scholarly journals Active Sensor Algorithm Approach to Optimize Nitrogen Rate Fertilization in Cotton Production

2021 ◽  
Author(s):  
Jonnas De Marchi ◽  
Ziany Brandao ◽  
Thiago Machado ◽  
Luciano Shiratsuchi
Keyword(s):  
Crop Production ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Variable Rate ◽  
Cotton Production ◽  
Virtual Reference ◽  
Mato Grosso ◽  
Active Sensor ◽  
Experimental Area ◽  
N Rates

Variable nitrogen(N) rate fertilization based on remote sensing is challenging for cotton production fields, but active crop canopy sensors (ACS) appear as an alternative to make this practical on farm since they can be used at night as well. The crop spatial variability in inherent in crop production in general, and not on-the-go solutions can be used with this type of active sensing technologies. Thus, the purpose of this study was to investigate the potential of two vegetation indices to identify the N requirement variability for cotton plants and to develop prototype algorithms for topdressing nitrogen variable rate on commercial and experimental areas, using the N-sufficiency methodology based on virtual reference. The concept of virtual reference is to use a histogram to characterize the vegetation index of properly fertilized plants without establishing an N-rich plot as a reference strip. The experiment was conducted in strips with four different N rates (0, 45, 90 and 180 kgN ha-1) during the 2015, 2016, 2017 and 2018 crop seasons in partnership with large cotton producers in Mato Grosso and also in experimental area of Embrapa Agrosilvopastoral. Two algorithms for variable rate nitrogen fertilization for cotton were developed, namely: 1) N recommendation algorithm for cotton in commercial production system: N rate (kg.N ha-1) = -234.79 ISN2 + 49,879 ISN + 195.15; R² = 0.97; and 2) for cotton grown in experimental area: N dose (kgN ha-1) = -174.73 ISN2 - 107.21 ISN + 306.78; R² = 0.94.

scholarly journals Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil

2012 ◽  
Vol 47 (9) ◽  
pp. 1270-1278 ◽  
Author(s):  
Daniel de Castro Victoria ◽  
Adriano Rolim da Paz ◽  
Alexandre Camargo Coutinho ◽  
Jude Kastens ◽  
J. Christopher Brown
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Vegetation Index ◽  
The State ◽  
Mato Grosso ◽  
Cropland Area ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Production Areas

The objective of this work was to evaluate a simple, semi‑automated methodology for mapping cropland areas in the state of Mato Grosso, Brazil. A Fourier transform was applied over a time series of vegetation index products from the moderate resolution imaging spectroradiometer (Modis) sensor. This procedure allows for the evaluation of the amplitude of the periodic changes in vegetation response through time and the identification of areas with strong seasonal variation related to crop production. Annual cropland masks from 2006 to 2009 were generated and municipal cropland areas were estimated through remote sensing. We observed good agreement with official statistics on planted area, especially for municipalities with more than 10% of cropland cover (R² = 0.89), but poor agreement in municipalities with less than 5% crop cover (R² = 0.41). The assessed methodology can be used for annual cropland mapping over large production areas in Brazil.

Multitemporal Analysis of Degraded Forests in the Southern Brazilian Amazon

2005 ◽  
Vol 9 (19) ◽  
pp. 1-25 ◽  
Author(s):  
Carlos M. Souza ◽  
Dar A. Roberts ◽  
AndréL. Monteiro
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Effective Means ◽  
Vegetation Indices ◽  
Brazilian Amazon ◽  
Thematic Mapper ◽  
Mato Grosso ◽  
Multitemporal Analysis ◽  
Fraction Images ◽  
Burned Forest

Abstract In this study, statistical multitemporal analysis was applied to evaluate the capability of reflectance, vegetation indices [normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI)], normalized difference infrared indices (NDII5 and NDII7), and fraction images, derived from spectral mixture analysis (SMA), to distinguish intact forest from four classes of degraded forests: nonmechanized logging, managed logging, conventional logging, and logged and burned. For this purpose, a robust time series dataset of Landsat Thematic Mapper 5/Enhanced Thematic Mapper (TM/ETM+) images was used in conjunction with forest inventory transects and data on disturbance history. The study area is located near two important sawmill centers—Sinop and Claúdia, in Mato Grosso State—in the southern Brazilian Amazon. Most of the remote sensing measures tested to distinguish intact forest from degraded forests showed statistically significant changes. Fraction images, particularly green vegetation (GV) and nonphotosynthetic vegetation (NPV), were the most effective means tested for identifying conventional logging and logged and burned forest in the region. The GV change, detected from intact forest to conventional logging and logged and burned forest classes, persists no more than 1 yr, but the NPV change is still significantly different for up to 2 yr. In the second and third years following a degradation event, a significant regeneration change signal was observed in reflectance and fraction images, which can be useful for identifying these types of forest disturbances in areas where optical satellite images cannot be acquired every year.

scholarly journals Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil

2018 ◽  
Vol 10 (9) ◽  
pp. 1322 ◽  
Author(s):  
Víctor Manabe ◽  
Marcio Melo ◽  
Jansle Rocha
Keyword(s):  
Land Use ◽  
Crop Production ◽  
Vegetation Index ◽  
Phase 1 ◽  
Plant Diseases ◽  
Mato Grosso ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Dynamic Time

Integrated crop-livestock (ICL) systems combine livestock and crop production in the same area, increasing the efficiency of land use and machinery, while mitigating greenhouse gas emissions, and reducing production risks, plant diseases and pests. ICL systems are primarily divided into annual (ICLa) and multi-annual (ICLm) systems. Projects such as the “Integrated crop-livestock-forest Network” and the “Livestock Rally” have estimated the ICL areas for Brazil on a state or regional basis. However, it remains necessary to create methods for spatial identification of ICL areas. Thus, we developed a framework for mapping ICL areas in Mato Grosso, Brazil using the Enhanced Vegetation Index time-series of Moderate Resolution Imaging Spectroradiometer and a Time-Weighted Dynamic Time Warping (TWDTW) classification method. The classification of ICL areas occurred in three phases. Phase 1 corresponded to the classification of land use from 2008 to 2016. In Phase 2, the ICLa areas were identified. Finally, Phase 3 corresponded to the ICLm identification. The framework showed overall accuracies of 86% and 92% for ICL areas. ICLm accounted for 87% of the ICL areas. Considering only agricultural areas or only pasture areas, ICL systems represented 5% and 15%, respectively.

scholarly journals MAIZE YIELD ESTIMATION IN KENYA USING MODIS

2020 ◽  
Vol V-3-2020 ◽  
pp. 477-482
Author(s):  
B. K. Kenduiywo ◽  
A. Ghosh ◽  
R. Hijmans ◽  
L. Ndungu
Keyword(s):  
Crop Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Research ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Maize Yield ◽  
Percentage Error ◽  
Support Vector ◽  
County Level

Abstract. Monitoring staple crop production can support agricultural research, business such as crop insurance, and government policy. Obtaining accurate estimates through field work is very expensive, and estimating it through remote sensing is promising. We estimated county-level maize yield for the 37 maize producing countries in Kenya from 2010 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Support Vector Regression (SVR) and Random Forest (RF) were used to fit models with observed county level maize yield as a function of vegetation indices. The following five MODIS vegetation indices were used: green normalized difference vegetation index, normalized difference vegetation index, normalized difference moisture index, gross primary production, and fraction of photosynthetically active radiation. The models were evaluated with 5-fold leave one year out cross-validation. For SVR, R2 was 0.70, the Root Mean Square Error (RMSE) was 0.50 MT/ha and Mean Absolute Percentage Error (MAPE) was 27.6%. On the other hand for RF these were 0.69, 0.51 MT/ha and 29.3% respectively. These results are promising and should be tested in specific applications to understand if they are good enough for use.

scholarly journals Using Solar-Induced Chlorophyll Fluorescence Observed by OCO-2 to Predict Autumn Crop Production in China

2019 ◽  
Vol 11 (14) ◽  
pp. 1715 ◽  
Author(s):  
Jin Wei ◽  
Xuguang Tang ◽  
Qing Gu ◽  
Min Wang ◽  
Mingguo Ma ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Crop Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Close Correlation ◽  
Estimation Accuracy ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

The remote sensing of solar-induced chlorophyll fluorescence (SIF) has attracted considerable attention as a new monitor of vegetation photosynthesis. Previous studies have revealed the close correlation between SIF and terrestrial gross primary productivity (GPP), and have used SIF to estimate vegetation GPP. This study investigated the relationship between the Orbiting Carbon Observatory-2 (OCO-2) SIF products at two retrieval bands (SIF757, SIF771) and the autumn crop production in China during the summer of 2015 on different timescales. Subsequently, we evaluated the performance to estimate the autumn crop production of 2016 by using the optimal model developed in 2015. In addition, the OCO-2 SIF was compared with the moderate resolution imaging spectroradiometer (MODIS) vegetation indices (VIs) (normalized difference vegetation index, NDVI; enhanced vegetation index, EVI) for predicting the crop production. All the remotely sensed products exhibited the strongest correlation with autumn crop production in July. The OCO-2 SIF757 estimated autumn crop production best (R2 = 0.678, p < 0.01; RMSE = 748.901 ten kilotons; MAE = 567.629 ten kilotons). SIF monitored the crop dynamics better than VIs, although the performances of VIs were similar to SIF. The estimation accuracy was limited by the spatial resolution and discreteness of the OCO-2 SIF products. Our findings demonstrate that SIF is a feasible approach for the crop production estimation and is not inferior to VIs, and suggest that accurate autumn crop production forecasts while using the SIF-based model can be obtained one to two months before the harvest. Furthermore, the proposed method can be widely applied with the development of satellite-based SIF observation technology.

scholarly journals Feature Fusion Approach for Temporal Land Use Mapping in Complex Agricultural Areas

2021 ◽  
Vol 13 (13) ◽  
pp. 2517
Author(s):  
Lijun Wang ◽  
Jiayao Wang ◽  
Fen Qin
Keyword(s):  
Land Use ◽  
Time Series ◽  
Crop Production ◽  
Large Scale ◽  
Vegetation Index ◽  
Feature Fusion ◽  
Vegetation Indices ◽  
Google Earth ◽  
Classification Rule ◽  
Cultivated Land

Accurate temporal land use mapping provides important and timely information for decision making for large-scale management of land and crop production. At present, temporal land cover and crop classifications within a study area have neglected the differences between subregions. In this paper, we propose a classification rule by integrating the terrain, time series characteristics, priority, and seasonality (TTPSR) with Sentinel-2 satellite imagery. Based on the time series of Normalized Difference Water Index (NDWI) and Vegetation Index (NDVI), a dynamic decision tree for forests, cultivation, urban, and water was created in Google Earth Engine (GEE) for each subregion to extract cultivated land. Then, with or without this cultivated land mask data, the original classification results for each subregion were completed based on composite image acquisition with five vegetation indices using Random Forest. During the post-reclassification process, a 4-bit coding rule based on terrain, type, seasonal rhythm, and priority was generated by analyzing the characteristics of the original results. Finally, statistical results and temporal mapping were processed. The results showed that feature importance was dominated by B2, NDWI, RENDVI, B11, and B12 over winter, and B11, B12, NDBI, B2, and B8A over summer. Meanwhile, the cultivated land mask improved the overall accuracy for multicategories (seven to eight and nine to 13 during winter and summer, respectively) in each subregion, with average ranges in the overall accuracy for winter and summer of 0.857–0.935 and 0.873–0.963, respectively, and kappa coefficients of 0.803–0.902 and 0.835–0.950, respectively. The analysis of the above results and the comparison with resampling plots identified various sources of error for classification accuracy, including spectral differences, degree of field fragmentation, and planting complexity. The results demonstrated the capability of the TTPSR rule in temporal land use mapping, especially with regard to complex crops classification and automated post-processing, thereby providing a viable option for large-scale land use mapping.

scholarly journals Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 652
Author(s):  
Attila Nagy ◽  
Andrea Szabó ◽  
Odunayo David Adeniyi ◽  
János Tamás
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Crop Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Landsat 8 ◽  
Tisza River ◽  
Yield Forecasting

Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013–2018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018–2019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41–71) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.

Characterising soil moisture regimes on poorly drained soils in Ireland using optical satellite derived vegetation indices and the OPTRAM model

2021 ◽  
Author(s):  
Rumia Basu ◽  
Colin Brown ◽  
Patrick Tuohy ◽  
Eve Daly
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Crop Production ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Accurate Information ◽  
Effective Manner ◽  
Normalised Difference Vegetation Index ◽  
Drainage Capacity ◽  
Poorly Drained Soils

&lt;p&gt;Soil drainage capacity is the degree and frequency at which the soil is free of saturation. It influences land use and management, soil nutrient cycling and greenhouse gas fluxes. Accurate information on drainage conditions is crucial for crop production and management and fundamental in developing strategies to adhere to environmental sustainability goals. This is particularly important in Ireland where approximately 50% of the soils are classified as &amp;#8220;marginal&amp;#8221;. These are mainly poorly drained soils which negatively impact plant growth and productivity.&lt;/p&gt;&lt;p&gt;Soil moisture acts as a proxy for drainage capacity. Timely and accurate information on soil moisture allows for precision management strategies. It aids in designing effective interventions on farms for artificial drainage works which are often assessed by information on soil moisture, soil type and hydrology. Such data are conventionally acquired by in-situ point sampling techniques which are costly and time consuming. Remote sensing has the potential to provide a solution by allowing simultaneous coverage of large geographic areas, quickly and in a cost effective manner.&lt;/p&gt;&lt;p&gt;This study uses optical remote sensing data from Sentinel 2 to derive information on soil moisture conditions on selected sites in Ireland.&amp;#160; We develop the OPTRAM model of Sadeghi et al (2017) by exploring the use of remote sensing based vegetation indices such as the Normalised Difference Vegetation index, Enhanced Vegetation Index and Normalised Difference Red Edge Index for the years 2015-2020 along with short wave transformed infrared reflectance to estimate soil moisture variations for our study areas. We show that &amp;#160;non-linear estimates of the wet and dry edge curves in the model are better suited for Ireland, which is dominated by wet conditions for most of the year and also identify the best vegetation indices for studying soil moisture variations.&lt;/p&gt;

scholarly journals Effectiveness of Common Preprocessing Methods of Time Series for Monitoring Crop Distribution in Kenya

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 79
Author(s):  
Rui Ni ◽  
Xiaohui Zhu ◽  
Yuping Lei ◽  
Xiaoxin Li ◽  
Wenxu Dong ◽  
...  
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Vegetation Index ◽  
Negative Impact ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Sub Saharan Africa ◽  
Support Vector ◽  
Rainfall Time Series ◽  
Phenological Data

Accurate crop identification and spatial distribution mapping are important for crop production estimation and famine early warning, especially for food-deficit African agricultural countries. By evaluating existing preprocessing methods for classification using satellite image time series (SITS) in Kenya, this study aimed to provide a low-cost method for cultivated land monitoring in sub-Saharan Africa that lacks financial support. SITS were composed of a set of MODIS Vegetation Indices (MOD13Q1) in 2018, and the classification method included the Support Vector Machine (SVM) and Random Forest (RF) classifier. Eight datasets obtained at three levels of preprocessing from MOD13Q1 were used in the classification: (1) raw SITS of vegetation indices (R-NDVI, R-EVI, and R-NDVI + R-EVI); (2) smoothed SITS of vegetation indices (S-NDVI); and (3) vegetation phenological data (P-NDVI, P-EVI, R-NDVI + P-NDVI, and P-NDVI-1). Both SVM and RF classification results showed that the “R-NDVI + R-EVI” dataset achieved the highest performance, while the three pure phenological datasets produced the lowest accuracy. Correlation analysis between variable importance and rainfall time series demonstrated that the vegetation index SITS during rainfall periods showed higher importance in RF classifiers, thus revealing the potential of saving computational costs. Considering the preprocessing cost of SITS and its negative impact on the classification accuracy, we recommend overlaying the original NDVI with the original EVI time series to map the crop distribution in Kenya.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

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