Use of remote sensing approaches for agricultural applications

Doctor of PhilosophyDepartment of AgronomyIgnacio CiampittiRemote sensing is a technology that has been utilized extensively in agriculture due to its capacity to provide precise and detailed information on various aspects of agricultural production. Farmers and researchers have utilized this technology to gain valuable insights regarding, among other things, crop phenology, yield prediction, land classification, soil quality, water management, and environmental monitoring. The present dissertation is structured into six chapters, with the first serving as an introduction to remote sensing technology in agriculture and the last chapter offering concluding remarks. The remaining chapters delve into various applications of remote sensing technology in agriculture. The second and third chapters examine the potential of remote sensing to classify maize phenology in Kansas, utilizing three distinct image resolutions. The second chapter identifies the optimal combination of spectral bands, vegetation indices, and weather data for phenology classification using Landsat 8 as a source of spectral information.  In chapter three, greater temporal and spatial resolution was  tested using Sentinel-2 and Planet Fusion, and the classification performance of both sources was compared. The model was tested in different areas, and the results emphasized the significance of temporal and spatial resolution for traits like phenology that can change rapidly. Chapter four explores the use of remote sensing technology to identify areas in Cambodia with traditional management practices where conservation agriculture could play a critical role. The study employs 3-meter daily imagery from Planet Fusion and image segmentation tools to distinguish between burned patches and bare soil after ploughing. The results demonstrate that these images, in conjunction with image segmentation tools, have the potential to identify management practices in areas where obtaining ground-truth data could be challenging.  Finally, chapter five discusses the current state of the art and the necessary changes to integrate soil science methods and remote sensing for determining soil organic carbon. This chapter examines the challenges and opportunities associated with using remote sensing to monitor soil properties and offers viable solutions for bridging these two domains

Assessing Spatio-Temporal Dynamics of Deep Percolation Using Crop Evapotranspiration Derived from Earth Observations through Google Earth Engine

Excess irrigation may result in deep percolation and nitrate transport to groundwater. Furthermore, under Mediterranean climate conditions, heavy winter rains often result in high deep percolation, requiring the separate identification of the two sources of deep percolated water. An integrated methodology was developed to estimate the spatio-temporal dynamics of deep percolation, with the actual crop evapotranspiration (ETc act) being derived from satellite images data and processed on the Google Earth Engine (GEE) platform. GEE allowed to extract time series of vegetation indices derived from Sentinel-2 enabling to define the actual crop coefficient (Kc act) curves based on the observed lengths of crop growth stages. The crop growth stage lengths were then used to feed the soil water balance model ISAREG, and the standard Kc values were derived from the literature; thus, allowing the estimation of irrigation water requirements and deep drainage for independent Homogeneous Units of Analysis (HUA) at the Irrigation Scheme. The HUA are defined according to crop, soil type, and irrigation system. The ISAREG model was previously validated for diverse crops at plot level showing a good accuracy using soil water measurements and farmers’ irrigation calendars. Results show that during the crop season, irrigation caused 11 3% of the total deep percolation. When the hotspots associated with the irrigation events corresponded to soils with low suitability for irrigation, the cultivated crop had no influence. However, maize and spring vegetables stood out when the hotspots corresponded to soils with high suitability for irrigation. On average, during the off-season period, deep percolation averaged 54 6% of the annual precipitation. The spatial aggregation into the Irrigation Scheme scale provided a method for earth-observation-based accounting of the irrigation water requirements, with interest for the water user’s association manager, and at the same time for the detection of water losses by deep percolation and of hotspots within the irrigation schemeinfo:eu-repo/semantics/publishedVersio

Mapping agricultural intensification in the Brazilian savanna: a machine learning approach using harmonized data from Landsat Sentinel-2.

Agricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pressure for new land clearing. Due to the growing demand for more sustainable practices, more accurate information on geospatial monitoring is required. Remote sensing products and artificial intelligence models for pixel-by-pixel classification have great potential. Therefore, we developed a methodological framework with spectral indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil-Adjusted Vegetation Index (SAVI)) derived from the Harmonized Landsat Sentinel-2 (HLS) and machine learning algorithms (Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost)) to map agricultural intensification considering three hierarchical levels, i.e., temporary crops (level 1), the number of crop cycles (level 2), and the crop types from the second season in double-crop systems (level 3) in the 2021-2022 crop growing season in the municipality of Sorriso, Mato Grosso State, Brazil. All models were statistically similar, with an overall accuracy between 85 and 99%. The NDVI was the most suitable index for discriminating cultures at all hierarchical levels. The RF-NDVI combination mapped best at level 1, while at levels 2 and 3, the best model was XGBoost-NDVI. Our results indicate the great potential of combining HLS data and machine learning to provide accurate geospatial information for decision-makers in monitoring agricultural intensification, with an aim toward the sustainable development of agriculture

Mapping and modeling groundnut growth and productivity in rainfed areas of Tamil Nadu

A research study was conducted at Tamil Nadu Agricultural University, Coimbatore during kharif and rabi 2015 to estimate groundnut area, model growth and productivity and assess the vulnerability of groundnut to drought using remote sensing techniques. Multi temporal Sentinel 1A satellite data at VV and VH polarization with 20 m spatial resolution was acquired from May, 2015 to January, 2016 at 12 days interval and processed using MAPscape-RICE software. Continuous monitoring was done for ground truth on crop parameters in twenty monitoring sites and validation exercise was done for accuracy assessment. Input files on soil, weather and management practices were generated and crop coefficients pertaining to varieties were developed to assess growth and productivity of groundnut using DSSAT CROPGRO-Peanut model. Outputs from remote sensing and DSSAT model were assimilated to generate LAI thereby groundnut yield spatially and validated against observed yields. Being a rainfed crop, vulnerability of groundnut to drought was assessed integrating different meteorological and spectral indices viz., Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI) and Water Requirement Satisfaction Index (WRSI).Spectral dB curve of groundnut was generated using temporal multi date Sentinel 1A data. A detailed analysis of temporal signatures of groundnut showed a minimum at sowing and a peak at pod development stage and decreasing thereafter towards maturity. Groundnut crop expressed a significant temporal behaviour and large dynamic range (-11.74 to -5.31 in VV polarization and -20.04 to -13.05 in VH polarization) during its growth period. Groundnut area map was generated using maximum likelihood classifier integrating multi temporal features with a classification accuracy of 87.2 per cent and a kappa score of 0.74. The total classified groundnut area in the study districts was 88023 ha covering 17817 and 22582 ha in Salem and Namakkal districts during kharif 2015 while Villupuram and Tiruvannamalai districts accounted for 22722 and 24903 ha respectively during rabi 2015. Blockwise statistics on groundnut area during both seasons were also generated. To model growth and productivity of groundnut in DSSAT, weather and soil input files were generated using weatherman and ‘S’ build respectively besides deriving genetic coefficients for CO 6, TMV 7 and VRI 2 varieties of groundnut. Growth and development variables of groundnut were simulated using CROPGROPeanut model i.e., days to emergence (7-9 days) and anthesis (25-32 days), canopy height (63 to 70 cm), maximum LAI (1.12 to 3.07) and biomass (4176 to 9576 kg ha-1 across twenty monitoring locations spatially. The resultant pod yield was simulated to be 1796 to 3060 kg ha-1 with a harvest index of 0.28 to 0.43. On comparison of LAI between observed (2.01 to 4.05) and simulated values (1.12 to 3.07) the CROPGRO-Peanut model was found to under estimate the values with R2, RMSE and NRMSE of 0.82, 1.10 and 34 per cent. However, the model predicted the biomass of groundnut with an agreement of 89 per cent through the simulated values of 4176 to9576 kg ha-1 as against the observed biomass to 4620 to 9959 kg ha-1. The simulated pod yields of groundnut in the study area were 1796 to 3060 kg ha-1 as compared to the observed yields of 2115 to 2750 kg ha-1. The overall agreement between simulated and observed yields was 84 per cent with the average errors of 0.81, 342 kg ha-1 and 16 percent for R2, RMSE and NRMSE respectively. LAI values of groundnut, generated spatially through suitable regression models using dB from satellite images and LAI from DSSAT, ranged from 1.31 to 3.23 with R2, RMSE and NRMSE of 0.86, 0.78 and 24 per cent respectively on comparison with observed values. Remote sensing based spatial estimation resulted in groundnut pod yields of 1570 to 3102 kg ha-1 across the study districts of Salem, Namakkal, Tiruvannamalai and Villupuram. In the 20 monitoring locations, the pod yields were estimated to be 1912 to 2975 kg ha-1 as against the observed pod yields of 1450 to 2750 kg ha-1 with a fairly good agreement of 80 per cent. The vulnerability of groundnut was assessed using different drought indices viz., SPI, NDVI and WRSI. Considering SPI, out of the total groundnut area of 88023 ha, an area of 86607 ha was found to be under near normal condition based on deviation of rainfall received during cropping season from historical precipitation. Similarly NDVI, an indicator of vegetation condition during the cropping season, showed that 14272 ha of groundnut area were under stressed condition during 2015. An area of 40981 ha in Villupuram and Tiruvannamalai districts was found to be under chances of crop failure based on Water Requirement Satisfaction index (WRSI). Major groundnut areas of Salem district (14188 ha) was under medium risk zone. Considering overall vulnerability, whole district of Villupuram was adjudged as highly vulnerable to drought with regard to groundnut cultivation whereas four blocks of Salem, eight blocks of Namakkal and all the blocks of Tiruvannamalai were found to be moderately vulnerable to drought

The use of satellite-derived data and neural-network analysis to examine variation in maize yield under changing climate

Climate change and variability is foreseen to have direct and indirect effects on the existing agricultural production systems potentially threatening local, regional and/or global food security depending on the spatial scale of the change. The trend and level of impact caused by climate change and/or variability is region dependent and adaptive capacity. Climate change is projected to have more adverse impact in high vulnerability areas of sub-Saharan Africa. This study aimed to examine the variation in maize yield and develop a framework for predicting maize yield in response to climate change. To achieve this aim, this study has analyzed the impact of agro-climatic parameters on maize production across the major four maize producing provinces of South Africa. This study went further to investigate changes in the satellite derived phenological parameters and its relationship with maize production. In addition, the influence of drought (a derivative of climate change) on maize production was investigated. The study concluded by integrating all datasets used in each objective to develop an empirical predicting model using artificial neural network. Previous studies have quantified the impact of climatic variables on maize and at a small geographic area. Attempts to predict maize yield have been minimal and the use of artificial intelligence such as the artificial neural network has not been conducted. In this study, alternative sources of climatic and environmental data have been employed using remotely sensed data which offers possibilities of collecting continuous data over a large area (including remote areas) through the use of satellite. The analysis of agro-climatic variables (precipitation, potential evapotranspiration, minimum and maximum temperatures) spanning a period of 1986–2015, over the North West, Free State, Mpumalanga and KwaZulu-Natal (KZN) provinces, indicated that there is a negative trend in precipitation for North West and Free State provinces and positive trend in maximum temperature for all the provinces over the study period. Further more, the result showed that one or more different agro-climatic variables has more influence on maize across the provinces. Analysis of the phenological parameters of maize indicated that climate change and climate variability affect plant phenology largely during the vegetative and reproductive stages. NDVI values exhibited a decreasing trend across the maize producing provinces of South Africa. The results further demonstrate that the influences of climate variables on phenological parameters exhibit a strong space-time and common covariate dependence. Agro-climatic variables can predict about 46% of the variability of phenology indicators and about 63% of the variability of yield indicators for the entire study area. The study also illustrated the spatial patterns of drought depicting drought severity, frequency, and intensity which has the potential to influence crop yield. The study found that maize yield is most sensitive to 3-month timescale coinciding with maize growing season (r = 0.59; p <0.05) affecting maize yield by up to 35% across the study area. In ensuring and fulfilling one of the seventeen sustainable development goals; to eradicate extreme poverty and hunger, the development of a system capable of monitoring and predicting crop yield becomes imperative. Machine learning tools such as the artificial neural network becomes handy and useful to provide a platform that is data intensive and robust to meet the requirements for an effective monitoring and predictive system for crop; particularly maize. The accuracy of the comparison between the actual and predicted maize yield is averaged at about 92% across the study area. The empirical model developed in this study can also be adopted to other grain crops such as Sorghum, wheat, soya beans etc.Thesis (PhD)--University of Pretoria, 2019.EU FP7 AnimalChange project under the grant agreements no. 266018Geography, Geoinformatics and MeteorologyPhDUnrestricte

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others