Normalized Difference Tillage Index Research Articles

An improvement of the Revised Wind Erosion Equation by considering the effect of non-photosynthetic vegetation

Non-photosynthetic vegetation (NPV) can significantly impact the magnitude of wind erosion. However, most wind erosion models did not take NPV into account, which led to large uncertainties in wind erosion simulation. To reduce these uncertainties, the effects of NPV on wind erosion should be considered in wind erosion simulations. Herein, we collected the hyperspectral and fractional coverage (fNPV) data of NPV from the Mu Us Sandy Land (MUSL). Through constructing a model between the normalized difference tillage index (NDTI) and fNPV, the fNPV values in the MUSL from 2014 to 2017 were estimated by Landsat 8-OLI images and were used to improve the combined vegetation factor (COG) in the Revised Wind Erosion Equation (RWEQ) model to improve this model. Then, the improved RWEQ model was employed to simulate the wind erosion process of the MUSL during these years. The results showed that the mean values of the fNPV in the MUSL from 2014 to 2017 were approximately 2.71 times higher than those estimated by NDVI data (MOD13Q1). The improved RWEQ that considering the NPV significantly improved the precision of the simulation results, as validated by measured data. Compared with the wind erosion modulus (WEM) without NPV, the decreased values caused by NPV were 130.48 t/km2/a (annum), 91.79 t/km2/a, 85.51 t/km2/a and 93.76 t/km2/a from 2014 to 2017, respectively, and the rates of decrease in wind erosion in the corresponding year were 26.52 %, 16.9 %, 21.47 % and 31.33 %, respectively. We believe that integrating NPV monitoring technology into wind erosion models could significantly improve the accuracy of wind erosion simulation, and this study provides new insight into wind erosion modelling, which would be of interest to scholars in the fields of wind erosion and dust emission.

Plastic-covered greenhouses mapping in Morocco with Google Earth engine: Comparing Sentinel-2 and Landsat-8 data using pixel- and object-based methods

With the ever-increasing demand for food production, monitoring plastic-covered greenhouse (PCG) spatiotemporal distribution accurately holds significant importance for monitoring agricultural expansion and environmental impacts. For this purpose, in this paper, we used a random forest (RF) algorithm to produce multi-temporal PCGs maps in three different intensive agricultural Moroccan areas (Loukkos (LS), Gharb (GB), and Souss-Mass (SM)). Two classification approaches, object-based (OB) and pixel-based (PB), were applied to Landsat-8 (L8) and Sentinel-2 (S2) imagery via the Google Earth Engine (GEE) platform. Additionally, the RF performance is compared across five feature scenarios, combining spectral bands, spectral indices, and texture information. Further, we estimated the PCGs' spatiotemporal dynamics from 2018 to 2023. Findings indicated that the Blue and SWIR bands, Normalized Difference Tillage Index (NDTI), Plastic-Mulched Landcover Index (PMLI), variance, and sum average texture were the most significant variables that contributed to accuracy in PCGs classification. The 6-year average overall accuracy achieved >93.10% in mapping PCGs. Notably, the band combination “All” (Bands + Indices + Texture) produced high overall accuracy (>94.9%) and F1-score (>91.8%) compared to other feature combinations. Nevertheless, we found considerably improved classification accuracy by combining only spectral indices with texture (OA >94.6%). It was noticed that both OB and PB approaches reveal their similar performance in most cases, with slight differences. Besides, the PCGs estimated area analysis showed that SM area had the largest PCG coverage, approximately three times larger than the other areas, and it exhibited an overall growth of +8.45% between 2018 and 2023. Thus, this research demonstrates the RF and OB-PB's capability to accurately identify PCGs in GEE. In addition, these results can serve as valuable insights for policymakers and agricultural planners to efficiently monitor and manage PCGs expansion in intensive agricultural areas.

Improving maize residue cover estimation with the combined use of optical and SAR remote sensing images

Conservation tillage is an important conservation measure for arable land in modern agricultural production, which plays an essential role in protecting black soil and improving the quality of arable land. The estimation of maize residue cover (MRC) can be used to obtain the spatial distribution characteristics of conservation tillage, which is essential for government departments to promote conservation tillage technology and understand the implementation of it. In this paper the southern part of the Songnen Plain was used as the study area, and Sentinel-2 MSI images and Sentinel-1 SAR images were used as data sources to correlate the spectral indices and radar backscatter coefficients with the field sampling data in the study area. The MRC estimation model of the study area was constructed using the Random Forest (RF) model, the Multiple Linear Stepwise Regression (MLSR) model, and Back Propagation Neural Network (BPNN) model, respectively. The results of the study showed that the correlation coefficients of normalized difference tillage index (NDTI), simple tillage index (STI), normalized difference index (NDI5), NDI7, shortwave infrared normalized difference residue index (SINDRI), normalized difference senescent vegetation index (NDSVI), normalized difference residue index 2 (NDRI2), NDRI3, NDRI4, NDRI5, NDRI6, NDRI7, NDRI8, NDRI9, and MRC in the study area were greater than 0.4, and the correlation coefficients were higher for NDTI and STI, which reached 0.861 and 0.860, respectively. The correlation coefficient between VV and MRC was 0.56 and between VH and MRC was 0.594. We used MLSR, RF, and BPNN methods in combination with Sentinel-2 MSI images and Sentinel-1 SAR images for MRC estimation. The synergistic use of Sentinel-2 MSI images and Sentinel-1 SAR images helped to improve the accuracy of the MRC estimation models and the correlation coefficient R2 of all three models to greater than 0.8. Based on the statistical analysis of remote sensing estimation results, we found that the average value of the MRC of the maize growing areas in Changchun, Siping, and eastern Songyuan in November 2020 was 66%, and 2% of farmland in the study area had a MRC of less than 30%.

Evaluating the Efficacy of Sentinel-2B and Landsat-8 for Estimating and Mapping Wheat Straw Cover in Rice–Wheat Fields

Sustainable agriculture and soil conservation methods are integral to ensuring food safety and mitigating environmental impacts worldwide. However, crop residue/straw serves many vital functions from tillage to harvest, so that quantifying the appropriate amount of Crop Straw Cover (CSC) on the soil surface is crucial for monitoring tillage intensity and crop yield performance. Thus, a novel research study is conducted to develop an innovative approach for accurately estimating and mapping the Wheat Straw Cover (WSC) percentage through two different multispectral satellites (Sentinel-2B MSI and Landsat-8 OLI-TIRS), using remote sensing-based techniques in Changshu County, China. The field measurements were collected from 80 distinct sites and eight images were acquired through both satellites for the analysis process by applying Crop Residue Indices (CRIs). The results indicate that the coefficients of determination (R2) of the Normalized Difference Tillage Index (NDTI) computed by Sentinel-2 and Landsat-8 were 0.80 and 0.70, respectively, and the root-mean-square deviation (RMSD) values were in the range from 6.88 to 12.04% for CRIs for both satellite data. Additionally, the comparative analysis of the developed model revealed that NDTI was R2 = 0.85 and R2 = 0.77, followed by STI, R2 = 0.82 and R2 = 0.80 and NDRI, R2 = 0.69 and R2 = 0.56 for Sentinel-2B and Landsat-8 data, respectively. Hence, the correlation strength of NDTI, STI and NDRI with WSC percentages was markedly superior by using Sentinel-2B spectral data compared to Landsat-8 ones. Moreover, the NDTI of Sentinel-2B data was the most accurate in mapping the WSC percentage in four categories, with an overall accuracy of 86.53% (κ = 0.78), surpassing the other CRI indices. Therefore, these findings suggest that the multispectral imagery of Sentinel-2B bolstered with enhanced temporal and spatial data was superior for precisely estimating and mapping the WSC percentage compared to Landsat-8 data over a large-scale agricultural region.

A new four-stage approach based on normalized vegetation indices for detecting and mapping sugarcane hail damage using multispectral remotely sensed data

Hailstorms have increased in frequency and intensity over the past decade causing substantial losses in agriculture. Since hailstorms often hit a wide area with no detectable pattern, relying on traditional field-based methods to assess crop damage is difficult. The aim of this study was to develop and test a new four-stage normalized vegetation index approach for detecting the severity of hailstorm damage on sugarcane plants in a large estate in south eastern Zimbabwe. The following six spectral indices were computed for the period before and after a hailstorm event to assess the extent of hailstorm damage on sugarcane: Green Chlorophyll Index (GCI); Normalized Difference Vegetation Index (NDVI); Normalized Difference Senescent Vegetation Index (NDSVI); Red Edge Chlorophyll Index (RECI); Normalized Difference Tillage Index (NDTI); and Modified Soil Adjusted Vegetation Index (MSAVI2). Then, the spectral differences were computed for each index separately and the difference maps are reported as delta (Δ) indices. The results of this study show that within one week and even two weeks after a hailstorm, ΔNDTI, ΔNDVI and ΔRECI were consistently able to detect and characterise the severity of sugarcane damage. When used in partial least squares-discriminant analysis (PLS-DA), ΔNDTI performed best in mapping the severity of crop damage throughout the large estate. ΔNDTI was able to discriminate three different levels of sugarcane damage with an overall accuracy of 90% and a Kappa value of 0.85. Combined these results imply that ΔNDTI computed using multi-spectral datasets within a fortnight after a hailstorm is a promising tool for generating reliable information about the severity of sugarcane damage by hailstorms. Such spatially explicit information is useful for creating customised crop insurance packages sensitive to damage incurred by the farmer.

Determination of Urban Areas Using Google Earth Engine and Spectral Indices; Esenyurt Case Study

Identifying impervious surfaces for monitoring urban expansion is important for the sustainable management of land resources and the protection of the environment. Remote sensing provides an important data source for urban land use/land cover mapping, and these data can be analyzed with various techniques for different purposes. If the aim is to extract information easily and rapidly, using spectral indices is the most appropriate solution, and there are many indices created for this purpose. The study carried out on the Google Earth Engine (GEE) platform, Esenyurt, the most populous district of Istanbul, was investigated using Sentinel 2 MSI image, with eight urban spectral indices and three vegetation indices. In addition, classification was made, and the results were evaluated. As a result of the urban index applications, it has been seen that the roofs are more or less mixed with the bare soil areas, and Normalized Difference Tillage Index (NDTI)gives the best results. Accuracy assessment is performed for index results and classification using the same points, and due to the urban area density in the application area, it is determined as 0.95% and 0.95% for NDTI and Normalized Difference Vegetation Index (NDVI), and 97% for classification, respectively. In GEE, a high (-0.79) negative correlation is observed between May mean values and 2007-2022 population data when the NDVI time series was applied to the entire area within the district borders using Landsat 5 and Landsat 8 images between 1990-2022. The rapidly increasing population in the district leads to rapid urbanization, and green areas are disappearing at the same rate.

Mapping Maize Tillage Practices over the Songnen Plain in Northeast China Using GEE Cloud Platform

As the population grows, the development of conservation tillage offers a means of promoting the sustainability of agricultural engineering. Remote sensing images with high spatial and temporal resolutions enable the accurate monitoring of conservation tillage on a broad spatial scale, further promoting conservation tillage research. This paper describes using streamlined time series Sentinel-2 images based on the Google Earth Engine (GEE) cloud platform for mapping maize tillage practices in the Songnen Plain region of Northeast China. Based on the correlation with the normalized difference tillage index (NDTI) and maize residue coverage (MRC) data, the optimal time series and streamlining functions in the GEE cloud platform are determined. Estimates of MRC and the mapping of tillage practices in the Songnen Plain for 2019–2022 are then determined using GEE and a previous model. Geostatistical analysis using ArcGIS is applied to analyze the spatial and temporal distribution characteristics of MRC and conservation tillage over the Songnen Plain. The results show that time series images from 20–30 May achieve an r value of 0.902 and an R2 value of 0.8136 when using the median streamlining function. The mean MRC for the study area in 2022 is 2.3%, and an overall upward trend in conservation tillage is observed (from 0.08% in 2019 to 0.25% in 2022). Our analysis shows that MRC monitoring and conservation tillage mapping can be performed over a broad spatial scale using remote sensing technology based on the GEE cloud platform. Spatial and temporal information on farm practices provides a theoretical basis for agricultural development planning efforts, which can promote sustainable agricultural development.

Investigating the Potential of Crop Discrimination in Early Growing Stage of Change Analysis in Remote Sensing Crop Profiles

Currently, remote sensing crop identification is mostly based on all available images acquired throughout crop growth. However, the available image and data resources in the early growth stage are limited, which makes early crop identification challenging. Different crop types have different phenological characteristics and seasonal rhythm characteristics, and their growth rates are different at different times. Therefore, making full use of crop growth characteristics to augment crop growth difference information at different times is key to early crop identification. In this study, we first calculated the differential features between different periods as new features based on images acquired during the early growth stage. Secondly, multi-temporal difference features of each period were constructed by combination, then a feature optimization method was used to obtain the optimal feature set of all possible combinations in different periods and the early key identification characteristics of different crops, as well as their stage change characteristics, were explored. Finally, the performance of classification and regression tree (Cart), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM) classifiers in recognizing crops in different periods were analyzed. The results show that: (1) There were key differences between different crops, with rice changing significantly in period F, corn changing significantly in periods E, M, L, and H, and soybean changing significantly in periods E, M, N, and H. (2) For the early identification of rice, the land surface water index (LSWI), simple ratio index (SR), B11, and normalized difference tillage index (NDTI) contributed most, while B11, normalized difference red-edge3 (NDRE3), LSWI, the green vegetation index (VIgreen), red-edge spectral index (RESI), and normalized difference red-edge2 (NDRE2) contributed greatly to corn and soybean identification. (3) Rice could be identified as early as 13 May, with PA and UA as high as 95%. Corn and soybeans were identified as early as 7 July, with PA and UA as high as 97% and 94%, respectively. (4) With the addition of more temporal features, recognition accuracy increased. The GBDT and RF performed best in identifying the three crops in the early stage. This study demonstrates the feasibility of using crop growth difference information for early crop recognition, which can provide a new idea for early crop recognition.

Classification of Conservation Tillage Using Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model

In the remote sensing monitoring of conservation tillage, the acquisition of remote sensing data with high spatial and temporal resolution is critical. The current optical remote sensing images cannot realize both temporal and spatial resolution, especially under cloud and rain interference. Thus, this study employs the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to obtain the normalized difference tillage index (NDTI) with both temporal and spatial resolution estimated by Sentinel−2 and MODIS using the Index−then−Blend (IB) and Blend−then−Index (BI) fusion schemes. After comparison, the IB scheme was better than the BI scheme in predicting results and prediction efficiency. The NDTI predicted by ESTARFM and Sentinel−2 on June 12, 2020 was compared. A coefficient of determination R2 of 0.73 and RMSE of 0.000117 was obtained, indicating a high prediction accuracy, which meets the prediction requirements. Based on the predicted ESTARFM NDTI of the study area on May 17, 2021, the maize residue cover (MRC) of the study area was estimated using the previously constructed MRC unary linear regression model. The MRC of the sampling points of the remote sensing images was estimated by verifying the predicted ESTARFM NDTI with the MRC of the sampling points taken in the field extracted by the maximum likelihood classifier, which has a coefficient of determination R2 of 0.78 and RMSE of 0.00676, signifying better prediction results. The proposed method provides considerable data sources for the remote sensing monitoring studies of conservation tillage.

Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera)

The fundamental difficulty in mapping urban areas, especially in semi-arid and arid environments, is the separation of built-up areas from bare lands, owing to their similar spectral characteristics. Accordingly, this study aims to identify the suitable spectral index that can provide high differentiation, between urban areas and bare lands, in semi-arid areas of three cities of the province of Djelfa, namely, Djelfa, Messaad, and Ain Oussera (Algerian central highlands), through a selection of four spectral indices including Urban Index (BUI), Band ratio for built-up area (BRBA), Normalized Difference Tillage Index (NDTI) and Dry Bare-soil Index (DBSI). In order to increase the mapping accuracy of the built-up in studied areas, a multi-index approach has been applied focusing on identifying an adequate combination of spectral indices of remote sensing that provides the highest performance compared to the images of sentinel 2A. The multi-index approach was developed using three spectral indices combinations and was created using a layer stack process. For forming bare land layer stacking data, both NDTI and DBSI indices were used, while the built-up area layer stacking data was made with both BUI and BRBA indices. The main process was carried out on the Cloud Computing Platform based on geospatial data of Google Earth Engine (GEE) and using machine learning classification by the Support Vector Machine (SVM) algorithm, based on imagery from sentinel 2A acquired during the dry season. The results indicated that the thresholds of the built-up areas are difficult to delineate and distinguish from bare land efficiently with a single index. The obtained results also revealed that the use of multi-index including BUI index provided the best results as they showed the highest effects with NDTI index and DBSI index compared to BRBA index, where the overall accuracies of the multi-index (DBSI/ NDTI/ BUI) were 98.7% in Djelfa, 96.5% in Messaad, and 97.87 % in Ain Oussera, and the kappa coefficients were 97.3%, 85.4%, and 95.3% respectively. These results show that this multi-index is effective and reliable and can be considered for use in other areas with similar characteristics.

Satellite-based assessment of hailstorm-affected potato crop for insurance purpose

Assessing the extent of hailstorm-affected crop is one of the thrust areas for quantifying mid-season adversities under crop insurance values chain. This study evaluated the pre- and post-hailstorm responses of potato crop, over different spectral bands and vegetation indices derived from Sentinel-2 data, towards assessment of damage severity classes. The potato crop was mapped using pre-event satellite data with overall accuracy of 88% (k = 0.82). Pair-wise Games–Howell t-test showed significant differences among the post-hailstorm potato severity classes in red, near infrared and short-wave infrared (SWIR) bands and normalized vegetation indices. Percentage change (from pre- to post-event) in band reflectance and vegetation indices showed a better sensitivity in differentiating damage severities. Differential behaviour of SWIR-1 (Band-11) and SWIR-2 (Band-12) was observed within severely affected potato crop under dry and wet soil conditions. Decision matrix based on percentage change in normalized difference vegetation index (ΔNDVI) and normalized difference tillage index (ΔNDVI) could able to capture the damage severity classes with an overall accuracy of 86.7%. Higher proportion of affected area was found to be associated with larger percentage of potato yield reduction based on measured yield data at insurance unit level. The proposed methodological approach could be adopted for operational assessment of the impact of hailstorm events on other crops with required modifications.

Integration of tillage indices and textural features of Sentinel-2A multispectral images for maize residue cover estimation

In this work, Sentinel-2A images were used to estimate maize residue cover (MRC) using tillage indices (TIs) and textural features. A MRC estimation model based on partial least squares regression (PLSR) was constructed using different features and methods (TIs method, textural feature method, and combination method). Results showed that the normalized difference tillage index (NDTI; r:0.854; R2: 0.729) and the simple tillage index (STI;r:0.853; R2: 0.728) had good linear relationships with MRC. Several models involving different combinations of TI and textural feature indicators were examined in regard to their accuracy. The best performing model (r:0.885; R2: 0.783; and RMSE = 10.63%), a combination of seven TIs and eight textural feature indicators, was selected to estimate MRC in the Songnen Plain, Northeast China. Results showed that MRC ranged from 0.05% to 95% over the study area (mean: 47%) and was generally higher in the northern and southern areas than in the central portion of the Songen Plain. From these results, the distribution of conservation tillage practices across the region was determined. These results demonstrate the spatial distribution of MRC can be monitored in a systematic way using remote sensing technologies, and this information can help guide research and outreach efforts for the emergence of more sustainable agricultural production systems.

Estimation of dry vegetation cover and mass from MODIS data: Verification by roughness length and sand saltation threshold

Monitoring of dry vegetation in arid and semi-arid regions is crucial in environmental investigations such as assessments of wind erosion risk, livestock forage, and fire risk. However, there has been little research into estimating the coverage and mass of dry (non-photosynthetic) vegetation. In our study area at Tsogt-Ovoo (TsO) in the northern Gobi Desert, Mongolia, we used MODIS data to compare observed dry vegetation cover (VCd_obs) and mass (Md_obs) with values estimated from the Normalized Difference Vegetation Index (NDVI) and six other potential Short Wavelength InfraRed (SWIR) dry vegetation indices. Of the seven vegetation indices, the Soil Tillage Index (STI) and Normalized Difference Tillage Index (NDTI), both of which are calculated from SWIR bands 6 and 7, provided good estimates of VCd_obs and Md_obs. Our comparisons of observed roughness length (z0) and threshold friction velocity (u*t) for sand saltation at stony and non-stony sites with dry vegetation cover determined from STI verified its suitability for estimating VCd(STI). At both sites, the observed z0and u*tincreased with increasing estimated VCd(STI). Based on their relationships with STI, we predicted values of z0and u*tin the absence of dry vegetation that were in accordance with published data. The threshold wind speeds at 10 m height derived from u*tat both the stony and non-stony sites were reasonably close to the value that is commonly used in dust models. Thus, we verified that STI is a suitable index for estimating the amount of dry vegetation in the Gobi Desert and, potentially, in other arid and semi-arid regions. Although the relationships of z0and u*twith STI require further validation, it appears that the use of STI can improve future wind erosion risk assessments in arid and semi-arid environments.

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

Estimating the fractional coverage of the photosynthetic vegetation (fPV) and non-photosynthetic vegetation (fNPV) is essential for assessing the growth conditions of vegetation growth in arid areas and for monitoring environmental changes and desertification. The aim of this study was to estimate the fPV, fNPV and the fractional coverage of the bare soil (fBS) in the lower reaches of Tarim River quantitatively. The study acquired field data during September 2020 for obtaining the fPV, fNPV and fBS. Firstly, six photosynthetic vegetation indices (PVIs) and six non-photosynthetic vegetation indices (NPVIs) were calculated from Sentinel-2A image data. The PVIs include normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), reduced simple ratio index (RSR) and global environment monitoring index (GEMI). Meanwhile, normalized difference index (NDI), normalized difference tillage index (NDTI), normalized difference senescent vegetation index (NDSVI), soil tillage index (STI), shortwave infrared ratio (SWIR32) and dead fuel index (DFI) constitutes the NPVIs. We then established linear regression model of different PVIs and fPV, and NPVIs and fNPV, respectively. Finally, we applied the GEMI-DFI model to analyze the spatial and seasonal variation of fPV and fNPV in the study area in 2020. The results showed that the GEMI and fPV revealed the best correlation coefficient (R2) of 0.59, while DFI and fNPV had the best correlation of R2 = 0.45. The accuracy of fPV, fNPV and fBS based on the determined PVIs and NPVIs as calculated by GEMI-DFI model are 0.69, 0.58 and 0.43, respectively. The fPV and fNPV are consistent with the vegetation phonological development characteristics in the study area. The study concluded that the application of the GEMI-DFI model in the fPV and fNPV estimation was sufficiently significant for monitoring the spatial and seasonal variation of vegetation and its ecological functions in arid areas.

Applying Multi-Index approach from Sentinel-2 Imagery to Extract Urban Area in dry season (Semi-Arid Land in North East Algeria)

<p>The mapping of urban areas mostly presents a big difficulty, particularly, in arid and semi-arid environments. For that reason, in this research, we expect to increase built up accuracy mapping for Bordj Bou Arreridj city in semi-arid regions (North-East Algeria) by focusing on the identification of appropriate combination of the remotely sensed spectral indices. The study applies the ‘k–means’ classifier. In this regard, four spectral indexes were selected, namely normalized difference tillage index (NDTI) for built-up, and both bare soil index (BSI) and dry bare-soil index (DBSI), which are related to bare soil, as well as the normalized difference vegetation index (NDVI). All previous spectral indices mentioned were derived from Sentinel-2 data acquired during the dry season. Two combinations of them were generated using layer stack process, keeping both of NDTI and NDVI index constant in both combinations so that the multi-index NDTI/BSI/NDVI was the first single dataset combination, and the multi-index NDTI/DBSI/NDVI as the second component. The results show that BSI index works better with NDTI index compared to the use of DBSI index. Therefore, BSI index provides improvements: bare soil classes and built-up were better discriminated, where the overall accuracy increased by 5.67% and the kappa coefficient increased by 12.05%. The use of k-means as unsupervised classifier provides an automatic and a rapid urban area detection. Therefore, the multi-index dataset NDTI/ BSI / NDVI was suitable for mapping the cities in dry climate, and could provide a better urban management and future remote sensing applications in semi-arid areas particularly.</p>

Estimates of Conservation Tillage Practices Using Landsat Archive

The USDA Environmental Quality Incentives Program (EQIP) provides financial assistance to encourage producers to adopt conservation practices. Historically, one of the most common practices is conservation tillage, primarily the use of no-till planting. The objectives of this research were to determine crop residue using remote sensing, an indicator of tillage intensity, without using training data and examine its performance at the field level. The Landsat Thematic Mapper Series platforms can provide global temporal and spatial coverage beginning in the mid-1980s. In this study, we used the Normalized Difference Tillage Index (NDTI), which has proved to be robust and accurate in studies built upon training datasets. We completed 10 years of residue maps for the 150,000 km2 study area in South Dakota, North Dakota, and Minnesota and validated the results against field-level survey data. The overall accuracy was between 64% and 78% with additional improvement when survey points with suspect geolocation and satellite tillage estimates with fewer than four dates of Landsat images were excluded. This study demonstrates that, with Landsat Archive available at no cost, researchers can implement retrospective, untrained estimates of conservation tillage with sufficient accuracy for some applications.

Remote sensing retrieval of maize residue cover on soil heterogeneous background

Maize stalk mulching is a conservation tillage method that has been currently promoted in northeastern China Plain. Remote sensing estimation of regional crop residue cover (CRC) can quickly obtain the information of straw mulching in a large area, which plays an important role in monitoring and popularizing the work of straw mulching. In this study, the normalized difference til-lage index (NDTI), simple tillage index (STI), normalized difference residue index (NDRI), and normalized difference index 7 (NDI7) were extracted from Sentinel-2A image and used to establish a linear regression model for CRC and spectral indices in Lishu County of Jilin Province. The results showed that soils had strong spatial heterogeneity in the study area, which would lead to a significant impact on the spectral index regression model. Using soil texture classification (zoning) to establish regression model could improve the inversion accuracy. Soil spatial heterogeneity would increase the estimation error of the model. The four spectral indices had a strong correlation with CRC, among which the NDTI and STI models performed better. The zonal linear regression model based on NDTI and STI verified that R2 was 0.84 and RMSE was 13.3%, which was better than the non-zonal model (R2 was 0.75 and RMSE was 16.5%) and thus effectively improved the inversion accuracy.

Retrospective tillage differentiation using the Landsat‐5 TM archive with discriminant analysis

AbstractAccurate and site‐specific information on tillage practice is vital to understand the impacts of crop management on water quality, soil conservation, and soil carbon sequestration. Remote sensing is a cost‐effective technique for surveillance and rapid assessment of tillage practice over large areas. A new empirical approach for accurately predicting tillage class using discriminant analysis (DA) on historical multi‐temporal Landsat‐TM 5 imagery has been developed. Ground truth data were obtained from the USDA‐NRCS at 48 locations (20 conventional till [CT] and 28 conservation tillage or no‐till [NT]). Classification accuracies were obtained for the DA models using reflectance values of Landsat‐5 TM bands and Normalized Difference Tillage Index (NDTI) values. The performance of the DA models was compared with Logistic Regression (LR) models. On the basis of classification accuracy and kappa (κ) value, our results showed that the DA models performed better in tillage classification than the LR models. However, using NDTI values, both the DA and LR models performed similarly in tillage class discrimination. Model performance improved when a subset of locations rather than years was used. The results indicated broad‐scale mapping of tillage practices is feasible using historical Landsat‐5 TM imagery and DA‐based classification.

Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue

Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil moisture content. In some areas of the world, the management of soil surface residue (SSR) is crucial for increasing soil fertility, maintaining high soil carbon levels, and reducing the degradation of soil due to rain and wind erosion. Standard methods of measuring the residue cover are time and labor intensive, but remote sensing can support the monitoring of conservation tillage practices applied to large fields. We investigated the potential of per-pixel and object-based image analysis (OBIA) for detecting and estimating the coverage of SSRs after tillage and planting practices for agricultural research fields in Iran using tillage indices for Landsat-8 and novel indices for Sentinel-2A. For validation, SSR was measured in the field through line transects at the beginning of the agricultural season (prior to autumn crop planting). Per-pixel approaches for Landsat-8 satellite images using normalized difference tillage index (NDTI) and simple tillage index (STI) yielded coefficient of determination (R2) values of 0.727 and 0.722, respectively. We developed comparable novel indices for Sentinel-2A satellite data that yielded R2 values of 0.760 and 0.759 for NDTI and STI, respectively, which means that the Sentinel data better matched the ground truth data. We tested several OBIA methods and achieved very high overall accuracies of up to 0.948 for Sentinel-2A and 0.891 for Landsat-8 with a membership function method. The OBIA methods clearly outperformed per-pixel approaches in estimating SSR and bear the potential to substitute or complement ground-based techniques.

Assessment of Wheat Straw Cover and Yield Performance in a Rice-Wheat Cropping System by Using Landsat Satellite Data

Proper straw cover information is one of the most important inputs for agroecosystem and environmental modeling, but the availability of accurate information remains limited. However, several remote-sensing (RS)-based studies have provided a residue cover estimation and provided spatial distribution mapping of paddy rice areas in a constant field condition. Despite this, the performance of rice crops with straw applications has received little attention. Furthermore, there are no methods currently available to quantify the wheat straw cover (WSC) percentage and its effect on rice crops in the rice-wheat cropping region on a large scale and a continuous basis. The novel approach proposed in this study demonstrates that the Landsat satellite data and seven RS-based indices, e.g., (i) normalized difference vegetation index (NDVI), (ii) Normalized difference senescent vegetation index (NDSVI), (iii) Normalized difference index 5 (NDI5), (iv) Normalized difference index 7 (NDI7), (v) Simple tillage index (STI), (vi) Normalized difference tillage index (NDTI), and (vii) Shortwave red normalized difference index (SRNDI), can be used to estimate the WSC percentage and determine the performance of rice crops over the study area in Changshu county, China. The regression model shows that the NDTI index performed better in differentiating the WSC at sampling points with a coefficient of determination (R2 = 0.80) and root mean squared difference (RMSD = 8.46%) compared to that of other indices, whereas the overall accuracy for mapping WSC was observed to be 84.61% and the kappa coefficient was κ = 0.76. Moreover, the rice yield model was established by correlating between the peak NDVI values and rice grain yield collected from ground census data, with R2 = 0.85. The finding also revealed that the highest estimated yield (8439.67 kg/ha) was recorded with 68% WCS in the study region. This study confirmed that the NDVI and NDTI algorithms are very effective and robust indicators. Also, it can be strongly concluded that multispectral Landsat satellite imagery is capable of measuring the WSC percentage and successively determines the impact of different WSC percentages on rice crop yield within fields or across large regions through remote sensing (RS) and geographical information system (GIS) techniques for the long-term planning of agriculture sustainability in rice-wheat cropping systems.