Using phenology-based enhanced vegetation index and machine learning for soybean yield estimation in Paraná State, Brazil

Providing accurate and spatially resolved predictions of crop yield is of utmost importance due to the rapid increase in the demand of biofuels and food in the foreseeable future. Satellite based remote sensing over agricultural areas allows monitoring crop development through key bio-geophysical variables such as the Enhanced Vegetation Index (EVI), sensitive to canopy greenness, the Vegetation Optical Depth (VOD), sensitive to biomass water-uptake dynamics, and Soil Moisture (SM), which provides direct information of plant available water. The aim of this work is to implement an automatic system for county-based crop yield estimation using time series from multisource satellite observations, meteorological data and available in situ surveys as supporting information. The spatio-temporal resolution of satellite and meteorological observations are fully exploited and synergistically combined for crop yield prediction using machine learning models. Linear and non-linear regression methods are used: least squares, LASSO, random forests, kernel machines and Gaussian processes. Here we are not only interested in the prediction skill, but also on understanding the relative relevance of the covariates. For this, we first study the importance of each feature separately and then propose a global model for operational monitoring of crop status using the most relevant agro-ecological drivers.&#160;We selected the Continental U.S. and a four-year time series dataset to perform the research study. Results reveal that the three satellite variables are complementary and that their combination with maximum temperature and precipitation from meteorological stations provides the best estimations. Interestingly, adding information about crop planted area also improved the predictions. A non-linear regression model based on Gaussian processes led to best results for all considered crops (soybean, corn and wheat), with high accuracy (low bias and correlation coefficients ranging from 0.75 to 0.92). The feature ranking allowed understanding the main drivers for crop monitoring and the underlying factors behind a prediction loss or gain.

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Evaluation of alternative methods to calculate evapotranspiration and their impact on soybean yield estimation

Agrometeoros ◽

10.31062/agrom.v28.e026753 ◽

2020 ◽

Vol 28 ◽

Author(s):

Rodrigo Cornacini Ferreira ◽

Otávio Jorge Grigoli Abi-Saab ◽

Marcelo Augusto de Aguiar e Silva ◽

Rubson Natal Ribeiro Sibaldellib ◽

José Renato Bouças Farias

Keyword(s):

Alternative Methods ◽

Yield Estimation ◽

Soybean Yield

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Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System

Remote Sensing ◽

10.3390/rs13030401 ◽

2021 ◽

Vol 13 (3) ◽

pp. 401

Author(s):

Cadan Cummings ◽

Yuxin Miao ◽

Gabriel Dias Paiao ◽

Shujiang Kang ◽

Fabián G. Fernández

Keyword(s):

Machine Learning ◽

Chlorophyll Content ◽

Vegetation Index ◽

Soil Drainage ◽

Management Information ◽

Area Index ◽

Sensing System ◽

Extreme Gradient Boosting ◽

Split Plot ◽

N Status

Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha−1 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.

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Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

Remote Sensing ◽

10.3390/rs13050907 ◽

2021 ◽

Vol 13 (5) ◽

pp. 907

Author(s):

Theodora Lendzioch ◽

Jakub Langhammer ◽

Lukáš Vlček ◽

Robert Minařík

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Spatial Data ◽

Groundwater Level ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Sampling Strategy ◽

Peat Bog ◽

Strong Impact ◽

Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

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Prediction of Maize Yield at the City Level in China Using Multi-Source Data

Remote Sensing ◽

10.3390/rs13010146 ◽

2021 ◽

Vol 13 (1) ◽

pp. 146

Author(s):

Xinxin Chen ◽

Lan Feng ◽

Rui Yao ◽

Xiaojun Wu ◽

Jia Sun ◽

...

Keyword(s):

Machine Learning ◽

Satellite Data ◽

Vegetation Index ◽

Maize Yield ◽

Machine Learning Algorithms ◽

Shortwave Radiation ◽

Growth Stages ◽

Climate Data ◽

Early Peak ◽

The City

Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

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A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

Sensors ◽

10.3390/s19224893 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4893 ◽

Cited By ~ 22

Author(s):

Hejar Shahabi ◽

Ben Jarihani ◽

Sepideh Tavakkoli Piralilou ◽

David Chittleborough ◽

Mohammadtaghi Avand ◽

...

Keyword(s):

Machine Learning ◽

Image Segmentation ◽

Vegetation Index ◽

Scale Parameter ◽

Slope Aspect ◽

Support Vector ◽

Topographic Wetness Index ◽

Slope Length ◽

Optimal Scale ◽

Object Based

Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

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Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

10.5194/egusphere-egu21-15589 ◽

2021 ◽

Author(s):

Brianna Pagán ◽

Adekunle Ajayi ◽

Mamadou Krouma ◽

Jyotsna Budideti ◽

Omar Tafsi

Keyword(s):

Machine Learning ◽

Satellite Imagery ◽

Cloud Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Learning Approaches ◽

Area Index ◽

Spatial And Temporal Scales ◽

Field Level ◽

Crop Health

The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level.&#160;Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.&#160;

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Enhanced Vegetation Index (EVI) na análise da dinâmica da vegetação da reserva biológica de Sooretama, ES

Revista Árvore ◽

10.1590/s0100-67622008000600015 ◽

2008 ◽

Vol 32 (6) ◽

pp. 1099-1107 ◽

Cited By ~ 3

Author(s):

André Quintão de Almeida ◽

Gilson Fernandes da Silva ◽

José Eduardo Macedo Pezzopane ◽

Carlos Alexandre Damasceno Ribeiro

Keyword(s):

Vegetation Index ◽

Enhanced Vegetation Index ◽

Espírito Santo ◽

Espirito Santo

Técnicas de análises de séries temporais são utilizadas para caracterizar o comportamento de fenômenos naturais no domínio do tempo. Neste artigo, segundo a metodologia proposta por Box et al. (1994), 125 observações do Enhanced Vegetation Index (EVI) foram analisadas. Os valores modelados correspondem às variações temporais ocorridas no dossel florestal da reserva biológica de Sooretama, localizada ao Norte do Estado do Espírito Santo, no Município de Linhares. Os resultados indicaram que a metodologia foi adequada. Os resíduos do modelo ajustado são não correlacionados com distribuição normal, média zero e variância s². Com o menor valor do Critério de Informação de Akaike (AIC) -570,51, o modelo ajustado foi o Sazonal Auto-Regressivo Integrado de Médias Móveis (1,0,1)(1,0,1)12.

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Improving Biomass and Grain Yield Prediction of Wheat GenoTypes on Sodic Soil Using Integrated High-Resolution MultiSpectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

Remote Sensing ◽

10.3390/rs13173482 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3482

Author(s):

Malini Roy Choudhury ◽

Sumanta Das ◽

Jack Christopher ◽

Armando Apan ◽

Scott Chapman ◽

...

Keyword(s):

Machine Learning ◽

Grain Yield ◽

Point Cloud ◽

Vegetation Index ◽

3D Point Cloud ◽

Sodic Soil ◽

Proximal Sensing ◽

Crop Height ◽

Wheat Genotypes ◽

Grain Yields

Sodic soils adversely affect crop production over extensive areas of rain-fed cropping worldwide, with particularly large areas in Australia. Crop phenotyping may assist in identifying cultivars tolerant to soil sodicity. However, studies to identify the most appropriate traits and reliable tools to assist crop phenotyping on sodic soil are limited. Hence, this study evaluated the ability of multispectral, hyperspectral, 3D point cloud, and machine learning techniques to improve estimation of biomass and grain yield of wheat genotypes grown on a moderately sodic (MS) and highly sodic (HS) soil sites in northeastern Australia. While a number of studies have reported using different remote sensing approaches and crop traits to quantify crop growth, stress, and yield variation, studies are limited using the combination of these techniques including machine learning to improve estimation of genotypic biomass and yield, especially in constrained sodic soil environments. At close to flowering, unmanned aerial vehicle (UAV) and ground-based proximal sensing was used to obtain remote and/or proximal sensing data, while biomass yield and crop heights were also manually measured in the field. Grain yield was machine-harvested at maturity. UAV remote and/or proximal sensing-derived spectral vegetation indices (VIs), such as normalized difference vegetation index, optimized soil adjusted vegetation index, and enhanced vegetation index and crop height were closely corresponded to wheat genotypic biomass and grain yields. UAV multispectral VIs more closely associated with biomass and grain yields compared to proximal sensing data. The red-green-blue (RGB) 3D point cloud technique was effective in determining crop height, which was slightly better correlated with genotypic biomass and grain yield than ground-measured crop height data. These remote sensing-derived crop traits (VIs and crop height) and wheat biomass and grain yields were further simulated using machine learning algorithms (multitarget linear regression, support vector machine regression, Gaussian process regression, and artificial neural network) with different kernels to improve estimation of biomass and grain yield. The artificial neural network predicted biomass yield (R2 = 0.89; RMSE = 34.8 g/m2 for the MS and R2 = 0.82; RMSE = 26.4 g/m2 for the HS site) and grain yield (R2 = 0.88; RMSE = 11.8 g/m2 for the MS and R2 = 0.74; RMSE = 16.1 g/m2 for the HS site) with slightly less error than the others. Wheat genotypes Mitch, Corack, Mace, Trojan, Lancer, and Bremer were identified as more tolerant to sodic soil constraints than Emu Rock, Janz, Flanker, and Gladius. The study improves our ability to select appropriate traits and techniques in accurate estimation of wheat genotypic biomass and grain yields on sodic soils. This will also assist farmers in identifying cultivars tolerant to sodic soil constraints.

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