) in soybean leaves. The model showed a higher accuracy in RWC determination (85%) compared to the raw temperature based RWC determination (69%). At the canopy level, multi-year close-range reflectance based vegetation indices (VIs) were correlated with soil moisture measured at four depths of maize and soybean root zone. Results indicated that maize VIs were significantly related to soil moisture at deeper depths and kept the soil moisture memory up to previous 45-days. Soybeans VIs were significantly related to soil moisture at shallower depths and kept a relatively shorter (5-days) memory of soil moisture compared to maize. At the landscape scale, Terra-MODIS Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) products were used to detect drought-induced stress in vegetation including corn, soybeans, and three grassland cover types across the state of Nebraska. Results indicate that the majority of the land cover pixels experienced significantly higher daytime and nighttime LSTs and lower NDVI during the drought-year growing season compared to the non-drought year. The findings of this dissertation research will contribute toward the development of more robust tools for monitoring drought stress in vegetation. Advisors: Sunil Narumalani and Brian D. Wardlo

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

Characterizing Cropland Phenology in Major Grain Production Areas of Russia, Ukraine, and Kazakhstan by the Synergistic Use of Passive Microwave and Visible to Near Infrared Data

We demonstrate the synergistic use of surface air temperature retrieved from AMSR-E (Advanced Microwave Scanning Radiometer on Earth observing satellite) and two vegetation indices (VIs) from the shorter wavelengths of MODIS (MODerate resolution Imaging Spectroradiometer) to characterize cropland phenology in the major grain production areas of Northern Eurasia from 2003–2010. We selected 49 AMSR-E pixels across Ukraine, Russia, and Kazakhstan, based on MODIS land cover percentage data. AMSR-E air temperature growing degree-days (GDD) captures the weekly, monthly, and seasonal oscillations, and well correlated with station GDD. A convex quadratic (CxQ) model that linked thermal time measured as growing degree-days to accumulated growing degree-days (AGDD) was fitted to each pixel’s time series yielding high coefficients of determination (0.88 ≤ r2 ≤ 0.98). Deviations of observed GDD from the CxQ model predicted GDD by site corresponded to peak VI for negative residuals (period of higher latent heat flux) and low VI at beginning and end of growing season for positive residuals (periods of higher sensible heat flux). Modeled thermal time to peak, i.e., AGDD at peak GDD, showed a strong inverse linear trend with respect to latitude with r2 of 0.92 for Russia and Kazakhstan and 0.81 for Ukraine. MODIS VIs tracked similar seasonal responses in time and space and were highly correlated across the growing season with r2 \u3e 0.95. Sites at lower latitude (≤49°N) that grow winter and spring grains showed either a bimodal growing season or a shorter unimodal winter growing season with substantial inter-annual variability, whereas sites at higher latitude (≥56°N) where spring grains are cultivated exhibited shorter, unimodal growing seasons. Sites between these extremes exhibited longer unimodal growing seasons. At some sites there were shifts between unimodal and bimodal patterns over the study period. Regional heat waves that devastated grain production in 2007 in Ukraine and in 2010 in Russia and Kazakhstan appear clearly anomalous. Microwave based surface air temperature data holds great promise to extend to parts of the planet where the land surface is frequently obscured by clouds, smoke, or aerosols, and where routine meteorological observations are sparse or absent