Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

Applications of Remote Sensing in Precision Agriculture: A Review

. NASA deployed the GeoTASO airborne UV–visible
spectrometer in May–June 2017 to produce high-resolution (approximately 250 m×250 m ) gapless NO2 datasets over the western shore of Lake Michigan and over the Los Angeles Basin. The results collected show that the airborne tropospheric vertical column retrievals compare well with
ground-based Pandora spectrometer column NO2 observations ( r2=0.91 and slope of 1.03). Apparent disagreements between the two measurements can be sensitive to the coincidence criteria and are often
associated with large local variability, including rapid temporal changes
and spatial heterogeneity that may be observed differently by the sunward-viewing Pandora observations. The gapless mapping strategy executed during
the 2017 GeoTASO flights provides data suitable for averaging to coarser
areal resolutions to simulate satellite retrievals. As simulated satellite
pixel area increases to values typical of TEMPO (Tropospheric Emissions: Monitoring Pollution), TROPOMI (TROPOspheric Monitoring Instrument), and OMI (Ozone Monitoring Instrument), the
agreement with Pandora measurements degraded, particularly for the most
polluted columns as localized large pollution enhancements observed by
Pandora and GeoTASO are spatially averaged with nearby less-polluted
locations within the larger area representative of the satellite spatial
resolutions (aircraft-to-Pandora slope: TEMPO scale  =0.88 ; TROPOMI
scale  =0.77 ; OMI scale  =0.57 ). In these two regions, Pandora and TEMPO or TROPOMI have the potential to compare well at least up to pollution scales of 30×1015  molecules cm −2 . Two publicly available OMI tropospheric NO2 retrievals are found to be biased low with respect to these Pandora observations. However, the agreement improves when higher-resolution a priori inputs are used for the tropospheric air mass factor calculation (NASA V3 standard product slope  =0.18 and Berkeley High Resolution product slope  =0.30 ). Overall, this work explores best practices for satellite validation strategies with Pandora direct-sun observations by showing the sensitivity to product spatial resolution and demonstrating how the high-spatial-resolution NO2 data retrieved from airborne spectrometers, such as GeoTASO, can be used with high-temporal-resolution ground-based column observations to evaluate the influence of spatial heterogeneity on validation results.

/pdf/evaluating-the-impact-of-spatial-resolution-on-tropospheric-5dhebf3jmu.pdf

Evaluating the impact of spatial resolution on tropospheric NO 2 column comparisons within urban areas using high-resolution airborne data

Crop type related information is very essential for various planning and decision support activities in everyday life especially for early forecast and monitoring of food production. Though smallholder farming areas are profound food producers, mapping crop types is mainly constrained by their inherent characteristics like fragmentation (small farm size), rugged terrain, and presence of thick clouds in the growing season. More importantly, crops mixed dominance of the landscape coupled with fragmented holdings, crops behave different phenological characteristics which mostly constrains conventional mapping techniques for crop type mapping. Therefore, the main objective of this study was mapping crop types using all-weather time-series Synthetic Aperture Radar (SAR) with time-weighted Dynamic Time Warping that accounts for phenological development of crops. The study has used Sentinel-1 dual polarimetry (VV, VH) and TerraSAR-X single polarimetry (HH) images. Basic Registration of Crop Plots (BRP) dataset was used as a reference for training and validation. Obtained imagery was passed through a series of pre-processing operations. As Sentinel-1 imagery has dual polarimetry bands, derived features (Ratio, Modified Radar Vegetation Index, and Dual Polarimetric Soil Vegetation Index) were computed. Additionally, polarimetric decomposition was also undertaken. Within stated broader objective, a detailed analysis was done to know crop-specific responses for incident radar signal, to understand the capability of Time-Weighted Dynamic Time Warping for crop type mapping, implications of using either only backscatter bands and inclusion of derived features and decomposed polarimetric features on mapping accuracy of crops. In addition to these, under broader dynamic time warping, two further model improvement strategies (Variable Time Weight Dynamic Time Warping and Angular Metric for Shape Similarity) were also tested for performance. More importantly, the study has investigated an ensemble classifier that integrates TerraSAR-X and Sentinel-1 classification outputs for synergistic use of both sensing systems for crop type mapping. From these analyses, the study has come up with promising findings that show potentials of SAR imagery with time-weighted Dynamic Time Warping for crop type mapping. It has also clearly demonstrated predictive capabilities of either using dual polarimetry or single polarimetry SAR datasets for mapping crops in smallholder farming areas. Finally, by considering achieved outputs and existing caveats on this study, to refine the findings, further works were also recommended.

Mapping crop types in complex farming areas using SAR imagery with dynamic time warping

Remote sensing techniques can be efficient for non-destructive, rapid detection of wheat nitrogen (N) nutrient status. In the paper, we examined the relationships of canopy multi-angular data with aerial N uptake of winter wheat (Triticum aestivum L.) across different growing seasons, locations, years, wheat varieties and N application rates. Seventeen vegetation indices (VIs) selected from the literature were measured for the stability in estimating aerial N uptake of wheat under 13 view zenith angles (VZAs) in the solar principal plane (SPP). In total, the back-scatter angles showed better VI behavior than the forward-scatter angles. The correlation coefficient of VIs with aerial N uptake increased with decreasing VZAs. The best linear relationship was integrated with the optimized common indices DIDA and DDn to examine dynamic changes in aerial N uptake; this led to coefficients of determination (R2) of 0.769 and 0.760 at the -10° viewing angle. Our novel area index, designed the modified right-side peak area index (mRPA), was developed in accordance with exploration of the spectral area calculation and red-edge feature using the equation: mRPA = (R760/R600)1/2×(R760-R718). Investigating the predictive accuracy of mRPA for aerial N uptake across VZAs demonstrated that the best performance was at -10° (R2 = 0.804, p < 0.001, root mean square error (RMSE) = 3.615) and that the effect was relatively similar between -20° to +10° (R2 = 0.782, p < 0.001, RMSE = 3.805). This leads us to construct a simple model under wide-angle combinations so as to improve the field operation simplicity and applicability. Fitting independent datasets to the models resulted in relative error (RE, %) values of 12.6%, 14.1%, and 14.9% between estimated and measured aerial N uptake for mRPA, DIDA, and DDn across the range of -20° to +10°, respectively, further confirming the superior test performance of the mRPA index. These results illustrate that the novel index mRPA represents a more accurate assessment of plant N status, which is beneficial for guiding N management in winter wheat.

/pdf/remotely-estimating-aerial-n-uptake-in-winter-wheat-using-4gosg4ns6h.pdf

Remotely Estimating Aerial N Uptake in Winter Wheat Using Red-Edge Area Index From Multi-Angular Hyperspectral Data

Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacas area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs' saturation in the Apiacas area (i.e., X = -0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = -0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

https://www.mdpi.com/1424-8220/21/6/2115/pdf

Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas.

Accurate and spatially explicit cropland maps are crucial for many applications, which include sustainable crop monitoring, food security, and land and agriculture planning and management. Zimbabwe lacks reliable data on cropland extent of the old and new re-allocated areas for inventory purposes. Objectives of this paper are to map cropland utilizing: 1) automatic classification; 2) multi-classifier system (MCS); and 3) normalized difference vegetation index and bare-soil index (NDVI-BSI) thresholding and determine the spatiotemporal cropland changes. Change detection is implemented through a post-classification statistical method. The classified results are compared with SADC and ESA land cover products, GFSAD30AFCE cropland layer, and Google Earth imagery. Results reveal that MCS and NDVI-BSI performed the best and achieved overall accuracies of 80.54% and 79.32% for 2013, and for 2018, they attained accuracies of 87.90% and 88.56%, respectively. Automated classification, MCS, and NDVI-BSI thresholding produced average cropland areas of 3416396, 10346778, and 9788833 Ha, respectively. Visual assessment observations show that NDVI-BSI thresholding outperformed the other two techniques. Comparing further the MCS and NDVI-BSI thresholding approaches’ results of total cropland areas of Zimbabwe’s ten provinces for the years 2013 and 2018, coefficients of determination of 0.8404 and 0.9619, respectively, are achieved. Change detection shows a general increase in the cropland area due to human activities despite the prolonged drought. However, we recommend further exploration of NDVI-BSI threshold values to derive cropland layers since the method is robust and can be automated easily and faster without inputting training data. We also recommend simulation of the changes in cropland areas using cellular automata and/or agent-based modeling.

/pdf/cropland-mapping-and-change-detection-toward-zimbabwean-4et95yn647.pdf

Cropland Mapping and Change Detection: Toward Zimbabwean Cropland Inventory

Soil-Adjusted Vegetation Index (SAVI) is found to be undesirable to estimate Leaf Area Index (LAI) with heterogeneous canopy structure in low vegetation cover. In this article, three new vegetation indices (VIs), such as Normalized Hotspot-Signature Vegetation Index 2 (NHVI2), Hotspot-Signature Soil-Adjusted Vegetation Index (HSVI), and Hotspot-Signature 2-Band Enhanced Vegetation Index (HEVI2), are proposed for a better quantitative estimation of LAI and soil-noise resistance than with SAVI. To obtain these new indices, the angular index called Normalized Difference between Hotspot and Darkspot (NDHD) is introduced which represents the distribution of foliage in vegetation canopy. The validity of new VIs is statistically verified using simulated data and field measurements. The Discrete Anisotropic Radiative Transfer (DART) model is used to simulate both the homogeneous and heterogeneous canopy for analyzing vegetation isolines behaviors, soil-noise resistance, and LAI estimation. In situ measurements of LAI and bidirectional reflectance factor from the Boreal Ecosystem-Atmosphere Study (BOREAS) are also used to test the robustness of the new VIs for the estimation of LAI. By considering the distribution of the foliage, the accuracy of LAI estimation of SAVI for heterogeneous canopy improved almost 16% using exponential regression analysis. With the improvement of multiangular remote-sensing and Bidirectional Reflectance Distribution Function (BRDF) models in the future, hotspot-signature VIs have the potential to provide a more accurate LAI estimation for heterogeneous canopy in strong soil-noise interference area.

Potentials and Limits of Vegetation Indices With BRDF Signatures for Soil-Noise Resistance and Estimation of Leaf Area Index

. EOS/MODIS land surface Bi-directional Reflectance Distribution Function (BRDF) product (MCD43), with the latest version C6, is one of the most important operational BRDF products with global coverage. The core sub-product MCD43A1 stores 3 parameters of the RossThick-LiSparseR semi-empirical kernel-driven BRDF model. It is important for confident use of the product to evaluate the accuracy of bi-directional reflectance factor (BRF) predicted by MCD43A1 BRDF model (mBRF). A typical region in the central part of Northeast Asia is selected as the study area. The performance of MCD43A1 BRDF model is analyzed in various observation geometries and phenological phases, using Multi-angle Imaging SpectroRadiometer (MISR) land-surface reflectance factor product (MILS_BRF) as the reference data. In addition, MODIS products MCD12Q1 and MOD/MYD10A1 are used to evaluate the impacts of land cover types and snow covers on the model accuracy, respectively. The results show an overall excellent performance of MCD43A1 in representing the anisotropic reflectance of land surface, with root mean square error (RMSE) of 0.0262 and correlation coefficient (R) of 0.9537, for all available comparable samples of MILS_BRF and mBRF pairs. The model accuracy varies in different months, which is related to the phenological phases of the study area. The accuracy for pixels labelled as ‘snow’ by MCD43 is obviously low, with RMSE/R of 0.0903/0.8401. Ephemeral snowfall events further decrease the accuracy, with RMSE/R of 0.1001/0.7715. These results provide meaningful information to MCD43 users, especially those, whose study regions are subject to phenological cycles as well as snow cover and change.

/pdf/analysis-of-accuracy-of-modis-brdf-product-mcd43-c6-based-on-5y7svs55kj.pdf

Analysis of accuracy of modis brdf product (mcd43 c6) based on misr land surface brf product – a case study of the central part of northeast asia

The Multi-angle imaging spectroradiometer (MISR) land-surface (LS) bidirectional reflectance factor (BRF) product (MILS_BRF) has unique semi-simultaneous multi-angle sampling and global coverage. However, unlike on-satellite observations, the spatio-temporal characteristics of MILS_BRF data have rarely been explicitly and comprehensively analysed. Results from 5-yr (2011–2015) of MILS_BRF dataset from a typical region in central Northeast Asia as the study area showed that the monthly area coverage as well as MILS_BRF data quantity varies significantly, from the highest in October (99.05%) through median in June/July (78.09%/75.21%) to lowest in January (18.97%), and a large data-vacant area exists in the study area during four consecutive winter months (December through March). The data-vacant area is mainly composed of crop lands and cropland/natural vegetation mosaic. The amount of data within the principal plane (PP) ±30° (nPP) or cross PP ±30° (nCP), varies intra-annually with significant differences from different view zeniths or forward/backward scattering directions. For example, multiple off-nadir cameras have nPP but no nCP data for up to six months (September through February), with the opposite occurring in June and July. This study provides explicit and comprehensive information about the spatio-temporal characteristics of product coverage and observation geometry of MILS_BRF in the study area. Results provide required user reference information for MILS_BRF to evaluate performance of BRDF models or to compare with other satellite-derived BRF or albedo products. Comparing this final product to on-satellite observations, what was found here reveals a new perspective on product spatial coverage and observation geometry for multi-angle remote sensing.

http://egeoscien.neigae.ac.cn/EN/article/downloadArticleFile.do?attachType=PDF&id=10038

Spatio-temporal Characteristics of Area Coverage and Observation Geometry of the MISR Land-surface BRF Product: A Case Study of the Central Part of Northeast Asia

Mike Murefu

Papers

Cropland Mapping and Change Detection: Toward Zimbabwean Cropland Inventory

Potentials and Limits of Vegetation Indices With BRDF Signatures for Soil-Noise Resistance and Estimation of Leaf Area Index

Analysis of accuracy of modis brdf product (mcd43 c6) based on misr land surface brf product – a case study of the central part of northeast asia

Spatio-temporal Characteristics of Area Coverage and Observation Geometry of the MISR Land-surface BRF Product: A Case Study of the Central Part of Northeast Asia